Wiki - User contributions [en]

Satellite submission

2013-01-14T13:39:14Z

Martin: /* Title: Cis encoding of the master developmental regulatory programme */

== Satellite manuscript internal review page ==

Welcome to the FANTOM5 Satellite review page. As discussed at the Ume and Koyo meetings, all papers will be visible to consortium members. This is to allow everyone to know what is going on, promote collaboration, carry out due process regarding co-authorship and to avoid competition.

== Authorship ==

The author list will basically be selected by the first author and the corresponding author of each satellite paper on the basis of the scientific contribution to the manuscript. Remember to include an authors contribution statement for all authors named in your manuscript (of the form AB carried out the cell isolation, SB carried out the network predictions etc.).

In addition the FANTOM5 headquarter will name RIKEN OSC members who should be co-authors for their input on each manuscript and to the entire FANTOM5 project. For those of you who have participated in previous FANTOMs you will be familiar with this process, for those new to FANTOM please look at the author lists on the satellite paper collections for FANTOM2-4. FANTOM5 headquarter is currently discussing the policy for RIKEN OSC co-authorship on the FANTOM5 satellites, but basically satellites papers will be considered on a case by case basis, and will take into account datasets used, intellectual input and facilitating technologies/analyses for each paper.

At this stage please name any authors from the OSC that you think should definitely be included as co-authors, in addition for all satellite submissions include the following term '''RIKEN_OSC_members''' as an additional author.

== Instructions ==

Please make a copy of the template below and enter your manuscript details.

If you are not able to edit the wiki yourself please email the secretariat with the subject line "FANTOM5_satellite", but please understand that these will be processed when we can rather than immediately. You must fill in all of the details below and provide both a PDF that contains all figures, and word doc of the main text, for reviewers to mark up directly.

= Manuscripts =

== Title: Analysis of DNA methylation and transcription during granulopoiesis reveals timed methylation changes in low CpG areas and regulation of transcription factor expression and motif activity ==

'''ManuscriptID: '''Phase1_001 '''Status: '''Good draft '''Abstract: '''In development epigenetic mechanisms such as DNA methylation have been suggested to provide cellular memory to maintain pluripotency but also stabilize cell fate decisions and direct lineage restriction. In this study we set out to characterize changes in DNA methylation levels and gene expression during granulopoiesis using four distinct cell populations ranging from the oligopotent common myeloid progenitor stage to terminally differentiated neutrophils. We found a general decrease of DNA methylation during granulopoiesis. Methylation levels appear to change at specific differentiation stages and correlate with changes in transcription and motif activity of key hematopoietic transcription factors. Differentially methylated sites (DMSs) are preferentially located in areas distal to CpG islands and shores and are overrepresented in potentially regulatory enhancer elements. Overall this study depicts in detail the epigenetic and transcriptional changes that occur during granulopoiesis and supports the role of DNA methylation as a regulatory mechanism in cell differentiation. '''Authors: '''Michelle Rönnerblad, Tor Olofsson, Sören Lehmann, RIKEN_OSC_members, Karl Ekwall*, Erik Arnér* & Andreas Lennartsson* '''Authors contribution statement: '''MR did most of the practical experiments, the bioinfo analysis (except CAGE related) and most manuscript writing, TO isolated the cells from bone marrows, SL gave valuable input to the planning, analysis and critically reviewed the manuscript, KE planned and supervised the study and contributed to the manuscript writing , EA supervised the bioinformatic analysis and performed the ones related to CAGE and contributed to the manuscript writing, AL initiated, planned and supervised the study and contributed to the manuscript writing and did some experiments. '''Datasets used: '''Helicos CAGE on granulo precursor populations '''Target journal(s): '''Blood '''Internal submission date: '''April 7th 2012 '''Contact by email: '''[mailto:andreas.lennartsson@ki.se,Karl.Ekwall@ki.se,arner@gsc.riken.jp andreas lennartsson, Karl Ekwall, Erik Arner] '''Word document version of manuscript for editors: '''[[Image:Rönnerblad.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Rönnerblad Aprl07.pdf]]

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== Title: Cell-type specificity and co-expression of regulatory polymorphisms associated with human disease ==

'''ManuscriptID: '''Phase1_002 '''Status: '''Good draft '''Abstract: ''' Our ability to use genetic associations with disease to develop better treatments has been limited by the difficulty of identifying a biological process, or cell type, on which to focus investigation. Most disease-associated polymorphisms do not lie within protein-coding genes, raising the possibility that variation in regulatory sequence plays a critical role in disease phenotypes. We have used genome-scale 5’RACE (CAGE) to identify the location and usage of transcription start sites in 864 human tissues, primary cells and cell lines, and show here that there is a strong enrichment for disease-associated variants within the sequence immediately adjacent to transcription start sites. Using the expression profiles of known variants associated with disease susceptibility, we identify experimentally-available cell types significantly associated with specific diseases and traits. The expression of genes known to be associated with particular diseases was positively correlated. Such co-expression was used to identify unreported candidate disease-associated regulatory regions within published genome-wide association studies (GWAS). The approach was validated by identifying candidate loci in a 2007 GWAS study that were subsequently validated in larger independent datasets These functional genomics approaches directly inform choices of model system and identify disease- and cell type-specific co-regulated networks for a wide range of common diseases.

 '''Authors: '''Baillie JK*, Haley CS, Schaefer U, Faulkner GJ, Freeman T, Brown JB, [others...], [Numerous RIKEN authors, order etc. TBC, at least including: Kawaji H, Forrest A, Carninci P]*, Hume DA* '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on Primary Cells '''Target journal(s): '''Nature Genetics '''Internal submission date: ''' ... '''Contact by email: '''[mailto:j.k.baillie@ed.ac.uk,david.hume@roslin.ed.ac.uk Kenneth Baillie, David Hume] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: What classes of mammalian promoter are there? ==

'''ManuscriptID: '''Phase1_003 '''Status: '''Working draft '''Abstract: '''This study uses the comprehensive FANTOM5 promoter data, and careful methodology, to identify classes of mammalian promoter. In agreement with previous results, we find that promoters fall into two classes with narrow or wide spread of transcription start sites. In stark contrast to previous studies, we find little association between width and either CpG rate or TATA signals. Width correlates with expression level, suggesting that strength of promoter signal is on average proportional to promoter length. The data are consistent with a simple null hypothesis for CpG islands: that they are a passive consequence of expression (and thus cytosine demethylation and reduced CpG mutation) in germ-line cells. Finally, we show that measures of tissue specificity are prone to statistical artifacts, and specificity is not correlated with promoter narrowness, in contrast to previous claims. These results clarify some fundamental properties of mammalian promoters. '''Todo: '''Use Charles's good way of measuring tissue specificity. '''Authors: '''Frith, maybe Drabløs et al., open to others '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''All human Phase1 CTSSs (plan to add mouse) '''Target journal(s): ''' '''Internal submission date: '''August 2012? '''Contact by email: '''[mailto:martin@cbrc.jp Martin Frith] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Epigenetic factors regulating Hematopoiesis ==

'''ManuscriptID: '''Phase1_004 '''Status: '''Good draft '''Abstract: '''The hematopoietic differentiation pathway is a complex regulatory program for generating different lineages of blood cell types from multipotent, hematopoietic stem cells. The transcriptional program dictating hematopoietic cell fate and differentiation requires an epigenetic memory function consisting of a network of enzymes controlling DNA methylation, histone posttranslational modifications and chromatin structure. Defective interactions between epigenetic enzymes and transcription factors cause perturbations in blood cell differentiation, which often leads to various types of hematopoietic disorders such as leukemia. To elucidate the contribution of different epigenetic factors in human hematopoieis, high-throughput Cap Analysis of Gene Expression (CAGE) sequencing was used to build comprehensive transcription profiles of 199 epigenetic factors in a wide range of blood cells. These epigenetic factors include proteins that covalently modify DNA/histones or alter chromatin structure dynamics. Our analysis revealed several epigenetic factors to have expression profiles specific for cell type, lineage type and/or leukemic cell lines. In this report the ‘epigenetic transcriptome’ has been systematically studied to predict their potential functions in the epigenetic regulatory network of human hematopoiesis. The potential of such a comprehensive study is not only to identify putative epigenetic regulators of normal hematopoiesis and postulate their function but also to serve as a resource for the scientific community for further characterization and validation of differentially expressed transcripts. '''Authors: '''Punit Prasad, Michelle Rönnerblad,...FANTOM5, Erik Arner, Karl Ekwall and Andreas Lennartsson '''Authors contribution statement: '''PP and MR have done analysis and written the manuscript. EA has performed the initial CAGE analysis for the epigenetic factors and assisted in writing the manuscript. AL and KE have assisted in writing the manuscript, planned and coordinated the study. The authors declare no conflict of interest. '''Datasets used: '''Helicos CAGE on ... '''Target journal(s): '''Blood or other '''Internal submission date: '''December 06, 2012 '''Contact by email: '''[mailto:andreas.lennartsson@ki.se,arner@gsc.riken.jp Andreas Lennartsson, Erik Arner] '''Word document version of manuscript for editors: '''[[Image:Prasad et al amnuscriot Blood.docx]], [[Image:Prasad et al Blood Figs.pdf]], [[Image:Prasad et al Table S1 .xlsx]], [[Image:Prasad et al Table S2.xlsx]], [[Image:Prasad Table S3.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Phase1 004.pdf]]

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== Title: Ab Initio Prediction of Tissue-Specific Regulatory Modules in the FANTOM5 Project ==

'''ManuscriptID: '''Phase1_005 '''Status: '''Final draft '''Abstract: '''One of the major goals of the FANTOM5 project, the broadest TSS-based promoter-level expression atlas of transcriptional regulatory networks, is the identification of coding and non-coding, annotated and novel transcriptional units being transcribed in a cell-specific mode across the different biological states/samples. In this work we analyzed the FANTOM5 dataset using ScanAll, a newly developed software here described, to ab initio predict the presence of conserved elements in the genomic regions surrounding FANTOM5 promoters. Firstly we aimed at identifying motifs that were conserved in a subset of the selected genomic regions and that possibly corresponded to Transcription Factor Binding Sites (TFBS); we then expanded our analysis to pinpoint the existence of more complex, structured regulatory modules, that is groups of conserved motifs co-occurring in the aforementioned (co-expressed) regions within a fixed distance. We confirmed the sample-specificity of our output by showing that the majority of the obtained combinations of modules were able to divide the specimens into sample-specific groups, thus possibly explaining the peculiarities of regulatory events occurring in each tissue. Among these sites it was possible to confirm the presence of TFBS for known regulators already associated to those samples together with an additional and significant portion of motifs remaining unannotated, thus representing putative novel binding elements. In addition we were able to associate the presence of a significant portion of the identified motifs to distinct families of repeated elements, thus confirming a structural/functional feature of mammalian promoters that is currently emerging as one of the most peculiar regulatory aspects associated to mammalian phylogeny. Finally, we were able to identify previously uncharacterized aspects of the regulatory networks occurring in early-development samples thus confirming the significant advantage deriving from our modular approach. '''Authors: '''Emiliano Dalla, Yari Ciani, Marco Zantoni, RIKEN_OSC_members, Alberto Policriti, Claudio Schneider, Silvano Piazza '''Authors contribution statement: '''ED conceived the project, developed part of the software, oversaw implementation, performed some of the analysis and most manuscript writing; YC implemented part of the software and prepared some figures; MZ developed and implemented part of the software; AP developed part of the software and contributed to the manuscript writing; CS supervised the study; SP developed and implemented part of the software, carried out statistical tests and results interpretation and wrote parts of the manuscript. '''Datasets used: '''Helicos CAGE on all of F5freeze1 '''Target journal(s): ''' '''Internal submission date: '''June 1st 2012; Update: December 21st 2012 '''Contact by email: '''[mailto:emiliano.dalla@lncib.it Emiliano Dalla] '''Word document version of manuscript for editors: '''[[Image:FANTOM5 PromoteromeSatelliteLNCIB.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:FANTOM5 PromoteromeSatelliteLNCIB wFigures.pdf]]

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== Title: Homotypic clusters of transcription factor binding sites in the vicinity of transcription start sites ==

'''ManuscriptID: '''Phase1_006 '''Status: '''Finished draft 

'''Abstract: ''' ''Background'' Transcription factors (TFs) specifically recognizing DNA binding sites (TFBS) play a key role in regulation of gene expression. Groups of closely localized TFBSs for a particular TF, so-called homotypic TFBS clusters (HCBSs), were originally detected in yeast and extensively studied in fruit fly early development. Recently HCs were found to be highly important for several human regulatory systems.

''Motivation'' It is a general practice to estimate an enrichment of binding sites in regulatory sequences. Still there is no systematized data whether the presence of HCBSs is common for promoter regions of human genes. The general properties of HCBSs also remain unclear as well as possible relation between HCBSs and regulation of tissue-specific expression.

''Results'' Using data on sample-specific transcription start sites (TSSs) detected in FANTOM5 and high quality binding models for more than 400 TFs from the HOCOMOCO TFBS model collection we have predicted TFBSs and corresponding HCBSs in promoter regions surrounding TSSs. TFBS models for most TFs were shown to form statistically significant HCBSs often formed by separate distant binding sites. For HCBSs of most of TFs we were able to identify samples having significant association between promoters of sample-specific or housekeeping TSSs. Thus for most of TFs we predict putative preferences for sample-specific or housekeeping HCBSs activity and provide a genome-wide map of HCBSs nearby FANTOM5-defined TSSs.

''Supplementary information'' https://fantom5-collaboration.gsc.riken.jp/webdav/home/vigg/homotypicus/

'''Authors: '''I.V. Kulakovskiy, Y.A. Medvedeva, M.S. Polishchuk, A.V. Favorov, S. Schmeier, T. Lassman, I.E. Vorontsov, RIKEN_OSC_members, V.J. Makeev 

'''Authors contribution statement: ''' IVK implemented the software and drafted the manuscript. YAM carried out statistical tests and results interpretation. MSP developed the homotypic cluster detection algorithm. AVF selected proper statistical tests. SS provided the housekeeping set of TSS-clusters. TL provided the set of sample-specific TSS-clusters. IEV estimated proper thresholds for PWMs used in the study. VJM coordinated the study. All the authors participated in writing and finalizing the manuscript. 

'''Datasets used: '''Helicos CAGE - FANTOM5 FREEZE1, "robust" subset 

'''Target journal(s): ''' Nucleic Acids Research, Bioinformatics 

'''Internal submission date: '''18 June 2012 / Updated: 12 September 2012 / Minor fixes: 1 December 2012 

'''Contact by email: '''[mailto:vsevolod.makeev@gmail.com,ivan.kulakovskiy@gmail.com Vsevolod Makeev, Ivan Kulakovskiy] 

'''Word document version of manuscript for editors: '''[[Image:HOMOTYPICUS-FANTOMsatellitepaper.r1.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:HOMOTYPICUS-FANTOMsatellitepaper.r1.pdf]]

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== Title: A high resolution spatial-temporal promoterome of the human brain (was Brain CAGE) ==

'''ManuscriptID: '''Phase1_007 '''Status: '''Good draft '''Abstract: ''' The human brain is an extremely complex organ that governs our abilities for cognition, reasoning and emotions and is the control center for the body. Its morphology and functionality during development have been well studied, but the molecular mechanisms contributing to its function and maintenance later in life remain poorly understood. Complexity at the transcriptional level is likely to play a major role in defining its morphological and functional characteristics. To investigate this we used single molecule CAGE and created a high resolution atlas of transcription start sites for 15 anatomical regions of the human central nervous system, using post-mortem samples derived from infant and aged adult donors. On the transcriptional level brain is clearly distinguishable from other tissues even if we consider only non-coding genes or expression from genomic regions often described as genomic dark matter. Using these differences we identify a specific set of transcription start sites that characterizes the brain. We show extensive differences in transcription between infant and adult that in some cases can be linked to loci associated with major neurodegenerative diseases. The differential expression across distinct regions correlates well with developmentally and/or functionally related anatomical districts and is refelected by distinct networks of interacting transcription factors, a range of lncRNAs and novel transcripts co-expressed in a regionally biased manner. Overall we provide the scientific community with a powerful expression resource based on post-mortem tissue, particularly highlighting the contribution of non-coding RNAs to the transcriptional complexity of human central nervous system.

 '''Authors: '''Margherita Francescatto, Morana Vitezic, Patrizia Rizzu, Javier Simon-Sanchez, Robin Andersson, FANTOM5_RIKEN_OSC_members, Carsten O Daub, Albin Sandelin, MIchiel JL de Hoon, Piero Carninci, Alistair RR Forrest, Peter Heutink 

'''Authors contribution statement: '''MF and MV did the analyses; MF, MV and PH wrote the manuscript, PR selected all samples, evaluated medical and pathological records and isolated RNA, JSS curated the list of disease loci, RA and AS provided the list of enhancers, ARRF, PC and PH designed the study ... 

'''Datasets used: '''Helicos CAGE on VUMC provided brain samples (adult and newborn); full list of samples presented in Supplementary Table 1 

'''Target journal(s): ''' Genome Research 

'''Internal submission date: ''' 

'''Contact by email: '''[mailto:Peter.Heutink@dzne.de,m.francescatto@vumc.nl,mvitezic@gsc.riken.jp Peter Heutink, Margherita Francescatto, Morana Vitezic] 

'''Word document version of manuscript for editors: '''[[Image:BrainCAGE manuscript presubmission enquiery.doc]] [[Image:BrainCAGE figures presubmission enquiery.pdf]] 

'''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Pathogen specific monocyte transcriptional responses ==

'''ManuscriptID: '''Phase1_008 '''Status: '''Working draft '''Abstract: ''' '''Authors: '''Wells '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on ... '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:c.wells@uq.edu.au,a.beckhouse@uq.edu.au Christine Wells, Anthony Beckhouse] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Transcriptome profiling of human skin mast cells by deep CAGE identifies unexpected gene activity patterns through direct comparison with multiple cell and tissue subsets ==

'''ManuscriptID: '''Phase1_009 '''Status: '''Working draft '''Abstract: '''Despite their haematopoietic origin, mast cells (MCs) mature exclusively in peripheral tissues, hampering research into their developmental and functional programs. Here, we employed deep-CAGE on skin-derived MCs to generate the most comprehensive view of the human MC transcriptome ever reported. A particular advantage is that MCs were embedded in the FANTOM5 project, giving the opportunity to contrast their molecular signature against an extensive panel of human samples. We demonstrate that MCs possess a unique and surprising transcriptional landscape, combining expression of typical haematopoietic genes with those exclusively active in MCs, and genes not previously reported as expressed in MCs. Specifically we found that MCs express functional BMP receptors, which transduce pro-survival and activatory signals. Conversely, several genes frequently studied in MCs were either not or only weakly expressed in direct comparison with other myelocytes. By the parallel use of MCs ex vivo and following culture, we also found that MCs change their transcriptome in in vitro surroundings. Befitting their uniqueness, MCs had no close relative in the haematopoietic network. This rich dataset reveals that our knowledge of human MCs is still fairly limited. It can be anticipated that with this resource novel functional programs of MCs will soon be discovered. 
'''Authors: '''Efthymios Motakis,1,* Sven Guhl,2,* Yuri Ishizu,1 RIKEN OSC members,1 Torsten Zuberbier,2 Alistair R R Forrest,1¶ Magda Babina2¶ 
'''Authors contribution statement: '''E.M. carried out bioifnormatics analayses S.G. isolated the mast cells and performed most experiments, M.B. performed several experiments, was involved in planning, supervision, and data analysis, and wrote the first draft of the manuscript, E.M. S.G., A.R.R.F. and T.Z. helped with planning, data analysis and manuscript writing. '''Datasets used: '''Helicos CAGE on mast cell samples in comparison to freeze 1 data '''Target journal(s): '''Blood, eBlood '''Internal submission date: ''' '''Contact by email: '''[mailto:magda.babina@charite.de,sven.guhl@charite.de Magda Babina, Sven Guhl] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:MC_satellite_Jan_6_merged.pdf]]

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== Title: Effect of cytosine methylation on transcription factor binding sites and regulation of transcription ==

'''ManuscriptID: '''Phase1_010 '''Status: '''Good draft '''Abstract: ''' Motivation: DNA methylation of gene promoters is strongly linked to gene repression. However, the mechanism of interaction between DNA methylation and gene repression is not fully understood. We cannot strictly state that DNA methylation of gene promoters is a cause of gene repression or, vise versa, that gene repression induced either by chromatin modification or by binding of Polycomb proteins leads to subsequent DNA methylation. Potential mechanism for transcriptional regulation by DNA methylation can be driven by methylation-induced changes in either accessibility of transcription factors (TFs) binding sites (TFBSs) or affinity of TFs to their TFBSs. This idea is supported by non-systematic evidences. Until now, this hypothesis has not been tested systematically for a wide spectrum of TFs with known TFBS models and across large number of cell types. Methods: To estimate DNA methylation in 50 different cell types we used data obtained by reduced representation bisulfite sequencing (RRBS) provided by the ENCODE project. To evaluate genome-wide expression in the corresponding cell types we utilized FANTOM5 data obtained by cap-analysis of gene expression (CAGE). To predict TFBSs we used remote dependency model (RDM), a generalization of a position weight matrix (PWM), which takes into consideration the correlation of remote nucleotides within a binding site and has been shown to effectively decrease false positive rate compared to the widely used PWM approach. Results and conclusions: In this work we surprisingly show that only 5% of CpG dinucleotides correspond to “traffic lights” genome positions, i.e. they manifest moderate to high negative correlation of their methylation profile and an expression profile of a neighboring TSS across cell samples. Significant share of TFBSs tend to avoid CpG “traffic lights”. This tendency is less pronounced if a binding site is surrounded by a homotypic cluster of TFBSs, suggesting that a loss of function for one TFBS due to methylation can be compensated by closely located weaker TFBSs for the same TF. In a way, this puts into a different perspective the current common perception of the link of methylation and gene expression. '''Authors: '''Medvedeva YA, Khamis A, Ba-Alawi W, Bhuyan MdSI, [potential F5 collaborators], Kulakovskiy IV, Bajic VB '''Authors contribution statement: '''YAM designed the computational experiments, selected and preprocessed the data, produced statistical analysis and wrote the manuscript; AK performed most of the data analysis; WBA and MdSIB contributed RDM models and tools for threshold estimation and mapping; [potential F5 collaborators], IVK performed part of the analysis, contributed to the design of the experiments and writing of the manuscript; VBB contributed to the design of the experiments and writing of the manuscript. '''Datasets used: '''Helicos CAGE on 50 sample types, ENCODE RRBS data for the same samples '''Target journal(s): ''' '''Internal submission date: '''December, 16 '''Contact by email: '''[mailto:ju.medvedeva@gmail.com Yulia Medvedeva] '''Word document version of manuscript for editors: '''[[Image:Effect of cytosine methylation on transcription factor binding sites and regulation of transcription.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Effect of cytosine methylation on transcription factor binding sites and regulation of transcription.pdf]]

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== Title: Transcription and enhancer profiling in human monocyte subsets ==

'''ManuscriptID: '''Phase1_011 '''Status: '''Good draft '''Abstract: ''' Human blood monocytes comprise at least three subpopulations that differ in phenotype and function. Here we present the first in-depth regulome analysis of classical (CD14++CD16-), intermediate (CD14+CD16+), and nonclassical (CD14dimCD16+) monocytes. Cap Analysis of Gene Expression (CAGE) adapted to Helicos single molecule sequencing was used to map transcription start sites throughout the genome in all three subsets. In addition, global maps of H3K4me1 and H3K27ac deposition were generated for classical and nonclassical monocytes defining enhanceosomes of the two major subsets. We identify differential regulatory elements (including promoters and putative enhancers) that were associated with subset-specific motif signatures corresponding to different transcription factor activities and exemplarily validate a novel downstream enhancer of the CD14 locus. In addition to known subset specific features, pathway analysis revealed marked differences in metabolic gene signatures. While classical monocytes expressed higher levels of genes involved in carbohydrate metabolism priming them for anaerobic energy production, nonclassical monocytes expressed higher levels of oxidative pathway components and showed a higher routine mitochondrial activity. Our findings describe promoter/enhancer landscapes and provide novel insights into the specific biology of human monocyte subsets. '''Authors: '''Christian Schmidl, Kathrin Renner, Ruediger Eder, Katrin Peter, Petra Hoffmann, Reinhard Andreesen, Marina P. Kreutz, RIKEN_OSC_members, Matthias Edinger, Michael Rehli '''Authors contribution statement: '''CS performed experiments, computational analyses and wrote parts of the manuscript writing, KR performed experiments and contributed to manuscript writing, RE isolated the cells, KP performed experiments, PH, RA, MK, and ME contributed to planning and supervision, RIKEN_OSC_members who organized or performed Helicos sequencing and provided aligned data; MR initiated, planned and supervised the study, performed computational analyses, and wrote the manuscript. '''Datasets used: '''Helicos CAGE on monocyte subsets (Regensburg samples) '''Target journal(s): ''' Blood, eBlood, other '''Internal submission date: September 1 ,2012 ''' '''Contact by email: '''[mailto:michael.rehli@ukr.de,Christian.Schmidl@klinik.uni-regensburg.de Michael Rehli, Christian Schmidl] '''Word document version of manuscript for editors: '''[[Image:Schmidl MonoSub.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Schmidl MonoSub.pdf]]  

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== Title: ... ==

'''ManuscriptID: '''Phase1_012 '''Status: '''Gone '''Abstract: ''' '''Authors: '''... '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on ... '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:blobby@blob.town Mr Blobby] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: The Evolution of Human Cells in terms of Protein Innovation ==

'''ManuscriptID: '''Phase1_013 '''Status: '''Working draft '''Abstract: ''' Humans are complex organisms composed of a great many cell types. Since the genomic DNA of each cell is identical, cell type is determined by what is expressed. We examine the evolutionary history of each human cell type at the molecular level via the collective histories of proteins, the principal product of gene expression. Sequence data from the FANTOM5 consortium are used to provide cell-type specific digital expression of protein-coding genes, and the SUPERFAMILY and dcGO resources provide domain and function annotation respectively. Cross-referencing with the domain annotation of all other completely-sequenced genomes provides the evolutionary context for each protein. We combine all of this to generate a description of cellular evolution at the molecular level.

We present a protein domain view of the evolution of cell type. To achieve this we first identify the most recent common ancestor (MRCA) or ‘creation epoch’ of every protein in the repertoire of the human genome. We are then able to use the protein creation epochs to describe the history of the emergence of each cell type over evolution in terms of the collective histories of the proteins expressed in that cell type. Each cell type has an evolutionary profile consisting of a timeline along the lineage from the ancient cellular ancestor to modern day human. The profile of each cell type shows at which epochs along the timeline innovations in protein evolution took place; required to allow the observed expression in that type of cell. By clustering cell types on these profiles, we find groups of cell types that share a parallel protein evolutionary history and thus potentially possess a common progenitor cell type or are evolving in cooperation. A functional enrichment analysis of these clusters reveals key proteins responsible for evolutionary shifts and functional innovations; it also suggests a possible order in which different cells could have emerged during evolution, which we discuss in relation to the human immune system. The structural domain-centric perspective which we employ in this work can also be used as the basis for a comparison of the molecular basis of functional and phenotypic differences between cell types within these evolutionary clusters, exemplified by an inspection of our results on different regions of the brain.

We present a view of the landscape of nature’s innovation of protein structure and architecture required to explain the creation of the different human cell types. This landscape has some important features such as the possibility that the last universal ancestor of life provided most of the innovation for the innate immune system whilst brain cells have been making use of novel proteins that first appeared in opisthokonta (animals and fungi) and continued to do so right up until homo sapiens. The landscape also lends itself to identifying candidate genes for disease by highlighting those that were important in enabling certain phenotypic shifts at key points in evolution. '''Authors: '''Julian Gough, Owen Rackham, Adam Sardar, Matt Oates + Sample Providers + RIKEN OSC '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on all samples '''Target journal(s):''' Bioinformatics? '''Internal submission date: ''' '''Contact by email: '''[mailto:gough@compsci.bristol.ac.uk,owen.rackham@gmail.com Julian Gough, Owen Rackham] '''Word document version of manuscript for editors: '''[[Rough draft available on request]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:TrapDraftv2.pdf]]

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== Title: Transcriptional profiling by deep CAGE of the human fibrillin/LTBP gene family, key regulators of mesenchymal cell functions.==

'''Manuscrjavascript:void(0)iptID''': Phase1_014 '''Status: '''Working draft '''Abstract:''' The fibrillins and latent transforming growth factor binding proteins (LTBPs) form a superfamily of extracellular matrix (ECM) proteins characterized by the presence of a unique domain, the 8-cysteine transforming growth factor beta (TGFβ) binding domain (TB domain). These proteins are involved in both maintaining the extracellular matrix and controlling the bioavailability of TGFβ family members. Genes encoding these proteins show differential expression in mesenchymal cell types which synthesise the extracellular matrix and give rise to connective tissues. We have investigated the promoter regions of the seven gene family members using the FANTOM5 CAGE data base for human. Although the protein and nucleotide sequences showed considerable homology (for the protein sequence of fibrillins the maximum sequence homology was 68% between fibrillin1 and fibrillin2; minimum sequence homology was 59% between fibrillin1 and fibrillin3), the promoter regions were quite diverse. The three fibrillin genes had a single predominant promoter cluster, while LTBP1 and LTBP4 showed promoter switching. The depth of the current CAGE study revealed that most of the family members were expressed in a range of mesenchymal and other cell types, often associated with use of alternative promoters or changes in the transcription start site within a compound promoter. FBN3 was the lowest expressed gene, and was expressed only in embryonic and fetal tissues, primarily neurological. There was evidence of enhancer activity in the regions of the genes. Each gene showed a unique pattern of transcription factor motifs or activity. This study highlights the role of alternative transcription start sites in regulating the tissue specificity of closely related genes and suggests that this important class of extracellular matrix genes is subject to subtle regulatory variations that explain the differential roles of members of this gene family.. '''Authors:''' Margaret R Davis, RIKEN OSC members, Kim M Summers '''Authors contribution statement:''' MRD performed most of the analysis and contributed to writing the paper, RIKEN OSC did ..., KMS performed the analysis and contributed to writing the paper '''Datasets used:''' Helicos CAGE on ... '''Target journal(s):''' '''Internal submission date: Contact by email: '''[mailto:kim.summers@roslin.ed.ac.uk kim.summers@roslin.ed.ac.uk] '''Word document version of manuscript for editors:''' File:XXXYOUR.doc '''PDF version for general viewing (including all figs in one PDF):''' File:XXXYOUR.pdf

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== Title: Quantifying the informational complexity of transcriptional regulatory programmes ==

'''ManuscriptID: '''Phase1_015 '''Status: '''On-hold. Focussing on the biological results Phase1_016 rather than methods. Hope to return to methods later (phase2). '''Abstract: ''' The regulation of gene expression defines cellular identity, it is the basis for organism development and it underlies many cellular responses to the environment. Its disruption is implicated in many diseases and changes in gene regulation appear to underlie many adaptations evident between species. Previously, genes have been grouped and interpreted based on their specificity of expression, for example house-keeping genes that are expressed by all cells in all conditions versus highly tissue restricted genes expressed by only one cell type at a particular developmental time. Although such studies have been informative they fail to capture important aspects of how a gene is regulated or account for the heterogeneous relatedness of samples. The expression pattern of a gene is the output of a regulatory program within the cell. A program that must affect many state changes (on, off, up, down) is likely to require more regulatory information (Kolmogorov complexity) than a program effecting fewer state switches. If we can quantify this "regulatory complexity" we can then start to address deeper questions as to where that regulatory information is encoded, how malleable it is through evolution and how susceptible it is to perturbation by mutation. For example, a greater regulatory complexity could correspond to a higher concentration of cis-regulatory sequences around the gene or alternatively a single binding site for a transcription factor that is the output of an extensive intracellular signalling network. To address these questions we have explored a range of possible measures regulatory complexity including distance weighted entropies, diversity and richness scores. This leads us to introduce a novel measure of regulatory complexity (CR). It is implemented as a hierarchical Baysian model parametrised through MCMC. The CR method can be thought of as a relative measure of the number of gene expression state changes occurring over a tree relating all analysed samples. A by-product of this analysis is a probabilistic scoring of gene expression state switches between all analysed gene expression libaries. CR is weighted to account for the genome wide similarity of gene expression between samples but does not depend on the inference of a fixed underlying tree topology. Note - this is intended as essentially a methods paper, see Phase1_016 for the biological insights paper '''Authors:''' Sarah Baker, Martin Taylor '''Authors contribution statement: '''SB developed and implemented methods and performed general analyses; MT conceived the project and oversaw implementation and performed some of the analysis '''Datasets used: '''Helicos CAGE on primary cells from human and mouse. '''Target journal(s): ''' Bioinformatics or Genome Research '''Internal submission date:''' ETA July 2013 '''Contact by email: '''[mailto:martin.tayor@igmm.ed.ac.uk,sarah.baker@igmm.ed.ac.uk Martin Taylor, Sarah Baker] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Cis encoding of the master developmental regulatory programme ==

'''ManuscriptID: '''Phase1_016 '''Status: '''Working draft, starting dataset being regenerated to incorporate improved method '''Abstract: ''' The regulation of gene expression defines cellular identity, it is the basis for organism development and it underlies many cellular responses to the environment. Its disruption is implicated in many diseases and changes in gene regulation appear to underlie many adaptations evident between species. '''Authors:''' Sarah Baker, Martin Taylor '''Authors contribution statement: '''SB developed and implemented methods and performed general analyses; MT conceived the project and oversaw implementation and performed some of the analysis '''Datasets used: '''Helicos CAGE on primary cells from human and mouse. We may also want to use time course data for this paper (does that push it into phase2?). '''Target journal(s): '''PLoS Biology '''Internal submission date:''' ETA March 2013 '''Contact by email: '''[mailto:martin.tayor@igmm.ed.ac.uk,sarah.baker@igmm.ed.ac.uk Martin Taylor, Sarah Baker] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Correspondence between CAGE clusters and chromatin marks ==

'''ManuscriptID: '''Phase1_017 '''Status: '''Preliminary Draft (Moved to Phase 2) '''Abstract: '''The paper presents an analysis of the correlation between cell-type-specific CAGE clusters and chromatin marks, using FANTOM CAGE data and ENCODE ChIP-Seq data for the four ENCODE cell lines K562, Gm12878, Helas3 and Hepg2. It shows that active chromatin marks are present at both expressed and repressed clusters. Chromatin profiles around expressed CAGE clusters have various shapes, and can be grouped into combinatorial subclusters based on their profiles. Repressed clusters with active chromatin mark represents a set of poised CAGE clusters enriched for Pol II and linked to immune response. The latter clusters also have a well-positioned nucleosome at the TSS. The manuscript is only preliminary, and some of the analysis still remains to be performed. The general content of the paper is considerably different than what is described in the report posted here previosly. [[Media:CAGE_cluster_evaluation_Drablos_Rye_13012012.pdf]] and April [[Media:CAGE_clusters_and_chromatin_Drablos_Rye_26042012.pdf]].) '''Authors: '''Morten Rye, Finn Drablos '''Authors contribution statement: '''MR and FD did data analysis and wrote the paper '''Datasets used: '''Helicos CAGE data, ENCODE chromatin ChIP-Seq and DNase HS data '''Target journal(s): ''' '''Internal submission date: '''Most likely February 2013 '''Contact by email: '''[mailto:finn.drablos@ntnu.no,morten.rye@ntnu.no Finn Drablos,Morten Rye] '''Word document version of manuscript for editors: '''[[Image:Preliminary Draft Drablos Rye 11-12-2012.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:All draft figs Drablos Rye 11-12-2012.pdf]] '''Supplementary figures: '''[[Image:All supplem figs Drablos Rye 11-12-2012.pdf]]

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== Title: Promoter specificity in transcription determines cell lineage choice ==

'''ManuscriptID: '''Phase1_018 '''Status: '''Delayed (as of September 12th) '''Abstract: '''This paper will use pathprint (pathway fingerprinting) to develop an overall phylogenetic tree of all samples in F5 freeze1. This tree will be used to determine relative ancestry of samples and cluster them accordingly. SwitchEngine will be run to find switching in TSS at key junctions in differentiation. Will show TSS dynamics at these informative sites is associated with lineage-commitment. '''Authors: '''Emmanuel Dimont, Gabriel Altschuler, Winston Hide '''Authors contribution statement: '''ED did ..., GA did ..., WH did ... '''Datasets used: '''Helicos CAGE on all of F5freeze1 '''Target journal(s): ''' '''Internal submission date: '''Most likely July-August 2012 '''Contact by email: '''[mailto:edimont@hsph.harvard.edu,gabrielaltschuler@googlemail.com,whide@hsph.harvard.edu Winston Hide, Emmanuel Dimont, Gabriel Altschuler] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Patterns of expression space change in the F5-CAGE encyclopedia of vertebrate gene expression. ==

'''ManuscriptID: '''Phase1_019 '''Status: '''Finished draft '''Abstract: ''' F5-CAGE encyclopedia of expression patterns is arguably the most comprehensive and technologically uniform functional genomics dataset ever generated. F5-CAGE includes 952 human and 396 mouse tissues (T), primary cells (PC) and cancer cell-lines (CCL). Here, we use F5-CAGE to explore expression space change in multiple contexts. Brain exhibits unique transcriptional features, including clustering into fetal, newborn, and adult samples. All samples group into three distinct categories with respect to expression evolution rate. There is trend for young genes to be tissue-speciﬁc, with the exception of taxon Eutheria. A major divide between leukemias and solid tumors is seen in CCL. Paralog expression pattern divergence suggests global devolution of expression in CCL. We explore global differences between T and CCL samples further, though family analysis and self-organizing maps. As a focused family evolution example, we use cdc42 family which features many tissue-speciﬁc genes and dramatic expression pattern shifts, correlated with ENCODE Tfbs. PhyloSigs suggest novel hypotheses for animal evolution: CNS and reproductive track are discussed as two examples. Most genes have multiple TSSes, with up to 87 for tintin, contributing to multiple isoforms which were previously attributed to alternative splicing alone. TSSes correlate between human and mouse, older genes tend to have more TSSes, and TSS-rich genes are associated with cancer. Finally, we test the hypothesis of CTCF acting as isolator between paralogs, and instead show its function is more likely in bringing duplicates under the control of the same enhancer. The trend is illustrated with semenogelins and pregnancy speciﬁc glycoproteins '''Authors: '''Lukasz Huminiecki, Oxana Sachenkova and Core RIKEN Authors 

'''Authors contribution statement: ''' LH: gathered and prepared the data, planned the study and analyzed the data, wrote the manuscript OS: wrote the software to analyze the data, performed the analysis, prepared the figures 

'''Datasets used: '''Helicos CAGE, TreeFam8, ENCODE TFBS ChIP-Seq '''Target journal(s): Genome Research''' '''Internal submission date: November 30th''' '''Contact by email: '''[mailto:Lukasz.Huminiecki@ki.se Lukasz Huminiecki] ,[mailto:oxana.sachenkova@scilifelab.se Oxana Sachenkova] '''Word document version of manuscript for editors : '''[[Image:The structure of animal expression pattern evolution.doc]] (only text) '''PDF version for general viewing (including all figs in one PDF): '''[[Image:The structure of animal expression pattern evolution.pdf]] (this file includes all the figures)

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== Title: Gene duplication and promoter divergence in mammals. ==

'''ManuscriptID: '''Phase1_020 '''Status: '''Delayed '''Abstract: ''' '''Authors: '''Lukasz Huminiecki and Core RIKEN Authors '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... and LH did everything else '''Datasets used: '''Helicos CAGE on ..., F5 promoter and enhancer datasets, TreeFam8 '''Target journal(s): Genome Research''' '''Internal submission date: September 1st''' '''Contact by email: '''[mailto:Lukasz.Huminiecki@ki.se Lukasz Huminiecki] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Gene duplication and TF/miRNA regulatory network evolution in mammals. ==

'''ManuscriptID: '''Phase1_021 '''Status: '''Delayed '''Abstract: ''' '''Authors: '''Lukasz Huminiecki and Core RIKEN Authors '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... and LH did everything else '''Datasets used: '''Helicos CAGE on ... TreeFam8, miRBase, microRNA target predictions '''Target journal(s): Genome Research''' '''Internal submission date: December 1st''' '''Contact by email: '''[mailto:Lukasz.Huminiecki@ki.se Lukasz Huminiecki] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Analysis of antisense transcription in loci associated to neurodegenerative diseases ==

'''ManuscriptID: '''Phase1_022 '''Status: '''Working draft '''Abstract: '''The FANTOM5 sequencing datasets represent the largest collection of transcriptomes from human cell lines, primary cells and whole tissues of various origin. Transcription starting sites are mapped at high resolution by the use of a modified protocol of Cap-Analysis of Gene Expression (CAGE) for high-throughput single molecule next-generation sequencing with Helicos (hCAGE). We employed the FANTOM5 collection of data to address the role of antisense transcription in neurodegeneration. We focused our analysis exclusively on tissues and primary cells, to avoid artifacts due to cellular transformation in culture cell lines. Among the >1261 human hCAGE libraries, we selected those of brain origin. Libraries from total blood and selected blood cell populations were also included in the analysis. A total of 66 tissue- and 244 cell-specific libraries were interrogated for the presence of antisense transcription to well-established loci associated to Alzheimer’s disease, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, Huntington’s and Parkinson’s disease. Almost all analyzed genes display some degree of antisense transcription mainly in their 5’ or 3’ UTRs. 5’ head-to-head divergent antisense transcription appears enriched compared to global distribution of sense/antisense pairs. Identified antisense transcripts may have coding and non-coding capabilities, with lncRNAs being more represented. Expressed transcripts are generally poorly annotated and may contain repetitive elements of the Alu, SINE and LINE families. Antisense transcription was validated for a subset of genes, including amyloid precursor protein, microtubule-associated protein tau, DJ-1, leucin-rich repeat kinase 2 and α-synuclein. The validated transcripts are predicted to have non-coding functions and most of them were not annotated. Quantitative analysis of antisense transcripts in human tissues indicates enrichment in the brain, compatible with FANTOM 5 data. Overall, these results represent the most comprehensive analysis of antisense transcription at loci associated to neurodegeneration and provide evidence for the existence of additional regulation of disease-related genes by previously not-annotated long non-coding RNAs. '''Authors:''' Zucchelli SIlvia, Paolo Vatta, Stefania Fedele, Raffaella Calligaris, XXXX (from F5 consortium), Al Forrest, Piero Carninci and Stefano Gustincich '''Authors contribution statement: '''SZ designed the experiments, analyzed the data, wrote the manuscript; PV performed the bioinformatics analysis, prepared some figures; SF designed the experiments, performed the experiments and analyzed the data; RC provided reagents, designed the experiments and analyzed the experiments; SG analyzed the data, wrote the manuscript '''Datasets used: '''Helicos CAGE on human brain and blood samples '''Target journal(s): Genome Research, Plos Genetics, Human Molecular Genetics''' '''Internal submission date: beginning of june''' '''Contact by email: '''[mailto:gustinci@sissa.it,silvia.zucchelli@sissa.it Stefano Gustincich, Silvia Zucchelli] '''Word document version of manuscript for editors: '''[[Image:Zucchelli FANTOM5 satellite 2012 09 14.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Zucchelli Figures.pdf]]

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== Title: Higher order chromatin structure and promoter activity ==

'''ManuscriptID: '''Phase1_023 '''Status: '''Delayed -> moved to PHASE2 '''Abstract: ''' '''Authors: '''Semple CA, Prendergast JG, et al '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on ... '''Target journal(s): ''' '''Internal submission date: October 2012''' '''Contact by email: '''[mailto:Colin.Semple@igmm.ed.ac.uk,prenderj@gmail.com Colin Semple, James Prendergast] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:Building context depending TSS regions from thousands of profiles ==

'''ManuscriptID: '''Phase1_024 '''Status: '''Unknown '''Abstract: '''about DPI '''Authors: '''Kawaji H, et al. '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on phase1 freeze '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:Gateways to the promoter level mammalian expression atlas covering thousands of biological states in FANTOM5 ==

'''ManuscriptID: '''Phase1_025 '''Status: '''working draft '''Abstract: '''Monitoring RNA transcribed within a cell is an essential step toward the identification of active information within the genome, and the understanding the entire cellular system ultimately. Most previous studies involving the collection of a large set of genome-wide transcription profiles consist of tissues and/or cell lines. In the FANTOM5 (Functional ANnotation Of Mammals 5) project we monitored transcription in more than one thousand mammalian samples, including nearly two hundred primary cell types in human and more than one hundred cell types in mouse. We used a sequencing-based digital counting technology, CAGE (Cap Analysis Gene Expression), which skips any PCR amplification steps relying on a single molecule sequencer.  This technology quantifies transcription starting site (TSS) activities at a single base pair resolution across the genomes, and the result is one of the largest sets of expression data available, consisting of diverse range of samples with a single platform based on the state-of-the-art technology.

We assembled the FANTOM5 TSS profiles and subsequent analyses into a centralized data archive and set up various on-line resources available for the scientific community. Researchers in cell biology can easily search samples of interest to inspect active elements within a cell type. Researchers in molecular biology can search genes or transcription factors of interest to inspect in which biological context they are highly activated. Researchers in genome biology and other fields can explore the data within dynamic and interactive graphical user interfaces dedicated for genomic viewing and expression. We based all analysis and database systems on careful annotation of the diverse range of samples, including an application ontology consisting of cell types, anatomy, and diseases. This large set of expression data combined with the extensive and systematic sample annotation enables the scientific community to explore, examine, and slice the data from multiple aspects. Here we introduce the on-line resources and underlying data structure as well as discuss its potential impact in multiple research fields. '''Authors: '''WP4, database providers, and analysis providers '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on phase1 freeze  '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:130111-F5web-resource-main JH HK.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:130104-F5web-resource-fig.pdf]]

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== Title:Application of Semantic MediaWiki to snapshot of thousands of biological states in transcription ==

'''ManuscriptID: '''Phase1_026 '''Status: '''Unknown '''Abstract: '''overview and instruction to the resource browser''' '''Authors: Shimoji H, Kawaji H., WP4 '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on phase1 freeze '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:Comparison of CAGE and RNA-seq transcriptome profiling using a clonally amplified and single molecule next generation sequencing ==

'''ManuscriptID: '''Phase1_027 '''Status: '''finished manuscript '''Abstract:''' CAGE (Cap Analysis Gene Expression) and RNA-seq are two major technologies used for transcript quantification. These protocols measure expression by from either the 5’ end of capped molecules (CAGE) or tags randomly distributed along the length of a transcript (RNA-seq). Library protocols for clonally amplified (Illumina, SOLiD, 454, Ion Torrent) 2nd generation sequencing platforms typically employ PCR pre-amplification prior to clonal amplification, while 3rd generation single molecule sequencers can sequence unamplified libraries. While these protocols individually have been demonstrated to be highly reproducible, no systematic comparison has been carried out between the protocols. Here we compare CAGE using both 2nd and 3rd generation sequencers and RNA-seq using a 2nd generation sequencer based on a panel of RNA mixtures from two human cell lines (THP-1 and HeLa, 100%, 50%, 20%, 10%, 5%, 1% and 0% of HeLa RNAs) to examine power to discriminate biological states, to detect differentially expressed genes, linearity of measurements as well as quantification reproducibility. Quantification by CAGE with the 2nd and 3rd generation sequencers (Illumina GA-IIx and HeliScope) were consistent at gene level, however we observed several differences, which can be explained by differences in their protocols and sequencing platforms. These include significant bias in the Illumina library, such as GC biases and over-estimation of transcripts harboring internal Ecop15I sites., A poorer correlation at the level of individual TSS positions, which is likely to be due to higher indel rate in HeliScope, is also found. We found high consistency between HeliScopeCAGE with RNA-seq (spearman correlations 0.88). Differences between CAGE and RNA-seq are explained by incompleteness of existing gene models in most cases, where 5’-ends of gene models do not reflect actual transcription starting site in the profiled cells, or RNA polymerase run through the poy adenylation site resulting in fusion of neighboring genes. '''Authors: '''WP3 '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: ''' '''Target journal(s): ''Genome Res. '' '''Internal submission date: 23rd Dec, 2012 ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:121223-PlatformEval.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:121223-PlatformEval.pdf]] '''

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== Title:Identification of miRNA promoters and primary structures ==

'''ManuscriptID: '''Phase1_028 '''Status: '''Unknown '''Abstract: ...''' '''Authors: Kawaji H.''' '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Differential roles of epigenetic conversion and Foxp3 expression in regulatory T cell-specific transcriptional regulation ==

'''ManuscriptID: '''Phase1_029 '''Status: '''good draft '''Abstract: '''Naturally occurring regulatory T (Treg) cells are engaged in the maintenance of immune tolerance and homeostasis. The development of Treg cells requires both the expression of the transcription factor Foxp3 and the establishment of Treg cell-type DNA hypomethylation pattern. By transcriptional start site (TSS) cluster analysis, we here assessed possible correlation of genome-wide DNA methylation pattern or Foxp3-binding pattern with Treg-specific gene expression. We found that Treg cell-specific DNA hypomethylated regions were closely correlated with Treg-upregualted TSS clusters, whereas Foxp3-binding regions had no significant correlation with either up- or down-regulated clusters, in non-activated Treg cells. On the other hand, in activated Treg cells, Foxp3-binding regions showed a strong correlation with down-regulated clusters. In silico search for transcription factor-binding motifs revealed that the motifs enriched in Foxp3-binding or Treg-specific DNA hypomethylated regions were mostly different. These results collectively indicate that Treg cell-specific DNA hypomethylation is conducive to up-regulation in the steady state Treg cells whereas Foxp3 expression to down-regulation of its target genes in activated Treg cells. Thus, the combination of the two events is required for the establishment of Treg cell-specific gene expression and function.

(185 words) 

'''Authors: '''Hiromasa Morikawa1,2, Naganari Ohkura1, Alexis Vandenbon3, RIKEN_OSC_members 4, Daron Standley3, Hiroshi Date2, Shimon Sakaguchi1

1. Department of Experimental Immunology, World Premier International Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan 2. Department of Thoracic Surgery, Kyoto University, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan 3. Department of Systems Immunology, World Premier International Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan 4. RIKEN Omics Center, Yokohama, Japan '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s):  Genome Research''' '''Internal submission date:  2012/12/18''' '''Contact by email: '''[mailto:hmorikawa@ifrec.osaka-u.ac.jp Hiromasa Morikawa] '''Word document version of manuscript for editors: [https://fantom5-collaboration.gsc.riken.jp/wiki/images/d/d7/Manuscript_morikawaIFREC.docx manuscript121218.docx]''' '''PDF version for general viewing (including all figs in one PDF): [https://fantom5-collaboration.gsc.riken.jp/wiki/index.php/File:Manuscript_morikawaIFREC.pdf manuscript121228.pdf]'''

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== Title:Automated clustering and quality control pipeline for CAGE technologies ==

'''ManuscriptID: '''Phase1_030 '''Status: '''Working draft '''Abstract:''' To understand the manner and mechanisms of transcription initiation by RNA Polymerase II, different strategies for genome-wide detection of transcription start sites (TSSs) have been developed. We propose the clustering and quality control pipeline suitable for the Cap Analysis of Gene Expression (CAGE) sequence tags. The new framework uses parametric clustering at multiple scales and adopts the irreproducible discovery rate (IDR) to measure reproducibility between replicates of each cluster. Our pipeline reveals that genes have complicated structures of transcription initiation events and discover novel alternative promoters which were not detected by previous approaches. '''Authors: ''' Hiroko Ohmiya1, Morana Vitezic1, Martin Frith, Yoshihide Hayashizaki1, Timo Lassmann1 and many more '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: ''' '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:lassmann@gsc.riken.jp Timo Lassmann] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

 

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== Title: Mogrify: Identifying Defined Factors For Direct Reprogramming Using Next-Generation Sequencing Data And Network Analysis. ==

'''ManuscriptID: '''Phase1_31 '''Status: '''Working draft -> PHASE2? '''Abstract: '''

We now know that cellular state is a plastic phenomenon which it is possible to control. There is an increasing number of reports in the literature where cells have been made to go from fully differentiated cell types to pluripotency and also from one fully differentiated cell type to another. Each of these experiments has relied heavily on a process of trial and error as well as expert knowledge in order to discover the transcription factors capable of inducing a cell conversion. Here we present a novel network based technique (Mogrify) that can identify the factors required for cell conversion. The technique integrates next generation sequence data and biological network knowledge in order to identify transcription factors for over-expression and knock-down along with a conversion likelihood score.

We show that we are able to predict the known reprogramming factors for several successful trans-differentiations from the literature (eg between fibroblast and cardiomyocyte, neuron and hepatocyte) and then provide evidence for a number of unpublished conversions.

The technique is then run without human intervention on every possible combination of over 1000 libraries in the FANTOM 5 set. This information is then used to construct a computational “Waddington landscape”, identifying the best candidate source and target cell types for future cell conversion experiments. This is the first resource of it’s kind, only made possible by the new FANTOM5 promoterome data and represents a considerable step forward in regenerative medicine.

. '''Authors: '''Owen and Julian '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks in all samples '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:owen.rackham@bristol.ac.uk,gough@cs.bris.ac.uk Owen Julian] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Mogrify.pdf]]

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== Title: ADIPOKINES LINK FAT CELLS TO OBESITY-ASSOCIATED CANCER ==

'''ManuscriptID: '''Phase1_32 '''Status: '''Good draft '''Abstract:''' Obesity confers an increased risk of developing specific cancer forms. Although the mechanisms are unclear, increased fat cell secretion of specific proteins (adipokines) may promote/facilitate development of malignant tumors in obesity by cross-talk between adipose tissues and the tissues prone to develop cancer among obese. This was investigated using expression data from human adipose tissue of obese and non-obese as well as from a large panel of human cancer cell lines and corresponding primary cells and tissues. We identified three previously described adipokines, SERPINE1, SERPINE2 and C3 sharing a common cognate receptor LRP1 which was expressed in all cancer cell lines associated with obesity. Expression and secretion of SERPINE1 and C3 were increased in obese adipose tissue and their plasma levels were elevated in obese subjects. We also identified genes enriched in obesity-associated cancer cells compared to cell lines and corresponding healthy tissues or primary cells. We found expression of ceruloplasmin to be the most enriched in obesity-associated cancer cells. This gene was also significantly up-regulated in adipose tissue of obese subjects. Ceruloplasmin is the body’s main copper carrier and is involved in angiogenesis. We demonstrated that ceruloplasmin was a novel adipokine and that obese adipose tissue contributed markedly (22%) to the total protein level. In summary, we have identified several adipokines, which can serve as endocrine signals facilitating growth of obesity-associated cancer tumors. These adipocyte signals are increased in obesity and may be important for development of cancer associated with excess body fat.

 '''Authors:''' Erik Arner, Alistair Forrest, Anna Ehrlund, Niklas Mejhert, [Additional RIKEN people?], Jurga Laurencikiene, Mikael Rydén, Peter Arner '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s):''' Cancer Research '''Internal submission date: ''' '''Contact by email: '''[mailto:arner@gsc.riken.jp Erik Arner] '''Word document version of manuscript for editors: '''[[Image:Fat cells and cancer draft 120816 EA.docx]] [[Image:Figs 2012-08-15.ppt]] '''PDF version for general viewing (including all figs in one PDF): '''

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== Title: ZENBU ==

'''ManuscriptID: '''Phase1_33 '''Status: '''Working draft '''Abstract:'''The world of genome sciences has dramatically changed over the last 5 years. With the advent of next generation sequencers and RNA-expression sequencing, genome science is no longer the domain of a few elite centralized "genome centers" like in the early days of the field. The advance of next-generation sequencers has spurred an ever-growing body of tag-based data allowing the survey of chromatin states and transcriptome dynamics. Visualization of expression levels of genomic regions was achieved by displaying expression levels in various experimental conditions in dedicated tracks allowing investigators a direct comparison of their dynamics. Novel file formats and browser design have allowed for dealing efficiently with the depth of data produced by next-generation sequencer based technologies. Researchers need to interact within global collaborations and need easy ways to process, share and visualize their data in a secured manner prior to publication. To this end we have developed the ZENBU system. ZENBU is a web based system which is a social networking platform for secured data upload and data sharing with collaborators, a data processing system, and a visualization system. ZENBU provides the infrastructure for working with 100s of terrabytes of sequence data in the form of BAM sequence alignment files and genome annotation formats like BED and GFF, to efficiently cross-analyze these databsets using a Map-Reduce/autonomous-agent based parallel processing system, and provide fast efficient web services for user interfaces. The user interfaces for ZENBU is based on Web2.0 technologies in the form of a new expression-enhanced genome browser, and data manipulation interfaces for data upload, data processing, and data download. ZENBU currently contains the entire FANTOM 3/4/5 datasets, the entire ENCODE datasets, and much of the UCSC genome annotation data. ZENBU is planned to be a corner stone in the expanding global network of scientific sharing web systems. 

 '''Authors:''' Jessica Severin*, Marina Lizio, Jayson Harshbarger, Hideya Kawaji, Carsten Daub, The FANTOM5 consortium, Yoshihide Hayashizaki, Nicolas Bertin*, Alistair Forrest* '''Authors contribution statement:''' ''JMS, ML, JH, HK, CD, YH, NB, AL''

*JMS, wrote the software/webservices.
*JMS, NB, planned the study.
*NB supervised the study.
*JMS, NB, contributed to the manuscript writing.
*JMS, NB, gave valuable input to the analysis in the manuscript.
*JMS, NB, critically reviewed the manuscript.
*''[addition of any other, clearer or more precise statement is very welcome]''

 '''Datasets used: '''phase1 CAGE peaks '''Target journal(s):''' Nature Biotech/Genome Research '''Internal submission date: ''' '''Contact by email: '''[mailto:severin@gsc.riken.jp,nbertin@gsc.riken.jp,forrest@gsc.riken.jp Jessica Severin, Nicolas Bertin, Alistair Forrest] '''Word document version of the most up to date manuscript draft: '''[[Image:ZENBU manuscript.014 (1).docx]] '''Word document version of manuscript for editors: '''[[]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:The enhancer and promoter landscape of regulatory and conventional T cell subpopulations ==

'''ManuscriptID: '''Phase1_34 '''Status: '''almost finished manuscript '''Abstract:''' CD4+CD25+FOXP3+ human regulatory T cells (Treg) are essential for self-tolerance and immune homeostasis. Here, we describe the promoterome of CD4+CD25highCD45RA+ naïve and CD4+CD25highCD45RA– memory Treg and their CD25– conventional T cell (Tconv) counterparts both before and after in vitro expansion by cap analysis of gene expression adapted to single molecule sequencing (HeliscopeCAGE). We performed comprehensive comparative digital gene expression analyses and revealed new orphan transcription start sites, of which several were validated as alternative promoters of known genes including FOXP3 and CTLA4. For all in vitro expanded subsets, we additionally generated genome-wide maps of poised and active enhancer elements marked by histone H3 lysine 4 monomethylation and histone H3 lysine 27 acetylation. Analysis of cell type-specific regulatory elements revealed a specific enrichment of several transcription factor binding motifs. We validated promising candidates by chromatin immunoprecipitation coupled to next generation sequencing and identified STAT5 and FOXP3 as well as RUNX1 and ETS1 as global regulators of Treg- and Tconv-specific enhancers, respectively. In summary we provide a highly detailed and easily accessible resource of gene expression and -regulation in Treg and Tconv subpopulations. '''Authors: R''' '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' Blood '''Internal submission date: ''' '''Contact by email: '''[mailto:christian.schmidl@klinik.uni-regensburg.de,michael.rehli@klinik.uni-regensburg.de Christian Schmidl, Michael Rehli] '''Word document version of manuscript for editors: '''[[Image:121027 FANTOM Treg manuscript.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Schmidl Treg.pdf]]

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== Title:Systematic in-vivo characterization of active enhancers across the human body ==

'''ManuscriptID: '''Phase1_35 '''Status: '''Almost finished manuscript '''Abstract: ''' In higher organisms, cellular development and diversity is highly controlled by enhancers, which regulate the correct temporal and cell type-specific activation of gene expression. Despite their obvious importance for development and disease, the exact locations, target genes and mechanisms of enhancers are still poorly defined. Thus, there is an urgent need not only to identify enhancer locations, but also to elucidate their specific usage across the wide diversity of cells within the human body, their impact on regulation in healthy and diseased individuals, and how enhancers interact with target genes. Here, we use the FANTOM5 panel of tissue and primary cell samples covering the majority of human tissues and cell types to define an atlas of active, in vivo bidirectionally transcribed enhancers across the human body. It enables comparison of regulatory programs between different cells and tissues at unprecedented depth, and makes it possible to define distinct subsets of enhancers, including fetal-specific, cell-specific and ubiquitous enhancers – a novel enhancer subtype with distinct properties. We show that known target genes of enhancers can be recaptured using expression correlations and predict many novel enhancer-TSS associations. We present models confirming the utility of multiple redundant enhancers, which explain TSS expression strength rather than expression patterns. We demonstrate that disease-associated functional single nucleotide polymorphisms are over-represented in enhancers and that such enhancers often have disease-relevant expression patterns. The human enhancer atlas can be accessed through an online database and is a unique resource for studies on tissue/cell-specific enhancers and their gene interactions. '''Authors:''' Robin Andersson1#, Claudia Gebhard2#, Irene Miguel-Escalada3, Ilka Hoof1, Xiaobei Zhao1, Christian Schmidl2, Eivind Valen1,4, Kang Li1, Lucia Schwarzfischer2, Dagmar Glatz2, Johanna Raithel2, Yun Chen1, Berit Lilje1, Nicolas Rapin1,5, Frederik Otzen Bagger1,5, Mette Jørgensen1, Mette Boyd1, Jette Bornholdt1, Kenneth Baillie6, Chris Mungall7, Timo Lassmann8, Hideya Kawaji8, Andreas Lennartsson9, Carsten Daub8,9, David Hume6, Peter Heutnik10, Alistair Forrest8, Piero Carninci8, Yoshihide Hayashizaki8, Ferenc Müller3, Michael Rehli2*, Albin Sandelin1* '''Authors contribution statement: '''RA, IH, EV, KL, YC, BL, XZ, MJ, HK, TL, KB, CM, NR, FOB, MR, AS made the computational analysis. TL, HK, CD, AF, PC, YH prepared, mapped and analyzed CAGE libraries. RA, CG, IH, EV, FM, PC, AF, AK, MB, JBL, AL, CD, DH, PH MR, AS interpreted results. CG, CS, ME, MR made the blood cell ChIP experiments, methylation assays and in vitro blood cell validations. IME, FM made zebrafish in vivo validations and interpretations. RA, CG, IH, FM, MR, AS wrote the paper. '''Datasets used: '''phase1 CAGE peaks and raw CAGE mapped data from human, internal ChIP and other validation data '''Target journal(s): ''' To be decided '''Internal submission date: ''' '''Contact by email: '''[robin@binf.ku.dk, michael.rehli@klinik.uni-regensburg.de, albin@binf.ku.dk , Michael Rehli Albin Sandelin] Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]] [[Image:Enhancerome full.pdf]]'''

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== Title: Transcriptome dynamics of mesenchymal stem/stromal cells from the high-grade serous ovarian cancer microenvironment ==

'''ManuscriptID: '''Phase1_036 '''Status: '''Working draft '''Abstract: '''From the most recent and accumulating evidence, the role of cancer microenvironment is being recognized as one of the most critical hallmarks in both cancer progression and metastasis. Mesenchymal Stem/Stromal Cells (MSCs) are the precursors of various cell types that compose both normal and cancer tissue microenvironments. We have isolated MSCs from various High-Grade Serous Ovarian Carcinomas (HG-SOCs), demonstrated their normal genotype, and analyzed their transcriptome using deep-CAGE analysis with respect to similarly derived normal tissues MSCs and to the comprehensive FANTOM5 sample dataset. The integrative analysis conducted against the extensive panel of primary cells and tissues of the FANTOM5 project allowed us to identify a cell-type specific transcriptional activity associated with the HG-SOC-MSCs. The hierarchical clustering analysis shows that MSCs derived from HG-SOCs co-cluster with other MSCs while retaining distinct transcriptional peculiarities. Their transcriptional activity shows a very strong correlation with that of primary mesothelial cells, which actually represent the embryonic cellular origin of serous ovarian cancer. Most importantly, this analysis has revealed HG-SOC-MSCs specific identity when compared to similarly derived MSCs from normal tissues such as bone marrow, heart and adipose tissues, enforcing the idea that the environment organized by the transformed serous ovarian cancer cells could be responsible for establishing such transcriptional specificity in the resident/mobilized stromal precursor cells. Integrating the identified transcriptional signatures of the HG-SOC-MSCs with the gene expression matrices of the publicly available TCGA HG-SOC dataset, we were able to trace HG-SOC-MSC signature in a fraction of the tumor samples. Altogether, the reported analysis support the hypothesis that HG-SOC-MSCs are bona-fide representatives of the ovarian district, either tracing their specific mesothelial origin or highlighting their epigenetic conditioning by the HG-SOC environment. '''Authors: '''Roberto Verardo, Silvano Piazza, Enio Klaric, Yari Ciani, Antonio Beltrami, Daniela Cesselli, Stefania Marzinotto, RIKEN_OSC_members, Carlo Alberto Beltrami, Claudio Schneider '''Authors contribution statement: '''RV conceived the project, performed some of the analysis and most manuscript writing; SP conceived the project developed, carried out statistical tests and results interpretation and wrote parts of the manuscript; YC implemented part of the software and prepared some figures; EK perfermed molecular biology assays ; SM AB and CAB; CS supervised the study '''Datasets used: '''Helicos CAGE on all of F5freeze1 '''Target journal(s): ''' '''Internal submission date: '''October 15th 2012 '''Contact by email: '''[mailto:schneide@lncib.it Claudio Schneider] '''Word document version of manuscript for editors: '''[[Image:Xxx claudio.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Claudio.pdf]]

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== Title: Investigating tissue-specificity of cancer-causing mutations ==

'''ManuscriptID: '''Phase1_037 '''Status: '''Working draft '''Abstract: ''' Over the past 10 years an increasing number of mutated genes have been associated with familial predisposition to cancer. Interestingly for more than half of these genes their involvement in cancer is restricted to only a few cancer types (e.g. BRCA1 mutations in breast and ovarian cancers). Even more interestingly some of these genes are expressed in all cell types, and perhaps we would expect to see them causing many more different types of cancer but they don’t. This paper will examine how these mutations are tolerated in most cell types but not in others by considering the network of genes expressed in different cell types and how that determines whether they are susceptible or resistant. '''Authors: '''Jessica Mar, Daniel Carbajo, RIKEN_OSC_members, Alistair Forrest '''Authors contribution statement: '''JM and AF conceived the project, DC conducted the analyses. '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:jessica.mar@einstein.yu.edu Jessica Mar] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:FANTOM5 reveals the genomic architecture of the genes implicated in Rett Syndrome ==

'''ManuscriptID: '''Phase1_038 '''Status: '''Working draft '''Abstract:''' Mutations in MECP2, FOXG1 and CDKL5 genes cause Rett Syndrome, a neuro-developmental disorder of the grey matter of the brain that almost exclusively affects females. We analyzed the RNA expression data from the FANTOM5 project in both human and mouse to investigate the genomic architecture of the three genes involved in Rett syndrome. Data from FANTOM 5 provides the unprecedented opportunity to study the expression profile, identify transcription start sites and, in conjunction with the recently released ENCODE dataset, identify the regulatory regions and transcription regulators of the three genes implicated in Rett Syndrome. Even though MECP2 and CDKL5 are expressed ubiquitously, mutations in these genes cause a brain specific phenotype suggesting that their role in brain is distinctly important from their function in other tissues. '''Authors:''' Morana Vitezic, Leonard Lipovitch, Alistair RR Forrest, Piero Carninci, Alka Saxena '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' NAR '''Internal submission date: ''' December 2012 '''Contact by email: '''[mailto:mvitezic@gsc.riken.jp,alka@gsc.riken.jp Morana Vitezic Alka Saxena] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Manuscript template ==

'''NOTE: Make a copy of the format below, paste it above and then edit with your details'''

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== Title:COPY THEN EDIT THIS TEMPLATE ==

'''ManuscriptID: '''Phase1_00x (INCREMENT THIS) '''Abstract: ...''' '''Authors: R''' '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:blah@change.this.edu,next.adress@change.this CHANGETHIScorresponding1 CHANGETHIScorresponding2] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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Satellite submission

2013-01-14T13:37:14Z

Martin: /* Title: Quantifying the informational complexity of transcriptional regulatory programmes */

== Satellite manuscript internal review page ==

Welcome to the FANTOM5 Satellite review page. As discussed at the Ume and Koyo meetings, all papers will be visible to consortium members. This is to allow everyone to know what is going on, promote collaboration, carry out due process regarding co-authorship and to avoid competition.

== Authorship ==

The author list will basically be selected by the first author and the corresponding author of each satellite paper on the basis of the scientific contribution to the manuscript. Remember to include an authors contribution statement for all authors named in your manuscript (of the form AB carried out the cell isolation, SB carried out the network predictions etc.).

In addition the FANTOM5 headquarter will name RIKEN OSC members who should be co-authors for their input on each manuscript and to the entire FANTOM5 project. For those of you who have participated in previous FANTOMs you will be familiar with this process, for those new to FANTOM please look at the author lists on the satellite paper collections for FANTOM2-4. FANTOM5 headquarter is currently discussing the policy for RIKEN OSC co-authorship on the FANTOM5 satellites, but basically satellites papers will be considered on a case by case basis, and will take into account datasets used, intellectual input and facilitating technologies/analyses for each paper.

At this stage please name any authors from the OSC that you think should definitely be included as co-authors, in addition for all satellite submissions include the following term '''RIKEN_OSC_members''' as an additional author.

== Instructions ==

Please make a copy of the template below and enter your manuscript details.

If you are not able to edit the wiki yourself please email the secretariat with the subject line "FANTOM5_satellite", but please understand that these will be processed when we can rather than immediately. You must fill in all of the details below and provide both a PDF that contains all figures, and word doc of the main text, for reviewers to mark up directly.

= Manuscripts =

== Title: Analysis of DNA methylation and transcription during granulopoiesis reveals timed methylation changes in low CpG areas and regulation of transcription factor expression and motif activity ==

'''ManuscriptID: '''Phase1_001 '''Status: '''Good draft '''Abstract: '''In development epigenetic mechanisms such as DNA methylation have been suggested to provide cellular memory to maintain pluripotency but also stabilize cell fate decisions and direct lineage restriction. In this study we set out to characterize changes in DNA methylation levels and gene expression during granulopoiesis using four distinct cell populations ranging from the oligopotent common myeloid progenitor stage to terminally differentiated neutrophils. We found a general decrease of DNA methylation during granulopoiesis. Methylation levels appear to change at specific differentiation stages and correlate with changes in transcription and motif activity of key hematopoietic transcription factors. Differentially methylated sites (DMSs) are preferentially located in areas distal to CpG islands and shores and are overrepresented in potentially regulatory enhancer elements. Overall this study depicts in detail the epigenetic and transcriptional changes that occur during granulopoiesis and supports the role of DNA methylation as a regulatory mechanism in cell differentiation. '''Authors: '''Michelle Rönnerblad, Tor Olofsson, Sören Lehmann, RIKEN_OSC_members, Karl Ekwall*, Erik Arnér* & Andreas Lennartsson* '''Authors contribution statement: '''MR did most of the practical experiments, the bioinfo analysis (except CAGE related) and most manuscript writing, TO isolated the cells from bone marrows, SL gave valuable input to the planning, analysis and critically reviewed the manuscript, KE planned and supervised the study and contributed to the manuscript writing , EA supervised the bioinformatic analysis and performed the ones related to CAGE and contributed to the manuscript writing, AL initiated, planned and supervised the study and contributed to the manuscript writing and did some experiments. '''Datasets used: '''Helicos CAGE on granulo precursor populations '''Target journal(s): '''Blood '''Internal submission date: '''April 7th 2012 '''Contact by email: '''[mailto:andreas.lennartsson@ki.se,Karl.Ekwall@ki.se,arner@gsc.riken.jp andreas lennartsson, Karl Ekwall, Erik Arner] '''Word document version of manuscript for editors: '''[[Image:Rönnerblad.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Rönnerblad Aprl07.pdf]]

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== Title: Cell-type specificity and co-expression of regulatory polymorphisms associated with human disease ==

'''ManuscriptID: '''Phase1_002 '''Status: '''Good draft '''Abstract: ''' Our ability to use genetic associations with disease to develop better treatments has been limited by the difficulty of identifying a biological process, or cell type, on which to focus investigation. Most disease-associated polymorphisms do not lie within protein-coding genes, raising the possibility that variation in regulatory sequence plays a critical role in disease phenotypes. We have used genome-scale 5’RACE (CAGE) to identify the location and usage of transcription start sites in 864 human tissues, primary cells and cell lines, and show here that there is a strong enrichment for disease-associated variants within the sequence immediately adjacent to transcription start sites. Using the expression profiles of known variants associated with disease susceptibility, we identify experimentally-available cell types significantly associated with specific diseases and traits. The expression of genes known to be associated with particular diseases was positively correlated. Such co-expression was used to identify unreported candidate disease-associated regulatory regions within published genome-wide association studies (GWAS). The approach was validated by identifying candidate loci in a 2007 GWAS study that were subsequently validated in larger independent datasets These functional genomics approaches directly inform choices of model system and identify disease- and cell type-specific co-regulated networks for a wide range of common diseases.

 '''Authors: '''Baillie JK*, Haley CS, Schaefer U, Faulkner GJ, Freeman T, Brown JB, [others...], [Numerous RIKEN authors, order etc. TBC, at least including: Kawaji H, Forrest A, Carninci P]*, Hume DA* '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on Primary Cells '''Target journal(s): '''Nature Genetics '''Internal submission date: ''' ... '''Contact by email: '''[mailto:j.k.baillie@ed.ac.uk,david.hume@roslin.ed.ac.uk Kenneth Baillie, David Hume] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: What classes of mammalian promoter are there? ==

'''ManuscriptID: '''Phase1_003 '''Status: '''Working draft '''Abstract: '''This study uses the comprehensive FANTOM5 promoter data, and careful methodology, to identify classes of mammalian promoter. In agreement with previous results, we find that promoters fall into two classes with narrow or wide spread of transcription start sites. In stark contrast to previous studies, we find little association between width and either CpG rate or TATA signals. Width correlates with expression level, suggesting that strength of promoter signal is on average proportional to promoter length. The data are consistent with a simple null hypothesis for CpG islands: that they are a passive consequence of expression (and thus cytosine demethylation and reduced CpG mutation) in germ-line cells. Finally, we show that measures of tissue specificity are prone to statistical artifacts, and specificity is not correlated with promoter narrowness, in contrast to previous claims. These results clarify some fundamental properties of mammalian promoters. '''Todo: '''Use Charles's good way of measuring tissue specificity. '''Authors: '''Frith, maybe Drabløs et al., open to others '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''All human Phase1 CTSSs (plan to add mouse) '''Target journal(s): ''' '''Internal submission date: '''August 2012? '''Contact by email: '''[mailto:martin@cbrc.jp Martin Frith] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Epigenetic factors regulating Hematopoiesis ==

'''ManuscriptID: '''Phase1_004 '''Status: '''Good draft '''Abstract: '''The hematopoietic differentiation pathway is a complex regulatory program for generating different lineages of blood cell types from multipotent, hematopoietic stem cells. The transcriptional program dictating hematopoietic cell fate and differentiation requires an epigenetic memory function consisting of a network of enzymes controlling DNA methylation, histone posttranslational modifications and chromatin structure. Defective interactions between epigenetic enzymes and transcription factors cause perturbations in blood cell differentiation, which often leads to various types of hematopoietic disorders such as leukemia. To elucidate the contribution of different epigenetic factors in human hematopoieis, high-throughput Cap Analysis of Gene Expression (CAGE) sequencing was used to build comprehensive transcription profiles of 199 epigenetic factors in a wide range of blood cells. These epigenetic factors include proteins that covalently modify DNA/histones or alter chromatin structure dynamics. Our analysis revealed several epigenetic factors to have expression profiles specific for cell type, lineage type and/or leukemic cell lines. In this report the ‘epigenetic transcriptome’ has been systematically studied to predict their potential functions in the epigenetic regulatory network of human hematopoiesis. The potential of such a comprehensive study is not only to identify putative epigenetic regulators of normal hematopoiesis and postulate their function but also to serve as a resource for the scientific community for further characterization and validation of differentially expressed transcripts. '''Authors: '''Punit Prasad, Michelle Rönnerblad,...FANTOM5, Erik Arner, Karl Ekwall and Andreas Lennartsson '''Authors contribution statement: '''PP and MR have done analysis and written the manuscript. EA has performed the initial CAGE analysis for the epigenetic factors and assisted in writing the manuscript. AL and KE have assisted in writing the manuscript, planned and coordinated the study. The authors declare no conflict of interest. '''Datasets used: '''Helicos CAGE on ... '''Target journal(s): '''Blood or other '''Internal submission date: '''December 06, 2012 '''Contact by email: '''[mailto:andreas.lennartsson@ki.se,arner@gsc.riken.jp Andreas Lennartsson, Erik Arner] '''Word document version of manuscript for editors: '''[[Image:Prasad et al amnuscriot Blood.docx]], [[Image:Prasad et al Blood Figs.pdf]], [[Image:Prasad et al Table S1 .xlsx]], [[Image:Prasad et al Table S2.xlsx]], [[Image:Prasad Table S3.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Phase1 004.pdf]]

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== Title: Ab Initio Prediction of Tissue-Specific Regulatory Modules in the FANTOM5 Project ==

'''ManuscriptID: '''Phase1_005 '''Status: '''Final draft '''Abstract: '''One of the major goals of the FANTOM5 project, the broadest TSS-based promoter-level expression atlas of transcriptional regulatory networks, is the identification of coding and non-coding, annotated and novel transcriptional units being transcribed in a cell-specific mode across the different biological states/samples. In this work we analyzed the FANTOM5 dataset using ScanAll, a newly developed software here described, to ab initio predict the presence of conserved elements in the genomic regions surrounding FANTOM5 promoters. Firstly we aimed at identifying motifs that were conserved in a subset of the selected genomic regions and that possibly corresponded to Transcription Factor Binding Sites (TFBS); we then expanded our analysis to pinpoint the existence of more complex, structured regulatory modules, that is groups of conserved motifs co-occurring in the aforementioned (co-expressed) regions within a fixed distance. We confirmed the sample-specificity of our output by showing that the majority of the obtained combinations of modules were able to divide the specimens into sample-specific groups, thus possibly explaining the peculiarities of regulatory events occurring in each tissue. Among these sites it was possible to confirm the presence of TFBS for known regulators already associated to those samples together with an additional and significant portion of motifs remaining unannotated, thus representing putative novel binding elements. In addition we were able to associate the presence of a significant portion of the identified motifs to distinct families of repeated elements, thus confirming a structural/functional feature of mammalian promoters that is currently emerging as one of the most peculiar regulatory aspects associated to mammalian phylogeny. Finally, we were able to identify previously uncharacterized aspects of the regulatory networks occurring in early-development samples thus confirming the significant advantage deriving from our modular approach. '''Authors: '''Emiliano Dalla, Yari Ciani, Marco Zantoni, RIKEN_OSC_members, Alberto Policriti, Claudio Schneider, Silvano Piazza '''Authors contribution statement: '''ED conceived the project, developed part of the software, oversaw implementation, performed some of the analysis and most manuscript writing; YC implemented part of the software and prepared some figures; MZ developed and implemented part of the software; AP developed part of the software and contributed to the manuscript writing; CS supervised the study; SP developed and implemented part of the software, carried out statistical tests and results interpretation and wrote parts of the manuscript. '''Datasets used: '''Helicos CAGE on all of F5freeze1 '''Target journal(s): ''' '''Internal submission date: '''June 1st 2012; Update: December 21st 2012 '''Contact by email: '''[mailto:emiliano.dalla@lncib.it Emiliano Dalla] '''Word document version of manuscript for editors: '''[[Image:FANTOM5 PromoteromeSatelliteLNCIB.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:FANTOM5 PromoteromeSatelliteLNCIB wFigures.pdf]]

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== Title: Homotypic clusters of transcription factor binding sites in the vicinity of transcription start sites ==

'''ManuscriptID: '''Phase1_006 '''Status: '''Finished draft 

'''Abstract: ''' ''Background'' Transcription factors (TFs) specifically recognizing DNA binding sites (TFBS) play a key role in regulation of gene expression. Groups of closely localized TFBSs for a particular TF, so-called homotypic TFBS clusters (HCBSs), were originally detected in yeast and extensively studied in fruit fly early development. Recently HCs were found to be highly important for several human regulatory systems.

''Motivation'' It is a general practice to estimate an enrichment of binding sites in regulatory sequences. Still there is no systematized data whether the presence of HCBSs is common for promoter regions of human genes. The general properties of HCBSs also remain unclear as well as possible relation between HCBSs and regulation of tissue-specific expression.

''Results'' Using data on sample-specific transcription start sites (TSSs) detected in FANTOM5 and high quality binding models for more than 400 TFs from the HOCOMOCO TFBS model collection we have predicted TFBSs and corresponding HCBSs in promoter regions surrounding TSSs. TFBS models for most TFs were shown to form statistically significant HCBSs often formed by separate distant binding sites. For HCBSs of most of TFs we were able to identify samples having significant association between promoters of sample-specific or housekeeping TSSs. Thus for most of TFs we predict putative preferences for sample-specific or housekeeping HCBSs activity and provide a genome-wide map of HCBSs nearby FANTOM5-defined TSSs.

''Supplementary information'' https://fantom5-collaboration.gsc.riken.jp/webdav/home/vigg/homotypicus/

'''Authors: '''I.V. Kulakovskiy, Y.A. Medvedeva, M.S. Polishchuk, A.V. Favorov, S. Schmeier, T. Lassman, I.E. Vorontsov, RIKEN_OSC_members, V.J. Makeev 

'''Authors contribution statement: ''' IVK implemented the software and drafted the manuscript. YAM carried out statistical tests and results interpretation. MSP developed the homotypic cluster detection algorithm. AVF selected proper statistical tests. SS provided the housekeeping set of TSS-clusters. TL provided the set of sample-specific TSS-clusters. IEV estimated proper thresholds for PWMs used in the study. VJM coordinated the study. All the authors participated in writing and finalizing the manuscript. 

'''Datasets used: '''Helicos CAGE - FANTOM5 FREEZE1, "robust" subset 

'''Target journal(s): ''' Nucleic Acids Research, Bioinformatics 

'''Internal submission date: '''18 June 2012 / Updated: 12 September 2012 / Minor fixes: 1 December 2012 

'''Contact by email: '''[mailto:vsevolod.makeev@gmail.com,ivan.kulakovskiy@gmail.com Vsevolod Makeev, Ivan Kulakovskiy] 

'''Word document version of manuscript for editors: '''[[Image:HOMOTYPICUS-FANTOMsatellitepaper.r1.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:HOMOTYPICUS-FANTOMsatellitepaper.r1.pdf]]

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== Title: A high resolution spatial-temporal promoterome of the human brain (was Brain CAGE) ==

'''ManuscriptID: '''Phase1_007 '''Status: '''Good draft '''Abstract: ''' The human brain is an extremely complex organ that governs our abilities for cognition, reasoning and emotions and is the control center for the body. Its morphology and functionality during development have been well studied, but the molecular mechanisms contributing to its function and maintenance later in life remain poorly understood. Complexity at the transcriptional level is likely to play a major role in defining its morphological and functional characteristics. To investigate this we used single molecule CAGE and created a high resolution atlas of transcription start sites for 15 anatomical regions of the human central nervous system, using post-mortem samples derived from infant and aged adult donors. On the transcriptional level brain is clearly distinguishable from other tissues even if we consider only non-coding genes or expression from genomic regions often described as genomic dark matter. Using these differences we identify a specific set of transcription start sites that characterizes the brain. We show extensive differences in transcription between infant and adult that in some cases can be linked to loci associated with major neurodegenerative diseases. The differential expression across distinct regions correlates well with developmentally and/or functionally related anatomical districts and is refelected by distinct networks of interacting transcription factors, a range of lncRNAs and novel transcripts co-expressed in a regionally biased manner. Overall we provide the scientific community with a powerful expression resource based on post-mortem tissue, particularly highlighting the contribution of non-coding RNAs to the transcriptional complexity of human central nervous system.

 '''Authors: '''Margherita Francescatto, Morana Vitezic, Patrizia Rizzu, Javier Simon-Sanchez, Robin Andersson, FANTOM5_RIKEN_OSC_members, Carsten O Daub, Albin Sandelin, MIchiel JL de Hoon, Piero Carninci, Alistair RR Forrest, Peter Heutink 

'''Authors contribution statement: '''MF and MV did the analyses; MF, MV and PH wrote the manuscript, PR selected all samples, evaluated medical and pathological records and isolated RNA, JSS curated the list of disease loci, RA and AS provided the list of enhancers, ARRF, PC and PH designed the study ... 

'''Datasets used: '''Helicos CAGE on VUMC provided brain samples (adult and newborn); full list of samples presented in Supplementary Table 1 

'''Target journal(s): ''' Genome Research 

'''Internal submission date: ''' 

'''Contact by email: '''[mailto:Peter.Heutink@dzne.de,m.francescatto@vumc.nl,mvitezic@gsc.riken.jp Peter Heutink, Margherita Francescatto, Morana Vitezic] 

'''Word document version of manuscript for editors: '''[[Image:BrainCAGE manuscript presubmission enquiery.doc]] [[Image:BrainCAGE figures presubmission enquiery.pdf]] 

'''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Pathogen specific monocyte transcriptional responses ==

'''ManuscriptID: '''Phase1_008 '''Status: '''Working draft '''Abstract: ''' '''Authors: '''Wells '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on ... '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:c.wells@uq.edu.au,a.beckhouse@uq.edu.au Christine Wells, Anthony Beckhouse] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Transcriptome profiling of human skin mast cells by deep CAGE identifies unexpected gene activity patterns through direct comparison with multiple cell and tissue subsets ==

'''ManuscriptID: '''Phase1_009 '''Status: '''Working draft '''Abstract: '''Despite their haematopoietic origin, mast cells (MCs) mature exclusively in peripheral tissues, hampering research into their developmental and functional programs. Here, we employed deep-CAGE on skin-derived MCs to generate the most comprehensive view of the human MC transcriptome ever reported. A particular advantage is that MCs were embedded in the FANTOM5 project, giving the opportunity to contrast their molecular signature against an extensive panel of human samples. We demonstrate that MCs possess a unique and surprising transcriptional landscape, combining expression of typical haematopoietic genes with those exclusively active in MCs, and genes not previously reported as expressed in MCs. Specifically we found that MCs express functional BMP receptors, which transduce pro-survival and activatory signals. Conversely, several genes frequently studied in MCs were either not or only weakly expressed in direct comparison with other myelocytes. By the parallel use of MCs ex vivo and following culture, we also found that MCs change their transcriptome in in vitro surroundings. Befitting their uniqueness, MCs had no close relative in the haematopoietic network. This rich dataset reveals that our knowledge of human MCs is still fairly limited. It can be anticipated that with this resource novel functional programs of MCs will soon be discovered. 
'''Authors: '''Efthymios Motakis,1,* Sven Guhl,2,* Yuri Ishizu,1 RIKEN OSC members,1 Torsten Zuberbier,2 Alistair R R Forrest,1¶ Magda Babina2¶ 
'''Authors contribution statement: '''E.M. carried out bioifnormatics analayses S.G. isolated the mast cells and performed most experiments, M.B. performed several experiments, was involved in planning, supervision, and data analysis, and wrote the first draft of the manuscript, E.M. S.G., A.R.R.F. and T.Z. helped with planning, data analysis and manuscript writing. '''Datasets used: '''Helicos CAGE on mast cell samples in comparison to freeze 1 data '''Target journal(s): '''Blood, eBlood '''Internal submission date: ''' '''Contact by email: '''[mailto:magda.babina@charite.de,sven.guhl@charite.de Magda Babina, Sven Guhl] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:MC_satellite_Jan_6_merged.pdf]]

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== Title: Effect of cytosine methylation on transcription factor binding sites and regulation of transcription ==

'''ManuscriptID: '''Phase1_010 '''Status: '''Good draft '''Abstract: ''' Motivation: DNA methylation of gene promoters is strongly linked to gene repression. However, the mechanism of interaction between DNA methylation and gene repression is not fully understood. We cannot strictly state that DNA methylation of gene promoters is a cause of gene repression or, vise versa, that gene repression induced either by chromatin modification or by binding of Polycomb proteins leads to subsequent DNA methylation. Potential mechanism for transcriptional regulation by DNA methylation can be driven by methylation-induced changes in either accessibility of transcription factors (TFs) binding sites (TFBSs) or affinity of TFs to their TFBSs. This idea is supported by non-systematic evidences. Until now, this hypothesis has not been tested systematically for a wide spectrum of TFs with known TFBS models and across large number of cell types. Methods: To estimate DNA methylation in 50 different cell types we used data obtained by reduced representation bisulfite sequencing (RRBS) provided by the ENCODE project. To evaluate genome-wide expression in the corresponding cell types we utilized FANTOM5 data obtained by cap-analysis of gene expression (CAGE). To predict TFBSs we used remote dependency model (RDM), a generalization of a position weight matrix (PWM), which takes into consideration the correlation of remote nucleotides within a binding site and has been shown to effectively decrease false positive rate compared to the widely used PWM approach. Results and conclusions: In this work we surprisingly show that only 5% of CpG dinucleotides correspond to “traffic lights” genome positions, i.e. they manifest moderate to high negative correlation of their methylation profile and an expression profile of a neighboring TSS across cell samples. Significant share of TFBSs tend to avoid CpG “traffic lights”. This tendency is less pronounced if a binding site is surrounded by a homotypic cluster of TFBSs, suggesting that a loss of function for one TFBS due to methylation can be compensated by closely located weaker TFBSs for the same TF. In a way, this puts into a different perspective the current common perception of the link of methylation and gene expression. '''Authors: '''Medvedeva YA, Khamis A, Ba-Alawi W, Bhuyan MdSI, [potential F5 collaborators], Kulakovskiy IV, Bajic VB '''Authors contribution statement: '''YAM designed the computational experiments, selected and preprocessed the data, produced statistical analysis and wrote the manuscript; AK performed most of the data analysis; WBA and MdSIB contributed RDM models and tools for threshold estimation and mapping; [potential F5 collaborators], IVK performed part of the analysis, contributed to the design of the experiments and writing of the manuscript; VBB contributed to the design of the experiments and writing of the manuscript. '''Datasets used: '''Helicos CAGE on 50 sample types, ENCODE RRBS data for the same samples '''Target journal(s): ''' '''Internal submission date: '''December, 16 '''Contact by email: '''[mailto:ju.medvedeva@gmail.com Yulia Medvedeva] '''Word document version of manuscript for editors: '''[[Image:Effect of cytosine methylation on transcription factor binding sites and regulation of transcription.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Effect of cytosine methylation on transcription factor binding sites and regulation of transcription.pdf]]

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== Title: Transcription and enhancer profiling in human monocyte subsets ==

'''ManuscriptID: '''Phase1_011 '''Status: '''Good draft '''Abstract: ''' Human blood monocytes comprise at least three subpopulations that differ in phenotype and function. Here we present the first in-depth regulome analysis of classical (CD14++CD16-), intermediate (CD14+CD16+), and nonclassical (CD14dimCD16+) monocytes. Cap Analysis of Gene Expression (CAGE) adapted to Helicos single molecule sequencing was used to map transcription start sites throughout the genome in all three subsets. In addition, global maps of H3K4me1 and H3K27ac deposition were generated for classical and nonclassical monocytes defining enhanceosomes of the two major subsets. We identify differential regulatory elements (including promoters and putative enhancers) that were associated with subset-specific motif signatures corresponding to different transcription factor activities and exemplarily validate a novel downstream enhancer of the CD14 locus. In addition to known subset specific features, pathway analysis revealed marked differences in metabolic gene signatures. While classical monocytes expressed higher levels of genes involved in carbohydrate metabolism priming them for anaerobic energy production, nonclassical monocytes expressed higher levels of oxidative pathway components and showed a higher routine mitochondrial activity. Our findings describe promoter/enhancer landscapes and provide novel insights into the specific biology of human monocyte subsets. '''Authors: '''Christian Schmidl, Kathrin Renner, Ruediger Eder, Katrin Peter, Petra Hoffmann, Reinhard Andreesen, Marina P. Kreutz, RIKEN_OSC_members, Matthias Edinger, Michael Rehli '''Authors contribution statement: '''CS performed experiments, computational analyses and wrote parts of the manuscript writing, KR performed experiments and contributed to manuscript writing, RE isolated the cells, KP performed experiments, PH, RA, MK, and ME contributed to planning and supervision, RIKEN_OSC_members who organized or performed Helicos sequencing and provided aligned data; MR initiated, planned and supervised the study, performed computational analyses, and wrote the manuscript. '''Datasets used: '''Helicos CAGE on monocyte subsets (Regensburg samples) '''Target journal(s): ''' Blood, eBlood, other '''Internal submission date: September 1 ,2012 ''' '''Contact by email: '''[mailto:michael.rehli@ukr.de,Christian.Schmidl@klinik.uni-regensburg.de Michael Rehli, Christian Schmidl] '''Word document version of manuscript for editors: '''[[Image:Schmidl MonoSub.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Schmidl MonoSub.pdf]]  

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== Title: ... ==

'''ManuscriptID: '''Phase1_012 '''Status: '''Gone '''Abstract: ''' '''Authors: '''... '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on ... '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:blobby@blob.town Mr Blobby] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: The Evolution of Human Cells in terms of Protein Innovation ==

'''ManuscriptID: '''Phase1_013 '''Status: '''Working draft '''Abstract: ''' Humans are complex organisms composed of a great many cell types. Since the genomic DNA of each cell is identical, cell type is determined by what is expressed. We examine the evolutionary history of each human cell type at the molecular level via the collective histories of proteins, the principal product of gene expression. Sequence data from the FANTOM5 consortium are used to provide cell-type specific digital expression of protein-coding genes, and the SUPERFAMILY and dcGO resources provide domain and function annotation respectively. Cross-referencing with the domain annotation of all other completely-sequenced genomes provides the evolutionary context for each protein. We combine all of this to generate a description of cellular evolution at the molecular level.

We present a protein domain view of the evolution of cell type. To achieve this we first identify the most recent common ancestor (MRCA) or ‘creation epoch’ of every protein in the repertoire of the human genome. We are then able to use the protein creation epochs to describe the history of the emergence of each cell type over evolution in terms of the collective histories of the proteins expressed in that cell type. Each cell type has an evolutionary profile consisting of a timeline along the lineage from the ancient cellular ancestor to modern day human. The profile of each cell type shows at which epochs along the timeline innovations in protein evolution took place; required to allow the observed expression in that type of cell. By clustering cell types on these profiles, we find groups of cell types that share a parallel protein evolutionary history and thus potentially possess a common progenitor cell type or are evolving in cooperation. A functional enrichment analysis of these clusters reveals key proteins responsible for evolutionary shifts and functional innovations; it also suggests a possible order in which different cells could have emerged during evolution, which we discuss in relation to the human immune system. The structural domain-centric perspective which we employ in this work can also be used as the basis for a comparison of the molecular basis of functional and phenotypic differences between cell types within these evolutionary clusters, exemplified by an inspection of our results on different regions of the brain.

We present a view of the landscape of nature’s innovation of protein structure and architecture required to explain the creation of the different human cell types. This landscape has some important features such as the possibility that the last universal ancestor of life provided most of the innovation for the innate immune system whilst brain cells have been making use of novel proteins that first appeared in opisthokonta (animals and fungi) and continued to do so right up until homo sapiens. The landscape also lends itself to identifying candidate genes for disease by highlighting those that were important in enabling certain phenotypic shifts at key points in evolution. '''Authors: '''Julian Gough, Owen Rackham, Adam Sardar, Matt Oates + Sample Providers + RIKEN OSC '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on all samples '''Target journal(s):''' Bioinformatics? '''Internal submission date: ''' '''Contact by email: '''[mailto:gough@compsci.bristol.ac.uk,owen.rackham@gmail.com Julian Gough, Owen Rackham] '''Word document version of manuscript for editors: '''[[Rough draft available on request]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:TrapDraftv2.pdf]]

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== Title: Transcriptional profiling by deep CAGE of the human fibrillin/LTBP gene family, key regulators of mesenchymal cell functions.==

'''Manuscrjavascript:void(0)iptID''': Phase1_014 '''Status: '''Working draft '''Abstract:''' The fibrillins and latent transforming growth factor binding proteins (LTBPs) form a superfamily of extracellular matrix (ECM) proteins characterized by the presence of a unique domain, the 8-cysteine transforming growth factor beta (TGFβ) binding domain (TB domain). These proteins are involved in both maintaining the extracellular matrix and controlling the bioavailability of TGFβ family members. Genes encoding these proteins show differential expression in mesenchymal cell types which synthesise the extracellular matrix and give rise to connective tissues. We have investigated the promoter regions of the seven gene family members using the FANTOM5 CAGE data base for human. Although the protein and nucleotide sequences showed considerable homology (for the protein sequence of fibrillins the maximum sequence homology was 68% between fibrillin1 and fibrillin2; minimum sequence homology was 59% between fibrillin1 and fibrillin3), the promoter regions were quite diverse. The three fibrillin genes had a single predominant promoter cluster, while LTBP1 and LTBP4 showed promoter switching. The depth of the current CAGE study revealed that most of the family members were expressed in a range of mesenchymal and other cell types, often associated with use of alternative promoters or changes in the transcription start site within a compound promoter. FBN3 was the lowest expressed gene, and was expressed only in embryonic and fetal tissues, primarily neurological. There was evidence of enhancer activity in the regions of the genes. Each gene showed a unique pattern of transcription factor motifs or activity. This study highlights the role of alternative transcription start sites in regulating the tissue specificity of closely related genes and suggests that this important class of extracellular matrix genes is subject to subtle regulatory variations that explain the differential roles of members of this gene family.. '''Authors:''' Margaret R Davis, RIKEN OSC members, Kim M Summers '''Authors contribution statement:''' MRD performed most of the analysis and contributed to writing the paper, RIKEN OSC did ..., KMS performed the analysis and contributed to writing the paper '''Datasets used:''' Helicos CAGE on ... '''Target journal(s):''' '''Internal submission date: Contact by email: '''[mailto:kim.summers@roslin.ed.ac.uk kim.summers@roslin.ed.ac.uk] '''Word document version of manuscript for editors:''' File:XXXYOUR.doc '''PDF version for general viewing (including all figs in one PDF):''' File:XXXYOUR.pdf

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== Title: Quantifying the informational complexity of transcriptional regulatory programmes ==

'''ManuscriptID: '''Phase1_015 '''Status: '''On-hold. Focussing on the biological results Phase1_016 rather than methods. Hope to return to methods later (phase2). '''Abstract: ''' The regulation of gene expression defines cellular identity, it is the basis for organism development and it underlies many cellular responses to the environment. Its disruption is implicated in many diseases and changes in gene regulation appear to underlie many adaptations evident between species. Previously, genes have been grouped and interpreted based on their specificity of expression, for example house-keeping genes that are expressed by all cells in all conditions versus highly tissue restricted genes expressed by only one cell type at a particular developmental time. Although such studies have been informative they fail to capture important aspects of how a gene is regulated or account for the heterogeneous relatedness of samples. The expression pattern of a gene is the output of a regulatory program within the cell. A program that must affect many state changes (on, off, up, down) is likely to require more regulatory information (Kolmogorov complexity) than a program effecting fewer state switches. If we can quantify this "regulatory complexity" we can then start to address deeper questions as to where that regulatory information is encoded, how malleable it is through evolution and how susceptible it is to perturbation by mutation. For example, a greater regulatory complexity could correspond to a higher concentration of cis-regulatory sequences around the gene or alternatively a single binding site for a transcription factor that is the output of an extensive intracellular signalling network. To address these questions we have explored a range of possible measures regulatory complexity including distance weighted entropies, diversity and richness scores. This leads us to introduce a novel measure of regulatory complexity (CR). It is implemented as a hierarchical Baysian model parametrised through MCMC. The CR method can be thought of as a relative measure of the number of gene expression state changes occurring over a tree relating all analysed samples. A by-product of this analysis is a probabilistic scoring of gene expression state switches between all analysed gene expression libaries. CR is weighted to account for the genome wide similarity of gene expression between samples but does not depend on the inference of a fixed underlying tree topology. Note - this is intended as essentially a methods paper, see Phase1_016 for the biological insights paper '''Authors:''' Sarah Baker, Martin Taylor '''Authors contribution statement: '''SB developed and implemented methods and performed general analyses; MT conceived the project and oversaw implementation and performed some of the analysis '''Datasets used: '''Helicos CAGE on primary cells from human and mouse. '''Target journal(s): ''' Bioinformatics or Genome Research '''Internal submission date:''' ETA July 2013 '''Contact by email: '''[mailto:martin.tayor@igmm.ed.ac.uk,sarah.baker@igmm.ed.ac.uk Martin Taylor, Sarah Baker] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Cis encoding of the master developmental regulatory programme ==

'''ManuscriptID: '''Phase1_016 '''Status: '''Unknown '''Abstract: ''' The regulation of gene expression defines cellular identity, it is the basis for organism development and it underlies many cellular responses to the environment. Its disruption is implicated in many diseases and changes in gene regulation appear to underlie many adaptations evident between species. '''Authors:''' Sarah Baker, Martin Taylor '''Authors contribution statement: '''SB developed and implemented methods and performed general analyses; MT conceived the project and oversaw implementation and performed some of the analysis '''Datasets used: '''Helicos CAGE on primary cells from human and mouse. We may also want to use time course data for this paper (does that push it into phase2?). '''Target journal(s): '''PLoS Biology '''Internal submission date:''' ETA October 2012 '''Contact by email: '''[mailto:martin.tayor@igmm.ed.ac.uk,sarah.baker@igmm.ed.ac.uk Martin Taylor, Sarah Baker] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Correspondence between CAGE clusters and chromatin marks ==

'''ManuscriptID: '''Phase1_017 '''Status: '''Preliminary Draft (Moved to Phase 2) '''Abstract: '''The paper presents an analysis of the correlation between cell-type-specific CAGE clusters and chromatin marks, using FANTOM CAGE data and ENCODE ChIP-Seq data for the four ENCODE cell lines K562, Gm12878, Helas3 and Hepg2. It shows that active chromatin marks are present at both expressed and repressed clusters. Chromatin profiles around expressed CAGE clusters have various shapes, and can be grouped into combinatorial subclusters based on their profiles. Repressed clusters with active chromatin mark represents a set of poised CAGE clusters enriched for Pol II and linked to immune response. The latter clusters also have a well-positioned nucleosome at the TSS. The manuscript is only preliminary, and some of the analysis still remains to be performed. The general content of the paper is considerably different than what is described in the report posted here previosly. [[Media:CAGE_cluster_evaluation_Drablos_Rye_13012012.pdf]] and April [[Media:CAGE_clusters_and_chromatin_Drablos_Rye_26042012.pdf]].) '''Authors: '''Morten Rye, Finn Drablos '''Authors contribution statement: '''MR and FD did data analysis and wrote the paper '''Datasets used: '''Helicos CAGE data, ENCODE chromatin ChIP-Seq and DNase HS data '''Target journal(s): ''' '''Internal submission date: '''Most likely February 2013 '''Contact by email: '''[mailto:finn.drablos@ntnu.no,morten.rye@ntnu.no Finn Drablos,Morten Rye] '''Word document version of manuscript for editors: '''[[Image:Preliminary Draft Drablos Rye 11-12-2012.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:All draft figs Drablos Rye 11-12-2012.pdf]] '''Supplementary figures: '''[[Image:All supplem figs Drablos Rye 11-12-2012.pdf]]

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== Title: Promoter specificity in transcription determines cell lineage choice ==

'''ManuscriptID: '''Phase1_018 '''Status: '''Delayed (as of September 12th) '''Abstract: '''This paper will use pathprint (pathway fingerprinting) to develop an overall phylogenetic tree of all samples in F5 freeze1. This tree will be used to determine relative ancestry of samples and cluster them accordingly. SwitchEngine will be run to find switching in TSS at key junctions in differentiation. Will show TSS dynamics at these informative sites is associated with lineage-commitment. '''Authors: '''Emmanuel Dimont, Gabriel Altschuler, Winston Hide '''Authors contribution statement: '''ED did ..., GA did ..., WH did ... '''Datasets used: '''Helicos CAGE on all of F5freeze1 '''Target journal(s): ''' '''Internal submission date: '''Most likely July-August 2012 '''Contact by email: '''[mailto:edimont@hsph.harvard.edu,gabrielaltschuler@googlemail.com,whide@hsph.harvard.edu Winston Hide, Emmanuel Dimont, Gabriel Altschuler] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Patterns of expression space change in the F5-CAGE encyclopedia of vertebrate gene expression. ==

'''ManuscriptID: '''Phase1_019 '''Status: '''Finished draft '''Abstract: ''' F5-CAGE encyclopedia of expression patterns is arguably the most comprehensive and technologically uniform functional genomics dataset ever generated. F5-CAGE includes 952 human and 396 mouse tissues (T), primary cells (PC) and cancer cell-lines (CCL). Here, we use F5-CAGE to explore expression space change in multiple contexts. Brain exhibits unique transcriptional features, including clustering into fetal, newborn, and adult samples. All samples group into three distinct categories with respect to expression evolution rate. There is trend for young genes to be tissue-speciﬁc, with the exception of taxon Eutheria. A major divide between leukemias and solid tumors is seen in CCL. Paralog expression pattern divergence suggests global devolution of expression in CCL. We explore global differences between T and CCL samples further, though family analysis and self-organizing maps. As a focused family evolution example, we use cdc42 family which features many tissue-speciﬁc genes and dramatic expression pattern shifts, correlated with ENCODE Tfbs. PhyloSigs suggest novel hypotheses for animal evolution: CNS and reproductive track are discussed as two examples. Most genes have multiple TSSes, with up to 87 for tintin, contributing to multiple isoforms which were previously attributed to alternative splicing alone. TSSes correlate between human and mouse, older genes tend to have more TSSes, and TSS-rich genes are associated with cancer. Finally, we test the hypothesis of CTCF acting as isolator between paralogs, and instead show its function is more likely in bringing duplicates under the control of the same enhancer. The trend is illustrated with semenogelins and pregnancy speciﬁc glycoproteins '''Authors: '''Lukasz Huminiecki, Oxana Sachenkova and Core RIKEN Authors 

'''Authors contribution statement: ''' LH: gathered and prepared the data, planned the study and analyzed the data, wrote the manuscript OS: wrote the software to analyze the data, performed the analysis, prepared the figures 

'''Datasets used: '''Helicos CAGE, TreeFam8, ENCODE TFBS ChIP-Seq '''Target journal(s): Genome Research''' '''Internal submission date: November 30th''' '''Contact by email: '''[mailto:Lukasz.Huminiecki@ki.se Lukasz Huminiecki] ,[mailto:oxana.sachenkova@scilifelab.se Oxana Sachenkova] '''Word document version of manuscript for editors : '''[[Image:The structure of animal expression pattern evolution.doc]] (only text) '''PDF version for general viewing (including all figs in one PDF): '''[[Image:The structure of animal expression pattern evolution.pdf]] (this file includes all the figures)

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== Title: Gene duplication and promoter divergence in mammals. ==

'''ManuscriptID: '''Phase1_020 '''Status: '''Delayed '''Abstract: ''' '''Authors: '''Lukasz Huminiecki and Core RIKEN Authors '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... and LH did everything else '''Datasets used: '''Helicos CAGE on ..., F5 promoter and enhancer datasets, TreeFam8 '''Target journal(s): Genome Research''' '''Internal submission date: September 1st''' '''Contact by email: '''[mailto:Lukasz.Huminiecki@ki.se Lukasz Huminiecki] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Gene duplication and TF/miRNA regulatory network evolution in mammals. ==

'''ManuscriptID: '''Phase1_021 '''Status: '''Delayed '''Abstract: ''' '''Authors: '''Lukasz Huminiecki and Core RIKEN Authors '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... and LH did everything else '''Datasets used: '''Helicos CAGE on ... TreeFam8, miRBase, microRNA target predictions '''Target journal(s): Genome Research''' '''Internal submission date: December 1st''' '''Contact by email: '''[mailto:Lukasz.Huminiecki@ki.se Lukasz Huminiecki] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Analysis of antisense transcription in loci associated to neurodegenerative diseases ==

'''ManuscriptID: '''Phase1_022 '''Status: '''Working draft '''Abstract: '''The FANTOM5 sequencing datasets represent the largest collection of transcriptomes from human cell lines, primary cells and whole tissues of various origin. Transcription starting sites are mapped at high resolution by the use of a modified protocol of Cap-Analysis of Gene Expression (CAGE) for high-throughput single molecule next-generation sequencing with Helicos (hCAGE). We employed the FANTOM5 collection of data to address the role of antisense transcription in neurodegeneration. We focused our analysis exclusively on tissues and primary cells, to avoid artifacts due to cellular transformation in culture cell lines. Among the >1261 human hCAGE libraries, we selected those of brain origin. Libraries from total blood and selected blood cell populations were also included in the analysis. A total of 66 tissue- and 244 cell-specific libraries were interrogated for the presence of antisense transcription to well-established loci associated to Alzheimer’s disease, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, Huntington’s and Parkinson’s disease. Almost all analyzed genes display some degree of antisense transcription mainly in their 5’ or 3’ UTRs. 5’ head-to-head divergent antisense transcription appears enriched compared to global distribution of sense/antisense pairs. Identified antisense transcripts may have coding and non-coding capabilities, with lncRNAs being more represented. Expressed transcripts are generally poorly annotated and may contain repetitive elements of the Alu, SINE and LINE families. Antisense transcription was validated for a subset of genes, including amyloid precursor protein, microtubule-associated protein tau, DJ-1, leucin-rich repeat kinase 2 and α-synuclein. The validated transcripts are predicted to have non-coding functions and most of them were not annotated. Quantitative analysis of antisense transcripts in human tissues indicates enrichment in the brain, compatible with FANTOM 5 data. Overall, these results represent the most comprehensive analysis of antisense transcription at loci associated to neurodegeneration and provide evidence for the existence of additional regulation of disease-related genes by previously not-annotated long non-coding RNAs. '''Authors:''' Zucchelli SIlvia, Paolo Vatta, Stefania Fedele, Raffaella Calligaris, XXXX (from F5 consortium), Al Forrest, Piero Carninci and Stefano Gustincich '''Authors contribution statement: '''SZ designed the experiments, analyzed the data, wrote the manuscript; PV performed the bioinformatics analysis, prepared some figures; SF designed the experiments, performed the experiments and analyzed the data; RC provided reagents, designed the experiments and analyzed the experiments; SG analyzed the data, wrote the manuscript '''Datasets used: '''Helicos CAGE on human brain and blood samples '''Target journal(s): Genome Research, Plos Genetics, Human Molecular Genetics''' '''Internal submission date: beginning of june''' '''Contact by email: '''[mailto:gustinci@sissa.it,silvia.zucchelli@sissa.it Stefano Gustincich, Silvia Zucchelli] '''Word document version of manuscript for editors: '''[[Image:Zucchelli FANTOM5 satellite 2012 09 14.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Zucchelli Figures.pdf]]

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== Title: Higher order chromatin structure and promoter activity ==

'''ManuscriptID: '''Phase1_023 '''Status: '''Delayed -> moved to PHASE2 '''Abstract: ''' '''Authors: '''Semple CA, Prendergast JG, et al '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on ... '''Target journal(s): ''' '''Internal submission date: October 2012''' '''Contact by email: '''[mailto:Colin.Semple@igmm.ed.ac.uk,prenderj@gmail.com Colin Semple, James Prendergast] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:Building context depending TSS regions from thousands of profiles ==

'''ManuscriptID: '''Phase1_024 '''Status: '''Unknown '''Abstract: '''about DPI '''Authors: '''Kawaji H, et al. '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on phase1 freeze '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:Gateways to the promoter level mammalian expression atlas covering thousands of biological states in FANTOM5 ==

'''ManuscriptID: '''Phase1_025 '''Status: '''working draft '''Abstract: '''Monitoring RNA transcribed within a cell is an essential step toward the identification of active information within the genome, and the understanding the entire cellular system ultimately. Most previous studies involving the collection of a large set of genome-wide transcription profiles consist of tissues and/or cell lines. In the FANTOM5 (Functional ANnotation Of Mammals 5) project we monitored transcription in more than one thousand mammalian samples, including nearly two hundred primary cell types in human and more than one hundred cell types in mouse. We used a sequencing-based digital counting technology, CAGE (Cap Analysis Gene Expression), which skips any PCR amplification steps relying on a single molecule sequencer.  This technology quantifies transcription starting site (TSS) activities at a single base pair resolution across the genomes, and the result is one of the largest sets of expression data available, consisting of diverse range of samples with a single platform based on the state-of-the-art technology.

We assembled the FANTOM5 TSS profiles and subsequent analyses into a centralized data archive and set up various on-line resources available for the scientific community. Researchers in cell biology can easily search samples of interest to inspect active elements within a cell type. Researchers in molecular biology can search genes or transcription factors of interest to inspect in which biological context they are highly activated. Researchers in genome biology and other fields can explore the data within dynamic and interactive graphical user interfaces dedicated for genomic viewing and expression. We based all analysis and database systems on careful annotation of the diverse range of samples, including an application ontology consisting of cell types, anatomy, and diseases. This large set of expression data combined with the extensive and systematic sample annotation enables the scientific community to explore, examine, and slice the data from multiple aspects. Here we introduce the on-line resources and underlying data structure as well as discuss its potential impact in multiple research fields. '''Authors: '''WP4, database providers, and analysis providers '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on phase1 freeze  '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:130111-F5web-resource-main JH HK.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:130104-F5web-resource-fig.pdf]]

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== Title:Application of Semantic MediaWiki to snapshot of thousands of biological states in transcription ==

'''ManuscriptID: '''Phase1_026 '''Status: '''Unknown '''Abstract: '''overview and instruction to the resource browser''' '''Authors: Shimoji H, Kawaji H., WP4 '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''Helicos CAGE on phase1 freeze '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:Comparison of CAGE and RNA-seq transcriptome profiling using a clonally amplified and single molecule next generation sequencing ==

'''ManuscriptID: '''Phase1_027 '''Status: '''finished manuscript '''Abstract:''' CAGE (Cap Analysis Gene Expression) and RNA-seq are two major technologies used for transcript quantification. These protocols measure expression by from either the 5’ end of capped molecules (CAGE) or tags randomly distributed along the length of a transcript (RNA-seq). Library protocols for clonally amplified (Illumina, SOLiD, 454, Ion Torrent) 2nd generation sequencing platforms typically employ PCR pre-amplification prior to clonal amplification, while 3rd generation single molecule sequencers can sequence unamplified libraries. While these protocols individually have been demonstrated to be highly reproducible, no systematic comparison has been carried out between the protocols. Here we compare CAGE using both 2nd and 3rd generation sequencers and RNA-seq using a 2nd generation sequencer based on a panel of RNA mixtures from two human cell lines (THP-1 and HeLa, 100%, 50%, 20%, 10%, 5%, 1% and 0% of HeLa RNAs) to examine power to discriminate biological states, to detect differentially expressed genes, linearity of measurements as well as quantification reproducibility. Quantification by CAGE with the 2nd and 3rd generation sequencers (Illumina GA-IIx and HeliScope) were consistent at gene level, however we observed several differences, which can be explained by differences in their protocols and sequencing platforms. These include significant bias in the Illumina library, such as GC biases and over-estimation of transcripts harboring internal Ecop15I sites., A poorer correlation at the level of individual TSS positions, which is likely to be due to higher indel rate in HeliScope, is also found. We found high consistency between HeliScopeCAGE with RNA-seq (spearman correlations 0.88). Differences between CAGE and RNA-seq are explained by incompleteness of existing gene models in most cases, where 5’-ends of gene models do not reflect actual transcription starting site in the profiled cells, or RNA polymerase run through the poy adenylation site resulting in fusion of neighboring genes. '''Authors: '''WP3 '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: ''' '''Target journal(s): ''Genome Res. '' '''Internal submission date: 23rd Dec, 2012 ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:121223-PlatformEval.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:121223-PlatformEval.pdf]] '''

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== Title:Identification of miRNA promoters and primary structures ==

'''ManuscriptID: '''Phase1_028 '''Status: '''Unknown '''Abstract: ...''' '''Authors: Kawaji H.''' '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:kawaji@gsc.riken.jp KAWAJI Hideya] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title: Differential roles of epigenetic conversion and Foxp3 expression in regulatory T cell-specific transcriptional regulation ==

'''ManuscriptID: '''Phase1_029 '''Status: '''good draft '''Abstract: '''Naturally occurring regulatory T (Treg) cells are engaged in the maintenance of immune tolerance and homeostasis. The development of Treg cells requires both the expression of the transcription factor Foxp3 and the establishment of Treg cell-type DNA hypomethylation pattern. By transcriptional start site (TSS) cluster analysis, we here assessed possible correlation of genome-wide DNA methylation pattern or Foxp3-binding pattern with Treg-specific gene expression. We found that Treg cell-specific DNA hypomethylated regions were closely correlated with Treg-upregualted TSS clusters, whereas Foxp3-binding regions had no significant correlation with either up- or down-regulated clusters, in non-activated Treg cells. On the other hand, in activated Treg cells, Foxp3-binding regions showed a strong correlation with down-regulated clusters. In silico search for transcription factor-binding motifs revealed that the motifs enriched in Foxp3-binding or Treg-specific DNA hypomethylated regions were mostly different. These results collectively indicate that Treg cell-specific DNA hypomethylation is conducive to up-regulation in the steady state Treg cells whereas Foxp3 expression to down-regulation of its target genes in activated Treg cells. Thus, the combination of the two events is required for the establishment of Treg cell-specific gene expression and function.

(185 words) 

'''Authors: '''Hiromasa Morikawa1,2, Naganari Ohkura1, Alexis Vandenbon3, RIKEN_OSC_members 4, Daron Standley3, Hiroshi Date2, Shimon Sakaguchi1

1. Department of Experimental Immunology, World Premier International Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan 2. Department of Thoracic Surgery, Kyoto University, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan 3. Department of Systems Immunology, World Premier International Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan 4. RIKEN Omics Center, Yokohama, Japan '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s):  Genome Research''' '''Internal submission date:  2012/12/18''' '''Contact by email: '''[mailto:hmorikawa@ifrec.osaka-u.ac.jp Hiromasa Morikawa] '''Word document version of manuscript for editors: [https://fantom5-collaboration.gsc.riken.jp/wiki/images/d/d7/Manuscript_morikawaIFREC.docx manuscript121218.docx]''' '''PDF version for general viewing (including all figs in one PDF): [https://fantom5-collaboration.gsc.riken.jp/wiki/index.php/File:Manuscript_morikawaIFREC.pdf manuscript121228.pdf]'''

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== Title:Automated clustering and quality control pipeline for CAGE technologies ==

'''ManuscriptID: '''Phase1_030 '''Status: '''Working draft '''Abstract:''' To understand the manner and mechanisms of transcription initiation by RNA Polymerase II, different strategies for genome-wide detection of transcription start sites (TSSs) have been developed. We propose the clustering and quality control pipeline suitable for the Cap Analysis of Gene Expression (CAGE) sequence tags. The new framework uses parametric clustering at multiple scales and adopts the irreproducible discovery rate (IDR) to measure reproducibility between replicates of each cluster. Our pipeline reveals that genes have complicated structures of transcription initiation events and discover novel alternative promoters which were not detected by previous approaches. '''Authors: ''' Hiroko Ohmiya1, Morana Vitezic1, Martin Frith, Yoshihide Hayashizaki1, Timo Lassmann1 and many more '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: ''' '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:lassmann@gsc.riken.jp Timo Lassmann] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

 

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== Title: Mogrify: Identifying Defined Factors For Direct Reprogramming Using Next-Generation Sequencing Data And Network Analysis. ==

'''ManuscriptID: '''Phase1_31 '''Status: '''Working draft -> PHASE2? '''Abstract: '''

We now know that cellular state is a plastic phenomenon which it is possible to control. There is an increasing number of reports in the literature where cells have been made to go from fully differentiated cell types to pluripotency and also from one fully differentiated cell type to another. Each of these experiments has relied heavily on a process of trial and error as well as expert knowledge in order to discover the transcription factors capable of inducing a cell conversion. Here we present a novel network based technique (Mogrify) that can identify the factors required for cell conversion. The technique integrates next generation sequence data and biological network knowledge in order to identify transcription factors for over-expression and knock-down along with a conversion likelihood score.

We show that we are able to predict the known reprogramming factors for several successful trans-differentiations from the literature (eg between fibroblast and cardiomyocyte, neuron and hepatocyte) and then provide evidence for a number of unpublished conversions.

The technique is then run without human intervention on every possible combination of over 1000 libraries in the FANTOM 5 set. This information is then used to construct a computational “Waddington landscape”, identifying the best candidate source and target cell types for future cell conversion experiments. This is the first resource of it’s kind, only made possible by the new FANTOM5 promoterome data and represents a considerable step forward in regenerative medicine.

. '''Authors: '''Owen and Julian '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks in all samples '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:owen.rackham@bristol.ac.uk,gough@cs.bris.ac.uk Owen Julian] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Mogrify.pdf]]

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== Title: ADIPOKINES LINK FAT CELLS TO OBESITY-ASSOCIATED CANCER ==

'''ManuscriptID: '''Phase1_32 '''Status: '''Good draft '''Abstract:''' Obesity confers an increased risk of developing specific cancer forms. Although the mechanisms are unclear, increased fat cell secretion of specific proteins (adipokines) may promote/facilitate development of malignant tumors in obesity by cross-talk between adipose tissues and the tissues prone to develop cancer among obese. This was investigated using expression data from human adipose tissue of obese and non-obese as well as from a large panel of human cancer cell lines and corresponding primary cells and tissues. We identified three previously described adipokines, SERPINE1, SERPINE2 and C3 sharing a common cognate receptor LRP1 which was expressed in all cancer cell lines associated with obesity. Expression and secretion of SERPINE1 and C3 were increased in obese adipose tissue and their plasma levels were elevated in obese subjects. We also identified genes enriched in obesity-associated cancer cells compared to cell lines and corresponding healthy tissues or primary cells. We found expression of ceruloplasmin to be the most enriched in obesity-associated cancer cells. This gene was also significantly up-regulated in adipose tissue of obese subjects. Ceruloplasmin is the body’s main copper carrier and is involved in angiogenesis. We demonstrated that ceruloplasmin was a novel adipokine and that obese adipose tissue contributed markedly (22%) to the total protein level. In summary, we have identified several adipokines, which can serve as endocrine signals facilitating growth of obesity-associated cancer tumors. These adipocyte signals are increased in obesity and may be important for development of cancer associated with excess body fat.

 '''Authors:''' Erik Arner, Alistair Forrest, Anna Ehrlund, Niklas Mejhert, [Additional RIKEN people?], Jurga Laurencikiene, Mikael Rydén, Peter Arner '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s):''' Cancer Research '''Internal submission date: ''' '''Contact by email: '''[mailto:arner@gsc.riken.jp Erik Arner] '''Word document version of manuscript for editors: '''[[Image:Fat cells and cancer draft 120816 EA.docx]] [[Image:Figs 2012-08-15.ppt]] '''PDF version for general viewing (including all figs in one PDF): '''

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== Title: ZENBU ==

'''ManuscriptID: '''Phase1_33 '''Status: '''Working draft '''Abstract:'''The world of genome sciences has dramatically changed over the last 5 years. With the advent of next generation sequencers and RNA-expression sequencing, genome science is no longer the domain of a few elite centralized "genome centers" like in the early days of the field. The advance of next-generation sequencers has spurred an ever-growing body of tag-based data allowing the survey of chromatin states and transcriptome dynamics. Visualization of expression levels of genomic regions was achieved by displaying expression levels in various experimental conditions in dedicated tracks allowing investigators a direct comparison of their dynamics. Novel file formats and browser design have allowed for dealing efficiently with the depth of data produced by next-generation sequencer based technologies. Researchers need to interact within global collaborations and need easy ways to process, share and visualize their data in a secured manner prior to publication. To this end we have developed the ZENBU system. ZENBU is a web based system which is a social networking platform for secured data upload and data sharing with collaborators, a data processing system, and a visualization system. ZENBU provides the infrastructure for working with 100s of terrabytes of sequence data in the form of BAM sequence alignment files and genome annotation formats like BED and GFF, to efficiently cross-analyze these databsets using a Map-Reduce/autonomous-agent based parallel processing system, and provide fast efficient web services for user interfaces. The user interfaces for ZENBU is based on Web2.0 technologies in the form of a new expression-enhanced genome browser, and data manipulation interfaces for data upload, data processing, and data download. ZENBU currently contains the entire FANTOM 3/4/5 datasets, the entire ENCODE datasets, and much of the UCSC genome annotation data. ZENBU is planned to be a corner stone in the expanding global network of scientific sharing web systems. 

 '''Authors:''' Jessica Severin*, Marina Lizio, Jayson Harshbarger, Hideya Kawaji, Carsten Daub, The FANTOM5 consortium, Yoshihide Hayashizaki, Nicolas Bertin*, Alistair Forrest* '''Authors contribution statement:''' ''JMS, ML, JH, HK, CD, YH, NB, AL''

*JMS, wrote the software/webservices.
*JMS, NB, planned the study.
*NB supervised the study.
*JMS, NB, contributed to the manuscript writing.
*JMS, NB, gave valuable input to the analysis in the manuscript.
*JMS, NB, critically reviewed the manuscript.
*''[addition of any other, clearer or more precise statement is very welcome]''

 '''Datasets used: '''phase1 CAGE peaks '''Target journal(s):''' Nature Biotech/Genome Research '''Internal submission date: ''' '''Contact by email: '''[mailto:severin@gsc.riken.jp,nbertin@gsc.riken.jp,forrest@gsc.riken.jp Jessica Severin, Nicolas Bertin, Alistair Forrest] '''Word document version of the most up to date manuscript draft: '''[[Image:ZENBU manuscript.014 (1).docx]] '''Word document version of manuscript for editors: '''[[]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:The enhancer and promoter landscape of regulatory and conventional T cell subpopulations ==

'''ManuscriptID: '''Phase1_34 '''Status: '''almost finished manuscript '''Abstract:''' CD4+CD25+FOXP3+ human regulatory T cells (Treg) are essential for self-tolerance and immune homeostasis. Here, we describe the promoterome of CD4+CD25highCD45RA+ naïve and CD4+CD25highCD45RA– memory Treg and their CD25– conventional T cell (Tconv) counterparts both before and after in vitro expansion by cap analysis of gene expression adapted to single molecule sequencing (HeliscopeCAGE). We performed comprehensive comparative digital gene expression analyses and revealed new orphan transcription start sites, of which several were validated as alternative promoters of known genes including FOXP3 and CTLA4. For all in vitro expanded subsets, we additionally generated genome-wide maps of poised and active enhancer elements marked by histone H3 lysine 4 monomethylation and histone H3 lysine 27 acetylation. Analysis of cell type-specific regulatory elements revealed a specific enrichment of several transcription factor binding motifs. We validated promising candidates by chromatin immunoprecipitation coupled to next generation sequencing and identified STAT5 and FOXP3 as well as RUNX1 and ETS1 as global regulators of Treg- and Tconv-specific enhancers, respectively. In summary we provide a highly detailed and easily accessible resource of gene expression and -regulation in Treg and Tconv subpopulations. '''Authors: R''' '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' Blood '''Internal submission date: ''' '''Contact by email: '''[mailto:christian.schmidl@klinik.uni-regensburg.de,michael.rehli@klinik.uni-regensburg.de Christian Schmidl, Michael Rehli] '''Word document version of manuscript for editors: '''[[Image:121027 FANTOM Treg manuscript.docx]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Schmidl Treg.pdf]]

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== Title:Systematic in-vivo characterization of active enhancers across the human body ==

'''ManuscriptID: '''Phase1_35 '''Status: '''Almost finished manuscript '''Abstract: ''' In higher organisms, cellular development and diversity is highly controlled by enhancers, which regulate the correct temporal and cell type-specific activation of gene expression. Despite their obvious importance for development and disease, the exact locations, target genes and mechanisms of enhancers are still poorly defined. Thus, there is an urgent need not only to identify enhancer locations, but also to elucidate their specific usage across the wide diversity of cells within the human body, their impact on regulation in healthy and diseased individuals, and how enhancers interact with target genes. Here, we use the FANTOM5 panel of tissue and primary cell samples covering the majority of human tissues and cell types to define an atlas of active, in vivo bidirectionally transcribed enhancers across the human body. It enables comparison of regulatory programs between different cells and tissues at unprecedented depth, and makes it possible to define distinct subsets of enhancers, including fetal-specific, cell-specific and ubiquitous enhancers – a novel enhancer subtype with distinct properties. We show that known target genes of enhancers can be recaptured using expression correlations and predict many novel enhancer-TSS associations. We present models confirming the utility of multiple redundant enhancers, which explain TSS expression strength rather than expression patterns. We demonstrate that disease-associated functional single nucleotide polymorphisms are over-represented in enhancers and that such enhancers often have disease-relevant expression patterns. The human enhancer atlas can be accessed through an online database and is a unique resource for studies on tissue/cell-specific enhancers and their gene interactions. '''Authors:''' Robin Andersson1#, Claudia Gebhard2#, Irene Miguel-Escalada3, Ilka Hoof1, Xiaobei Zhao1, Christian Schmidl2, Eivind Valen1,4, Kang Li1, Lucia Schwarzfischer2, Dagmar Glatz2, Johanna Raithel2, Yun Chen1, Berit Lilje1, Nicolas Rapin1,5, Frederik Otzen Bagger1,5, Mette Jørgensen1, Mette Boyd1, Jette Bornholdt1, Kenneth Baillie6, Chris Mungall7, Timo Lassmann8, Hideya Kawaji8, Andreas Lennartsson9, Carsten Daub8,9, David Hume6, Peter Heutnik10, Alistair Forrest8, Piero Carninci8, Yoshihide Hayashizaki8, Ferenc Müller3, Michael Rehli2*, Albin Sandelin1* '''Authors contribution statement: '''RA, IH, EV, KL, YC, BL, XZ, MJ, HK, TL, KB, CM, NR, FOB, MR, AS made the computational analysis. TL, HK, CD, AF, PC, YH prepared, mapped and analyzed CAGE libraries. RA, CG, IH, EV, FM, PC, AF, AK, MB, JBL, AL, CD, DH, PH MR, AS interpreted results. CG, CS, ME, MR made the blood cell ChIP experiments, methylation assays and in vitro blood cell validations. IME, FM made zebrafish in vivo validations and interpretations. RA, CG, IH, FM, MR, AS wrote the paper. '''Datasets used: '''phase1 CAGE peaks and raw CAGE mapped data from human, internal ChIP and other validation data '''Target journal(s): ''' To be decided '''Internal submission date: ''' '''Contact by email: '''[robin@binf.ku.dk, michael.rehli@klinik.uni-regensburg.de, albin@binf.ku.dk , Michael Rehli Albin Sandelin] Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]] [[Image:Enhancerome full.pdf]]'''

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== Title: Transcriptome dynamics of mesenchymal stem/stromal cells from the high-grade serous ovarian cancer microenvironment ==

'''ManuscriptID: '''Phase1_036 '''Status: '''Working draft '''Abstract: '''From the most recent and accumulating evidence, the role of cancer microenvironment is being recognized as one of the most critical hallmarks in both cancer progression and metastasis. Mesenchymal Stem/Stromal Cells (MSCs) are the precursors of various cell types that compose both normal and cancer tissue microenvironments. We have isolated MSCs from various High-Grade Serous Ovarian Carcinomas (HG-SOCs), demonstrated their normal genotype, and analyzed their transcriptome using deep-CAGE analysis with respect to similarly derived normal tissues MSCs and to the comprehensive FANTOM5 sample dataset. The integrative analysis conducted against the extensive panel of primary cells and tissues of the FANTOM5 project allowed us to identify a cell-type specific transcriptional activity associated with the HG-SOC-MSCs. The hierarchical clustering analysis shows that MSCs derived from HG-SOCs co-cluster with other MSCs while retaining distinct transcriptional peculiarities. Their transcriptional activity shows a very strong correlation with that of primary mesothelial cells, which actually represent the embryonic cellular origin of serous ovarian cancer. Most importantly, this analysis has revealed HG-SOC-MSCs specific identity when compared to similarly derived MSCs from normal tissues such as bone marrow, heart and adipose tissues, enforcing the idea that the environment organized by the transformed serous ovarian cancer cells could be responsible for establishing such transcriptional specificity in the resident/mobilized stromal precursor cells. Integrating the identified transcriptional signatures of the HG-SOC-MSCs with the gene expression matrices of the publicly available TCGA HG-SOC dataset, we were able to trace HG-SOC-MSC signature in a fraction of the tumor samples. Altogether, the reported analysis support the hypothesis that HG-SOC-MSCs are bona-fide representatives of the ovarian district, either tracing their specific mesothelial origin or highlighting their epigenetic conditioning by the HG-SOC environment. '''Authors: '''Roberto Verardo, Silvano Piazza, Enio Klaric, Yari Ciani, Antonio Beltrami, Daniela Cesselli, Stefania Marzinotto, RIKEN_OSC_members, Carlo Alberto Beltrami, Claudio Schneider '''Authors contribution statement: '''RV conceived the project, performed some of the analysis and most manuscript writing; SP conceived the project developed, carried out statistical tests and results interpretation and wrote parts of the manuscript; YC implemented part of the software and prepared some figures; EK perfermed molecular biology assays ; SM AB and CAB; CS supervised the study '''Datasets used: '''Helicos CAGE on all of F5freeze1 '''Target journal(s): ''' '''Internal submission date: '''October 15th 2012 '''Contact by email: '''[mailto:schneide@lncib.it Claudio Schneider] '''Word document version of manuscript for editors: '''[[Image:Xxx claudio.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:Claudio.pdf]]

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== Title: Investigating tissue-specificity of cancer-causing mutations ==

'''ManuscriptID: '''Phase1_037 '''Status: '''Working draft '''Abstract: ''' Over the past 10 years an increasing number of mutated genes have been associated with familial predisposition to cancer. Interestingly for more than half of these genes their involvement in cancer is restricted to only a few cancer types (e.g. BRCA1 mutations in breast and ovarian cancers). Even more interestingly some of these genes are expressed in all cell types, and perhaps we would expect to see them causing many more different types of cancer but they don’t. This paper will examine how these mutations are tolerated in most cell types but not in others by considering the network of genes expressed in different cell types and how that determines whether they are susceptible or resistant. '''Authors: '''Jessica Mar, Daniel Carbajo, RIKEN_OSC_members, Alistair Forrest '''Authors contribution statement: '''JM and AF conceived the project, DC conducted the analyses. '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:jessica.mar@einstein.yu.edu Jessica Mar] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Title:FANTOM5 reveals the genomic architecture of the genes implicated in Rett Syndrome ==

'''ManuscriptID: '''Phase1_038 '''Status: '''Working draft '''Abstract:''' Mutations in MECP2, FOXG1 and CDKL5 genes cause Rett Syndrome, a neuro-developmental disorder of the grey matter of the brain that almost exclusively affects females. We analyzed the RNA expression data from the FANTOM5 project in both human and mouse to investigate the genomic architecture of the three genes involved in Rett syndrome. Data from FANTOM 5 provides the unprecedented opportunity to study the expression profile, identify transcription start sites and, in conjunction with the recently released ENCODE dataset, identify the regulatory regions and transcription regulators of the three genes implicated in Rett Syndrome. Even though MECP2 and CDKL5 are expressed ubiquitously, mutations in these genes cause a brain specific phenotype suggesting that their role in brain is distinctly important from their function in other tissues. '''Authors:''' Morana Vitezic, Leonard Lipovitch, Alistair RR Forrest, Piero Carninci, Alka Saxena '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' NAR '''Internal submission date: ''' December 2012 '''Contact by email: '''[mailto:mvitezic@gsc.riken.jp,alka@gsc.riken.jp Morana Vitezic Alka Saxena] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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== Manuscript template ==

'''NOTE: Make a copy of the format below, paste it above and then edit with your details'''

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== Title:COPY THEN EDIT THIS TEMPLATE ==

'''ManuscriptID: '''Phase1_00x (INCREMENT THIS) '''Abstract: ...''' '''Authors: R''' '''Authors contribution statement: '''MR did ..., TO did ..., KE did ..., EA did ..., AL did ... '''Datasets used: '''phase1 CAGE peaks '''Target journal(s): ''' '''Internal submission date: ''' '''Contact by email: '''[mailto:blah@change.this.edu,next.adress@change.this CHANGETHIScorresponding1 CHANGETHIScorresponding2] '''Word document version of manuscript for editors: '''[[Image:XXXYOUR.doc]] '''PDF version for general viewing (including all figs in one PDF): '''[[Image:XXXYOUR.pdf]]

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File:Topic1.pdf

2012-09-25T07:26:26Z

Martin:

File:Projection tables.pdf

2012-07-19T01:56:05Z

Martin:

Cross-species projection of TSS

2012-07-19T01:32:40Z

Martin:

==Contacts==
*martin.taylor@igmm.ed.ac.uk

==Objectives==
* Project (liftOver) human TSS into mouse and mouse TSS into human. '''[complete]'''
* Assign orthology/equivalence relationships between projections. '''[complete]'''
* Identify which types of TSS are turning over (being gained and lost) and which are conserved. '''[in progress eta: 20th July 2012]'''
* Look for themes in the gain/loss of TSS (insertion, deletion, transposon activity). '''[in progress eta: 20th July 2012]'''
* Project annotation between species. '''[complete]'''

==Files==
* Data files (BEDs where possible) can be found at the [https://fantom5-collaboration.gsc.riken.jp/webdav/home/taylor/ WEBDAV area]
{| style="color:#000000; background-color:#ffffff;" cellpadding="10" cellspacing="0" border="1"
|'''File'''
|'''Description'''
|-
|'''hg19_projectedMousePermissiveTSS.bed.gz'''
|Mouse TSS projected into human. Red forward strand, blue reverse. hg19 coordinates.
|-
|'''mm9_projectedHumanPermissiveTSS.bed.gz'''
|Human TSS projected into mouse. Red forward strand, blue reverse. mm9 coordinates.
|-
|'''hg19_mm9ProjectionOutcome.bed.gz'''
|Records the outcome of projecting human TSS into mouse. hg19 coordinates. See table below for 12 classifications of outcome.
|-
|'''mm9_hg19ProjectionOutcome.bed.gz'''
|Records the outcome of projecting mouse TSS into human. mm9 coordinates. See table below for 12 classifications of outcome.
|-
|'''mouse_human_oneToOneOrthologyTSS.txt.gz'''
|Two column file giving 1:1 correspondence between mouse and human TSS orthologue pairs.
|}
*Additional files are given for both mouse and human TSS projected into other species.
** File names are underscore (_) delimited. The first part, e.g. mm9, denotes the genome the coordinates correspond to. The second part denotes what has been projected. E.g. mm9_projectedHumanPermissiveTSS indicates the human permissive set of TSS were projected into the mm9 mouse reference genome assembly.
===ProjectionOutcome files===
* These files contain all permissive (thus also robust) TSS defined in the FANTOM5 freeze 1.1. They are partitioned into twelve color-coded categories base on both the projection and expression properties of the TSS.
* Projections between species fall into one of four categories:
# '''Unaligned''' - TSS location is not aligned with any sequence from the target species. This does not discriminate between genomic gain or loss and technical problems (alignment error, genome assembly error, lack of read coverage in raw genomic sequence).
# '''Gap''' - TSS location projects into an alignment gap in the target species. This indicates the gain or loss of sequence over evolution.
# '''Aligned''' - An orthologous sequence for the TSS can be found in the target genome, but there is not a FANTOM5 defined TSS at that position in the target genome. These include both the '''aligned''' and '''peakGap''' categories of projection discussed in the details below.
# '''Orthologous''' - An orthologous sequence for the TSS can be found in the target genome and there '''is''' a FANTOM5 TSS defined at that position.
* Additionally, three expression categories have also been considered:
#'''non-robust''' - the permissive but not robust FANTOM5 TSS (maxcounts>=3 in any one library).
#'''robust''' - TSS classified as robust under FANTOM5 (maxcounts>=11 and maxTPM>=1) in any one library).
#'''robust equiv''' - TSS meeting the robust criteria, but additionally exhibiting maxTPM>=3 in at least one of the CAGE libraries defined by Al as either approximately or exactly equivalent between mouse and human.
* With four projection criteria and three expression criteria that gives a total of 12 categories.
{| style="color:#000000; background-color:#ffffff;" cellpadding="10" cellspacing="0" border="1"
|'''Projection'''
|'''Expression'''
|'''Color-code (RGB)'''
|'''Color'''
|-
|Orthologous
|robust-equiv
|2,112,51
|Dark green
|-
|
|robust
|89,155,109
|Green
|-
|
|non-robust
|163,211,161
|Light Green
|-
|Aligned
|robust-equiv
|16,82,156
|Dark blue
|-
|
|robust
|65,146,201
|Blue
|-
|
|non-robust
|168,201,246
|Light blue
|-
|Gap
|robust-equiv
|156,52,15
|Dark orange
|-
|
|robust
|230,118,54
|Orange
|-
|
|non-robust
|253,196,89
|Light orange
|-
|Unaligned
|robust-equiv
|80,80,80
|Dark grey
|-
|
|robust
|130,130,130
|Grey
|-
|
|non-robust
|180,180,180
|Light grey
|}

==Alignments==
* We have used Ensembl's EPO alignments [http://www.ncbi.nlm.nih.gov/pubmed/18849524 pmid:18849524] [http://www.ncbi.nlm.nih.gov/pubmed/19033362 pmid:19033362].
* They have several advantages over the widely used UCSC nets and chains:
** EPO are true multi-sequence alignments so gap placement is optimised relative to all aligned species, not the case in the stacked pairwise nets-chains.
** EPO uses wider synteny data and local sequence inferred phylogenetic trees to resolve ambiguities in matching segment placement, for example non-ortholgous processed pseudogenes often align as a top-level chain segment in UCSC nets. These are usually accurately resolved in EPO.
** Unlike nets-chains there is no master reference sequence in EPO, so a sequence deleted in human can still align between dog and rat.
** As EPO is true multi-sequence alignment there is much better circular consistency in coordinate projection. E.g. project human->dog->mouse->human is likely (but because of alignment gaps not guaranteed) to give you back the original human coordinate and it will be in the same locality. With nets-chains you could end up on a different chromosome.
* We have used the EPO12 alignments (eutherian mammals: <code>homo_sapiens pan_troglodytes gorilla_gorilla pongo_abelii macaca_mulatta callithrix_jacchus mus_musculus rattus_norvegicus bos_taurus sus_scrofa canis_familiaris equus_caballus</code>) from the Ensembl 67 release. Although all these species can be projected into, only species for which CAGE data has been generated are summarised here and included in the linked files.
* Used the Ensembl 67 API version for interaction with the data.

==Coordinate projection==
* For a human (or mouse) TSS, the boundaries of the TSS were used to define an "alignment slice" of the EPO12 alignments, using the alignSlice functions to resolve overlapping alignment blocks and to orient and order alignment blocks relative to the human (or mouse) genome.
* The reference position of the TSS (BED thick line) was projected through the alignment slice to obtain a projected reference position.
* In cases where the projected reference falls in an alignment gap, the reference is projected onto the nucleotide at the closest edge of the gap, but still within the alignment slice. These are recorded as '''peakGap''' alignments.
* In cases where the alignment slice is entirely gap this is recorded and no coordinate projection made. A '''GAP''' alignment indicates that the TSS position has been deleted/inserted during genome evolution.
**An option to add an arbitrary additional window around the alignment slice is implemented that could allow for the mapping recovery of additional TSS but that option was not used in the data and results presented here.
* In the cases where the alignment slice cannot be projected into a genome at all, i.e. there is no syntenic interval that aligns across the interval, this is recorded as '''unaligned'''. Here we don't have evidence to discriminate the evolutionary gain or loss of sequence from technical difficulties such as alignment or genome assembly problems or the absence of read coverage in the raw genomic sequence.
* The outer margins of the TSS interval were mapped into the aligned sequences, requiring that they map into the same chromosomal locus as the projected reference position (+-80nt). In cases of genomic rearrangement between species, the projected interval was trimmed down to the boundary of the rearrangement.
===hg19 projection===
* Fantom5 cluster-freeze 1.1
* Initial file is tc.decompose_smoothing_merged.ctssMaxCounts3.clustername_update.bed
* Starting with 1,048,124 permissive TSS (includes robust TSS). Requires minimum of 3 tags to have been seen in a single library for cluster to be reported.
* Projected into other species using epoProject code (above) using the EPO 12 eutherian mammals alignments from Ensembl version 67.
** 1,041,433 (99.4%) of TSS could be processed.
** 6,691 (0.6%) of TSS could not be processed as they are in regions of high identity segmental duplication.
{| style="color:#000000; background-color:#ffffff;" cellpadding="10" cellspacing="0" border="1"
|'''Org'''
|'''aligned'''
|'''peakGap'''
|'''GAP'''
|'''unaligned'''
|-
|'''macaca_mulatta'''
|792676
|21223
|43522
|184012
|-
|'''mus_musculus'''
|523343
|42490
|180573
|295027
|-
|'''rattus_norvegicus'''
|485882
|40230
|174998
|340323
|-
|'''canis_familiaris'''
|624391
|38445
|175930
|202667
|-
|}

===mm9 projection===
* Started with 652,860 mouse permissive TSS (same as human criteria, smaller set of libraries, poorly matched).
* 649,178 (99.4%) were processed
* 3,682 (0.6%) could not be processed (high identity segmental duplication regions).
{| style="color:#000000; background-color:#ffffff;" cellpadding="10" cellspacing="0" border="1"
|'''Org'''
|'''aligned'''
|'''peakGap'''
|'''GAP'''
|'''unaligned'''
|-
|'''macaca_mulata'''
|336966
|20051
|96068
|196093
|-
|'''homo_sapiens'''
|358765
|20828
|94926
|174659
|-
|'''rattus_norvegicus'''
|480037
|19093
|40966
|109082
|-
|'''canis_familiaris'''
|324064
|25609
|120553
|178952
|-
|}
'''See the analysis section for a breakdown of what projects and what does not'''

==Assignment of orthology/equivalence==
* TSS projected from mouse into human were progressively assigned to a human TSS.

** If the real human and projected mouse TSS intervals overlapped, the human TSS was assigned to the mouse TSS with the closest projected reference position to the real reference position (so a real TSS can only be involved in one mapping, a projected on could be involved in multiple).
** Where a real TSS has no overlapping projected interval, the distance between closest intervals is used in the same manner: only one mapping for a real TSS but possibly more for projected. An upper limit constraint of 20nt was applied distance between real and projected intervals.
* An identical procedure was then performed for TSS projected from human into mouse.
* The final set of 1:1 orthology/equivalence mapping was obtained by identifying the reciprocal human-into-mouse and mouse-into-human mappings.
* It was clear from this orthology/equivalence assignment procedure that the human TSS are more fragmentary than the mouse (#numbers). This suggests that for cross-species comparison of gene expression measures, some "clusters" of TSS will need to be grouped into merged TSS, particularly in the human data. These groupings can be obtained from the one-to-many relationships (real to projected) in the orthology/equivalence assignment.
===Summary of orthology/equivalence assignment===
* There are 119,653 1:1 human:mouse projected orthologous TSS relationships.

==Summary stats==
* Summary numbers on TSS conservation and turnover [[media:projection_tables.pdf]].

==Analysis==

Cross-species projection of TSS

2012-07-17T16:36:59Z

Martin: Created page with '==Contacts== *martin.taylor@igmm.ed.ac.uk ==Objectives== * Project (liftOver) human TSS into mouse and mouse TSS into human. '''[complete]''' * Assign orthology/equivalence rela…'

Promoterome paper

2012-07-17T16:22:02Z

Martin: /* Tasks: The Promoterome */

This page is to start filling in the structure of the promoterome paper.

 

== Results: ==

=== The Promoterome ===

*'''Table 1''': The dataset. Species, Number of tags, libraries, tissue, cell line, primary cells, number of clusters, number of clusters at 10 tag count threshold, Gene model enrichment ('''Kawaji''')
*'''Figure 1''': Tag clustering/Promoter decomposition figure (perhaps show B4GALT1 example) This should show how DPI works, and an example showing a composite promoter architecture.('''Kawaji/Al''') 

Methodology for promoter definition 

Explanation of the clustering method and decomposition ('''Kawaji''') 

General features of the promoters.('''Piero to lead''') 

Enrichment of general sequence motifs, other features, Epigenetics, Bidirectionality, small promoter associated RNAs ('''Sandelin/Lenhard others... groups''') 

Repeat elements ('''Faulkner/OSC''')

Conservation of promoters for matched mouse-human pairs ('''Taylor/Semple groups + Lukasz Huminiecki''') 

[https://fantom5-collaboration.gsc.riken.jp/wiki/index.php/Promoterome_paper#Tasks:_The_Promoterome Tasks] 

 

=== Tissue/cell type specific expression ===

*'''Figure 2''': More than half of our genes demonstrate tissue dependent regulation (Al - max/median plot for the ~180,000 robust tag clusters) 
*in supps show, known genes, CpG/TATA, gene class specific plots (Al) 

More than half of the genes detected above 10 TPM in at least one sample are not detected in 50% of the collection. Housekeeping like genes (including canonical genes like ACTB HPRT and GAPDH) that are broadly expressed across the collection still show some level of tissue specificity, which cannot be explained by sampling depth or gene amplifications. Transcription factors, lncRNAs and other gene classes have both housekeeper and tissue specific 

Known gene coverage in the collection. 22,789 human gencode genes with greater than 10 tags per million are detected. Primary cells, Cell lines and Tissues are required to recover this. On average primary cells, cell lines and tissues express X, Y and Z genes above 10 Tags per million. (supplementary fig 1 showing venn diagram overlap). Genes that we miss include tissue specific expression from rare/difficult to obtain cell populations absent from our collection.

Improvement of gene models 5' end comparison to Gencode (Timo)

Missing genes due to mapping (xxx)
Missing genes due to sample (Al)

Non-coding promoters

The collection also captures

Call out to lncRNA paper, enhancer paper [[media:Andersson_F5_telecon_dec_22_2011.pdf]] and miRNA promoter paper 

Tissue specificity and multicellularity 

[https://fantom5-collaboration.gsc.riken.jp/wiki/index.php/Promoterome_paper#Tasks:_Tissue.2Fcell_type_specific_expression Tasks] 

Is there a trend for young genes (recent gene duplicates) to be tissue-specific in expression domain? If so, would it be worthwhile to look for exceptions to this trend, for example ancient genes which have narrow expression domain (Lukasz Huminiecki) 

 

=== Transcription factors and motifs defining cellular states ===

Of an estimated X,000 transcription factors, Y,000 are detected in the collection above 10TPM. The remaining TFs are highly duplicated pseudogenes and XXX TFs expressed in early developmental samples not covered in our collection. TFs display both housekeeper and tissue specific modes. They are also more likely to have multiple promoters (or consist of composite promoters) than other loci…? Comment about paralog expression: expression pattern divergence between duplicated TFs (Lukasz Huminiecki) 

*'''Figure 3a:''' Heatmap of motifs observed across the cellular states 
*'''Figure 3b:''' Heatmap of TF expression across the cellular states 
*'''Figure 3c:''' Combined heatmap showing enrichment of TF<>motif pairs in each cellular state 
*               (metric undecided - i) TF expression correlation to Motif activity 
*                                           ii) TF expression correlation to simple expression weighted motif observance 
*                                           iii) TF expression 
*'''Figure 3d?''': Cell tree (Win) or Biolayout (Tom) 

[https://fantom5-collaboration.gsc.riken.jp/wiki/index.php/Promoterome_paper#Tasks:_The_Promoterome Tasks] 

=== Inference of transcriptional regulatory networks from FANTOM5 ===

The network part is the least clear. Below I have put two possible ways to demonstrate utility. If you can generate networks from the phase1 data and can suggest a figure/or figures to demonstrate utility please nominate.

From this study we predict the key transcription factors for each cellular state. We demonstrate we have the right factors by 

'''option1: '''literature validations of key factors - eg. can we confirm we have the top factors for a.. 

*CD14+ monocyte 
*inner ear hair cell 
*hepatocyte 
*astrocyte 

Checking literature for TF name and cell type. Checking OMIM and MGI phenotypes for support that factor is important in that cell type/organ.

 

'''option2:''' take the insulin network as an example to demonstrate utility by KD of top ranked TFs and showing connection 

'''Figure 4: '''Network figure XXX [https://fantom5-collaboration.gsc.riken.jp/wiki/index.php/Promoterome_paper#Tasks:_Inference_of_transcriptional_regulatory_networks_from_FANTOM5 Tasks] 

=== Implications for disease/evolution/robustness/multicellularity ===

From this study ..

GWAS (Kenny)

Expression of disease genes (Al)

Expression of genes that have appeared since multicellularity (Al) [https://fantom5-collaboration.gsc.riken.jp/wiki/index.php/Promoterome_paper#Tasks:_Implications_for_disease.2Fevolution.2Frobustness.2Fmulticellularity Tasks] 

= Tasks and dependencies =

== Tasks: The Promoterome ==

*<strike>QC bad libraries (Al)</strike> '''989 human libraries in FREEZE1''' 
*<strike>QC sample swapping (Al, would like help from WP4/5)</strike> 
*<strike>Finalise thresholding for 'reliable clusters'(Piotr/Erik/Kawaji/Al)</strike> '''Permissive (>2tags in one library, ~1million) and Robust (1TPM and 10 tags in at least one sample ~180K) sets decided''' 
*<strike>Finalise normalization (Piotr/Kawaji/Yishai/Marco/Cesare)</strike> '''RLE was chosen''' 

*Human and mouse clusters and expression tables for FREEZE1 (Kawaji/Marina)
*Complete annotation of Permissive cluster sets (Kawaji/Nicolas) 
*Liftover of mouse clusters to human (and human to mouse Martin Taylor) AND table of mouse<>human cluster homologs [[Cross-species projection of TSS]]

*Text on clustering method (Kawaji) 
*Figure 1 (Kawaji/Al) 

 

== Tasks: Tissue/cell type specific expression ==

*Number of lncRNA promoters hit (Leonard/Max)
*Number of miRNA promoters hit (Eivind/Kawaji)
*Number of putative eRNAs hit (Robin)
*Known genes missed because of duplication (Lukasz Huminiecki)
*Known genes missed because of rare sample (Al) 

 

== Tasks: Transcription factors and motifs defining cellular states ==

'''TFs''' 

*Finalise transcription factor collection - must annotate to cluster!! (Ulf/Al others) 
*Build heatmap and/or biolayout clusters or above TF set (xxx/Tom) 

 

'''Motifs''' 

*Definition of background and target sets for sample specific ab-initio motif finding (Al defined HERE) 
*Starting point will be the thresholded set (either 10 tags in one library or 1.63 TPM.. in progress) 

*'''Comparison 1 - sample specific Motifs''' 

Target set 1. => 10fold enriched (TPM signal in sample +1)/(TPM median signal +1)  ------ There is one target set for EVERY sample!!! 

Background set 1a. => all clusters with TPM above 10 TPM 

Background set 1b. => all clusters with TPM above 10 TPM '''AND''' 10fold enriched (TPM signal in sample +1)/(TPM median signal +1) in at least one sample 

 

*'''Comparison 2 - high and weakly expressed houskeeper motifs''' 

Housekeeping genes are defined by their expression thusly: (log10(maxTPM+1) - log10(medianTPM+1) <=1 '''AND''' log10(medianTPM+1) > 0.5 = 5757 

Target set 2a. => 'Housekeeping genes' with median TPM >100TPM 

Target set 2b. => 'Housekeeping genes' with median TPM>10 and <50 

Background set 2 => all house keeping genes 

 

*Test motif finding in proximal regions 
*Test motif finding in distal regions 

 

-> outcome will be lists of motifs enriched in each sample for comparison 

-> next step is how to compare these into a less redundant set for displaying motif observance/enrichment across the tissue panel. 

 

*Metric for tissue/cell type specificity/activity profile across the collection 
*This is needed for comparing motifs 
*A continuous value would be better for comparing to TF expression, but open to any metric that can be used consistently for comparison to TF expression

 

Web-based repository proposed by Michael Rehli: (cut and pasted from Yulia's email) First: three sections: Primary, time-courses, cancer (cell lines) Should contain: 

*De novo derived motifs (including matrix, annotation and threshold) + best matches from the combined JASPAR/HOMER-ChIP-derived/SwissRegulon/KAUST/HOCOMOCO PWM data base + enrichment in CAGE-data (total clusters as well as promoterome);
*For every sample;
*Special sets: ‚Ubiquitous genes’; Reduced ‚Family Set’

For every Motif: 

*provide best matches from the combined JASPAR/HOMER-ChIP-derived/SwissRegulon/KAUST/HOCOMOCO PWM data base
*enrichment in CAGE-data (total clusters as well as promoterome, p-values);
*motif activity across all samples

Results (whatever type of analysis we can do; motif co-occurences, motif activity, correlation between motif activity and TF expression, Timo’s analysis, etc.)

Functionality (as much as possible): 

*Should be searchable (factor/sample) + contain an index.
*Should be able to select motifs for viewing in UCSC browser or ZENBU (e.g. generate bigWig and load it into Genome Browser and whatever is needed for ZENBU)

 

*Samples could be linked to the cell ontology database? 

 

== Tasks: Inference of transcriptional regulatory networks from FANTOM5 ==

*Mara on all samples to point of core network figure for each sample (Michiel/Piotr) 

*Finish KD on top 40 TFs in Insulin network (Al/Ishizu)
*Matrix siRNA qRTPCR of top 40 TFs in Insulin network + 10 TFs NOT enriched but expressed (Al/Ishizu)
*KD-CAGE on 10 enriched and 10 non-enriched TFs (Al/Ishizu)
*Draw pertubation based network (Al)
*miRNA promoters and miRNA gene vs miRNA target expression (Lukasz Huminiecki)

 

== Tasks: Implications for disease/evolution/robustness/multicellularity ==

*Call out to GWAS paper (Kenny)
*Tissue specific expression of disease genes (Al/Jess)
*Evolution and multicellularity (Al and Lukasz Huminiecki and XXX)
*Expression domain of genes that have appeared since multicellularity (Lukasz Huminiecki)
*Expression domain of the components of the animal developmental toolkit, e.g. the TGFbeta pathway (Lukasz Huminiecki)

Satellite submission

2012-05-23T12:34:16Z

Martin: /* Manuscripts */

Satellite papers

2011-08-03T10:20:21Z

Martin: /* Tfbs turnover in liver (integrate with ChIP seq) Martin */

= Instructions =

Below you can find the list of satellite paper proposals collected in the February meeting. Please add the following information to each of the proposals

*Check the title
*provide brief outline of the proposal
*add/remove your name in case you are interested to work on this satellite paper

Proposal for satellites papers 2/25/2011 Purpose: list up potential satellites; avoid redundancies, make better papers Figure out potential titles to discuss how to negotiate with specific journals.

Add a set of sentences (mini abstract) on the wiki and write an abstract

= Bioinformatics analysis methods =

== Normalization and clustering issues ==

*Outline: bla
*Group members: Tom Freeman

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Modulation of gene expression (Jess Mar) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Expanding transcriptional reg. networks (Vlad) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tag clustering in helicos CAGE (Cesare) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Computational methods for networks comparisons (cesare) ==

*Outline: bla
*Group members: FBK (C. Furlanello, G: Jurman, ...) 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tool to make the promoter subsets at will (do not ask us datasets!) (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Delve tag mapping paper (Timo L) ==

*Outline: Methods paper on Delve: a probabilistic read mapper.
*Group members: Timo Lassmann, Carsten Daub

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Classification of CAGE peaks (Timo L) ==

*Outline: Deeply sequenced CAGE libraries capture signals on many non-promoter regions. The purpose of this paper is to describe a strategy to separate TSS from non-TSS CAGE peaks (see: [[Media:CAGE_classification.pdf]]). Preliminary work suggest that further sub-classicifation of promoters based on the shape of the CAGE signal is possible (see: [[Media:Brood_october_2010.pdf]]).
*Group members: Timo Lassmann, Ben Brown, Colin Semple

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Peak finder-noise elimination contest paper (all runners) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Genomics-broad scale analysis =

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Alternative TSS in cancer relevant to biomarkers (Win Hide) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Impact of alternative promoters on biology of genes (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== How much do we need to sequence? Complexity of the transcriptome (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Revised analysis of zinc finger proteins (Tim Ravasi, David Hume) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification of distal regulation elements: role of enhancers in differentiation (Carsten, Boris, Ana P, Jose, YH) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== miRNA promoters (Hideya K, Eivind Al, ) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== CAGE tags on Pigs: Gain and loss of promoters (David Hume) [satellite of the pig genome] ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulatory transcription outside canonical promoters (Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Transcription initiation in embryo development (Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Promoters with multiple TSS configuration-multiple ways to use the same promoters (Boris, Kawaji) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Link Fantom 5 to genetic datasets (Peter Heutink; Juha K) ==

*Outline: Genome Wide Association Studies (GWAS) have been very succesfull in identifying new risk loci for multifactorial human disease. It has however been very difficult to identify the true biologically relevant variant. GWAS studies in general do not directly test the unknown causal variant but a variant that is in Linkage Disequilibrium with the causal variant. Studies to identify the causal variants are complicated by the observation that most signals from GWAS studies point to non-coding regions of the genome for which the functions are currently unknown. The dataset generated by FANTOM5 now allows to investigate the regions around the association signal for functional elements involved in transcription.

Research method: We have developed a statistical method to delineate the critical region for GWAS loci (Bochdanovits et al. Submitted). We aim to use this method on all publically available GWAS datasets in order to obtain the boundaries of identified GWAS loci. We will then superinpose these genomic region on FANTOM5 data from relevant tissues/celltypes for the disease and identify possible promoters. By using data from the 1000 Genomes project we will investigate if genomic variation exists in the identified promoters. These variants can then be tested for functional effects in cellular reporter assays.

*Group members: Peter Heutink, Juha Kere, Zoltan Bochdanovits and ......please sign up if you are interested. 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== DNA methylation affects TF binding and transcription (Yulia) ==

*Outline: It's commonly accepted that DNA methylation of a promoter repress transcription of this gene in normal tissues. Recently, a class of actively expressed genes having relatively methylated promoters has been discovered. The purpose of this research is to explore the idea that DNA methylation affects CG-rich TFBS, preventing some TF from binding to DNA, and therefore represses transcription.

*Details: [[DNA_methylation_and_transcription]]

*Group members: Yulia Medvedeva

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Comparison of different types/feature of promoters and genome features to study specific differences (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Multiple genomics analysis on multiple datasets (Haru) Extension of the validation? ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Prediction of cell transformation states (Win Hide) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Convergent evolution of retrotransposon promoters (Geoff) ==

*Outline: Following the model for the anti-apoptosis gene NAIP (Romanish et al., PLoS Genetics, 2007), we will start by screening the mouse and human genomes for instances where two different retrotransposons occupy the same or similar location in protein-coding genes (e.g. an Alu in human, a B2 in mouse). If this happens frequently enough to be interesting, we will overlay the F5 data onto the "convergent" retrotransposons to see how many are transcribed, what role they may have in regulation (e.g. Lunyak et al., Science, 2007) and if the events are more common for some pathways than others (e.g. in embryogenesis or brain development). 
*Group members: Geoff, Piero 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Alternative TSS and alternative splicing (Nicolas) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Chimaeric RNA and 3D structure (if it works) (Nicolas) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Annotation of genes involved in biochemical, metabolic processes and signature for processes-for instance signature for tumors – expression based GO terms (Tom Freeman) (Richard Baldarelli, Jackson and GO groups) (David Hume) ==

*Outline: bla
*Group members: xxx, yyy, zzz

 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Blood group: fill in the holes, more discussion =

== Granulopoiesis analysis (Andreas Lenn.+Erik Arner) ==

*Outline:
*Group members: Andreas Lennartsson, Erik Arner

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Erythropoiesis (Peter K) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== HSC (Sugiyama san) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Macrophages (DH) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Subpopulations T cells and monocytes (Michael R) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Brain groups 4 papers Other priority areas in brain: discuss other brain and diseases (YH) =

== Evolution gene expression in vertebrates Martin + Peter Heutink ==

*Outline: Study the evolution of gene expression combining insights from each of the following:
**Gene/transcript level changes in expression (and estimating it's constraint/diversification).
**TSS/promoter turnover: orthologous genes using non-orthologous promoters, or changes in promoter-preference for one cell type between species.
**Sequence evolution of core promoters and distant regulatory blocks correlated with changes in gene expression.
*Focus of the paper on the well matched cells between ((Human, (Macaque?)),(Mouse, Rat),Dog),Chicken) for which we have hCAGE data. (Cell types: Hepatocytes, Aortic smooth muscle cells, mesenchymal stem cells).
*Group members: Martin Taylor, Peter Heutink, Alison Meynert
*Details: [[Evolution in gene expression]].

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Nature Genetics / Genome Research / Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Jan 2012'''

== Transcriptional constrains seq evolution [Martin+Michiel talk] ==

*Outline: [Michiel:] Network analysis across organisms & evolution of regulatory networks, in particular of developmental networks. Are there any subnetworks particularly conserved between organisms? What does this tell us about the functional importance and relevance of specific subnetworks? Do we see any recurring patterns in the network (Uri Alon-type feed-forward loops)? What are the conservation patterns and rates of divergence of transcription factors and specific regulatory relations? Do we see turnover of TFBSs, or do we see conservation of TFBSs in alignments? This can be applied specifically to brain, or more generally to all CAGE samples. TFBS prediction in Neanderthal compared to Homo sapiens would be really cool.
*Group members: Martin, Michiel, Peter Heutink
*Martin's and Michiel's idea for this paper may overlap or may be complementary to each other; we need to discuss this. This may end up as two satellite papers or one integrated one.

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Genome Research / Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Jan 2012'''

== Tfbs turnover in liver (integrate with ChIP seq) Martin ==

*Outline: Integration of cross-species ChIP-seq data in liver with proximal CAGE tag cluster responses in the same species. Data on liver ChIP-seq for the transcription factors HNF1A and CEBPA in human/mouse/dog/(chicken) Schmidt et al, Science 2010 has been obtained. The questions we can address with this study are:
**Are conserved binding sites more likely than non-conserved sites to elicit a local, hepatocyte specific ranscriptional response? (Use CEBPA non-expressing cells to generate a background model of proximal transcriptional responses). This could be used to estimate "functional turnover" as opposed to the "binding turnover" as reported by Duncan Odom.
**Does hepatocyte specific expression (around binding sites) segregate through species lineages with the experimentally defined binding site?
**If there is apparent turn-over of binding sites, is the pattern of local responsive transcription conserved?
**Do we see conservation of hepatocyte specific transcriptional responses even in the absence of binding site conservation?
*Group members: Martin Taylor, Alison Meynert

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Genome Research / Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Dec 2011'''

== Disease paper (brain): human post mortem, … comparison healthy-disease Peter Heutink + Gustincich group ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Rett syndrome and visual cortex Alka ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Genomic architecture in 3 genes involved in Rett syndrome Alka ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Genomic architecture of neurodegenerative disease (Gustincich talk P.H., etc.) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Others =

== [[Olfactory receptors]] ==

*Outline: Promoters of [[Olfactory receptors]] (ORs) are still poorly documented. We have an unpublished promoter list for mouse, and CAGE libraries from human olfactory mucosa will be made. We will identify the promoters of the human ORs and analyse their structure. Many ORs have [[Alternative Promoters|alternative promoters]] and this is a potential example for the promotorome paper. Human-specific OR promoters might be found. There is evidence of expression of the ORs outside the mouse and human olfactory mucosa, and this satellite paper will report this. Experiments to find a ligand and propose a function may be carried out. More information on the page: [[Olfactory receptors]].
*Group members: [[User:Plessy|Charles Plessy]], Giovanni Pascarella, Stefano Gustincich and others, but I am too shy to add their name without asking.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cell-Cell communicatome (Al forrest) ==

*Outline: We have possibly the broadest expression map to date. The idea behind this paper will look at the expression of cell surface receptors and ligands (secreted and membrane bound) in the primary cell collection to examine which cells can communicate with which other cells. The receptor-ligand pairing will rely on published interactions. This will build a network where primary cell types are the nodes and receptor-ligand interactions are the edges.

*Group members: none assigned yet

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article: '''Genome Research, Molecular Systems Biology, Genome Biology'''

3. Estimated month of submission: '''month/year'''

== Drugable cells: drug targets (Al Forrest) ==

*Outline: We have possibly the broadest expression map to date. The idea behind this paper will look at the cell(and tissue) restriction of known drug targets. Given that we generally want to target one cell type (cancer) or organ, what does this expression profile tell us about undesired side effects due to the drug affecting other cells expressing the drug target.

*Group members: none assigned yet

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''Genome Research, Genome Biology'''

3. Estimated month of submission: '''month/year'''

== Definition of stem or precursors relationship (Claudio Schneider) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Gene regulation in cells of connective tissues (Vlad, Kim) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Transdifferentiation and network rewiring (Haru; WP6 + others) POTENTIAL main paper for later stage ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Network in cancer (Rama, win’s group) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulatory network in cell lineage tree (Carsten wp5) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Determination of conserved CAGE (Vlad) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Promoting human uniqueness: human-specific promoters of regulatory lncRNA genes drive cis- and trans-regulation. (LL) ==

*some BACKGROUND on our past work and F5 plans here: [[Media:FANTOM5-LL.ppt]] 
*More information, anticipated Abstract, Definitions, Plan of Work at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] 
*Outline: In FANTOM3, we described complex loci -- sense-antisense pairs [[Media:F5_human_sense-antisense_pairs_hg19.zip]], bidirectional promoters, and gene chains -- prevalent in mammalian genomes. These complex loci often contain long non-coding RNA (lncRNA) genes. Please see [[Media:F5_human_lncRNAome%28Jia%26Lipovich_Gencode_Lander%29.xls]] for our complete reference list of human lncRNA genes, the human lncRNAome; and [[Media:F5_human_lncRNAome(Jia%26Lipovich_Gencode)BED.zip]] for our BED file of the Gencode and our Jia et al lncRNAs. LncRNA Genes are often not conserved between mouse and human. Now in FANTOM5, our goal is to functionally characterize the specific contribution of non-conserved sequences in human, particularly promoters of lncRNA genes, to gene regulation at complex loci. We will reach this goal by:  

#identifying all "human-specific" (definition = primate-specific; thus absent in the F5 nonhuman species) promoters in CAGE and CAGEscan data. 
#using F5 Cluster Annotation results to find all lncRNA genes whose promoters are human-specific. 
#determining which lncRNA genes with human-specific promoters are in complex loci, as defined in the first sentence of this Outline. 
#testing each complex locus from #4 for the existence of a unique cis-regulatory expression signature (simple e.g.: all genes in the complex locus are on, all off, or some on and specific others off) that corresponds to a specific cell type, tissue type, or steady state. Signatures are defined both by an expression pattern and by an adjacency, overlap, and specific order / orientation of the co-expressed genes neighboring along the genome. 
#determining whether, and how, each complex-locus steady-state-specific expression signature is dependent upon the human-specific promoter of the lncRNA within that signature. (Implementation details are at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] )  
#identifying lncRNA genes whose human-specific promoters have evidence of recent functional constraint or recent positive selection. 
#performing, for lncRNAs of exceptional interest based on #5 and #6, reverse-genetic experiments in cell culture to validate whether the human-specific promoter of the lncRNA really has a regulatory impact that contributes to defining a particular steady state. (Note: we would need the OSC's direct help with wet-lab validations. Let's discuss.)  
#defining the unique functional proteome space (e.g. gene ontologies? positive selection? brain genes? etc) cis-regulated by human-specific lncRNA promoters. 
#finally, deriving a multidimensional unified cis- and trans-regulatory network that describes human-specific and lncRNA-mediated gene regulation in specific cellular states. Nodes (regulatory lncRNAs and transcription factors) will be shared between the cis-regulatory network (regulation based on genomic overlap or adjacency) and the trans-regulatory network (regulation based on TF-target or known lncRNA-TF relationships). (Definition of such a network is at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] ) 

*Group members: Leonard Lipovich, Yulia Medvedeva, Vlad Bajic and lab, Nicolas Bertin ([[User:Nbertin]]), (inviting you to join - please confirm: Jess Mar & colleagues), and I am also too shy to name (or invite) potential others. Please email me or the F5 list, or please just add yourselves to this page, if you would like to join this effort.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Using single direction promoter ti eliminate noise (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Specific transcript (Human) regulation and what are nover TF in human (Haru) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification of TFBS by de novo methods (Vlad; Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Predicted homotypic clusters and motif prediction (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification fo features of primates specific promoters (?; together with LL) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulation specificity of cells and tissues (Vlad’ s group) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Host pathogen infection relationship; influenza virus, Mycobacteria, Salomonella (Arnab) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Searching for viruses, cryptic viruses (Arnab, mamoon, al, nico) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Deorphanizing transcription factors (Vlad) ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Variation in small RNA population and variation in siRNA machinery (Max) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cellular restriction of terminal ligases in ubiquitin system (Max) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Papers of individual cells time courses ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Extend rat gene models with CAGEscan]] ==

*Outline: Rat gene models sometimes lack a proper 5′ UTR. CAGEscan data has been produced using the same RNA (10009-101B8) as the reference FANTOM5 Helicos CAGE library CNhs10612. This experimental data can be used to propose an update of the rat gene models.
*Group members: [[User:Plessy|Charles Plessy]], Albin Sandelin, Mette Jørgensen, Johannes Waage, other people, please list yourself.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Novel metrics for promoter activity profiles]] ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Pathway Fingerprinting]] ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cellular restriction of epigenomic regulation Erik A. + Andreas Lenn. ==

*Outline: We aim to map cell and differentiation specific expression and usage of alternative transcription start sites of chromatin remodellers, histone chaperones and other chromatin modifying enzymes.
*Group members: Erik Arner, Andreas Lennartsson

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Issues: negotiation with sample providers =

== Encouraged to write paper, but larger stronger papers is perhaps better? ==

The above is a tentative list of satellite manuscripts listed at the Ume meeting. Please take the time to fill in a brief description/abstract of the manuscript and read what others are proposing. Consider whether working together on a combined manuscript will generate a stronger (higher impact) manuscript.

== Talk with collaborator before the datasets is published ==

As mentioned at the Ume meeting. We are working together as a consortium which shares samples, analyses and ideas. Please talk to the sample providers and analysts if you are using a particular subset of the data. If you are uncertain about whether a dataset has particular strings attached please check the collaborator column on the Helicos data production schedule file on the front page. Samples labelled as "FANTOM5 OSC core" are largely free for use. If you want to use data generated on samples by a particular collaborator please contact them.

Satellite papers

2011-08-03T10:19:55Z

Martin: /* Tfbs turnover in liver (integrate with ChIP seq) Martin */

= Instructions =

Below you can find the list of satellite paper proposals collected in the February meeting. Please add the following information to each of the proposals

*Check the title
*provide brief outline of the proposal
*add/remove your name in case you are interested to work on this satellite paper

Proposal for satellites papers 2/25/2011 Purpose: list up potential satellites; avoid redundancies, make better papers Figure out potential titles to discuss how to negotiate with specific journals.

Add a set of sentences (mini abstract) on the wiki and write an abstract

= Bioinformatics analysis methods =

== Normalization and clustering issues ==

*Outline: bla
*Group members: Tom Freeman

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Modulation of gene expression (Jess Mar) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Expanding transcriptional reg. networks (Vlad) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tag clustering in helicos CAGE (Cesare) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Computational methods for networks comparisons (cesare) ==

*Outline: bla
*Group members: FBK (C. Furlanello, G: Jurman, ...) 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tool to make the promoter subsets at will (do not ask us datasets!) (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Delve tag mapping paper (Timo L) ==

*Outline: Methods paper on Delve: a probabilistic read mapper.
*Group members: Timo Lassmann, Carsten Daub

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Classification of CAGE peaks (Timo L) ==

*Outline: Deeply sequenced CAGE libraries capture signals on many non-promoter regions. The purpose of this paper is to describe a strategy to separate TSS from non-TSS CAGE peaks (see: [[Media:CAGE_classification.pdf]]). Preliminary work suggest that further sub-classicifation of promoters based on the shape of the CAGE signal is possible (see: [[Media:Brood_october_2010.pdf]]).
*Group members: Timo Lassmann, Ben Brown, Colin Semple

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Peak finder-noise elimination contest paper (all runners) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Genomics-broad scale analysis =

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Alternative TSS in cancer relevant to biomarkers (Win Hide) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Impact of alternative promoters on biology of genes (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== How much do we need to sequence? Complexity of the transcriptome (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Revised analysis of zinc finger proteins (Tim Ravasi, David Hume) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification of distal regulation elements: role of enhancers in differentiation (Carsten, Boris, Ana P, Jose, YH) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== miRNA promoters (Hideya K, Eivind Al, ) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== CAGE tags on Pigs: Gain and loss of promoters (David Hume) [satellite of the pig genome] ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulatory transcription outside canonical promoters (Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Transcription initiation in embryo development (Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Promoters with multiple TSS configuration-multiple ways to use the same promoters (Boris, Kawaji) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Link Fantom 5 to genetic datasets (Peter Heutink; Juha K) ==

*Outline: Genome Wide Association Studies (GWAS) have been very succesfull in identifying new risk loci for multifactorial human disease. It has however been very difficult to identify the true biologically relevant variant. GWAS studies in general do not directly test the unknown causal variant but a variant that is in Linkage Disequilibrium with the causal variant. Studies to identify the causal variants are complicated by the observation that most signals from GWAS studies point to non-coding regions of the genome for which the functions are currently unknown. The dataset generated by FANTOM5 now allows to investigate the regions around the association signal for functional elements involved in transcription.

Research method: We have developed a statistical method to delineate the critical region for GWAS loci (Bochdanovits et al. Submitted). We aim to use this method on all publically available GWAS datasets in order to obtain the boundaries of identified GWAS loci. We will then superinpose these genomic region on FANTOM5 data from relevant tissues/celltypes for the disease and identify possible promoters. By using data from the 1000 Genomes project we will investigate if genomic variation exists in the identified promoters. These variants can then be tested for functional effects in cellular reporter assays.

*Group members: Peter Heutink, Juha Kere, Zoltan Bochdanovits and ......please sign up if you are interested. 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== DNA methylation affects TF binding and transcription (Yulia) ==

*Outline: It's commonly accepted that DNA methylation of a promoter repress transcription of this gene in normal tissues. Recently, a class of actively expressed genes having relatively methylated promoters has been discovered. The purpose of this research is to explore the idea that DNA methylation affects CG-rich TFBS, preventing some TF from binding to DNA, and therefore represses transcription.

*Details: [[DNA_methylation_and_transcription]]

*Group members: Yulia Medvedeva

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Comparison of different types/feature of promoters and genome features to study specific differences (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Multiple genomics analysis on multiple datasets (Haru) Extension of the validation? ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Prediction of cell transformation states (Win Hide) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Convergent evolution of retrotransposon promoters (Geoff) ==

*Outline: Following the model for the anti-apoptosis gene NAIP (Romanish et al., PLoS Genetics, 2007), we will start by screening the mouse and human genomes for instances where two different retrotransposons occupy the same or similar location in protein-coding genes (e.g. an Alu in human, a B2 in mouse). If this happens frequently enough to be interesting, we will overlay the F5 data onto the "convergent" retrotransposons to see how many are transcribed, what role they may have in regulation (e.g. Lunyak et al., Science, 2007) and if the events are more common for some pathways than others (e.g. in embryogenesis or brain development). 
*Group members: Geoff, Piero 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Alternative TSS and alternative splicing (Nicolas) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Chimaeric RNA and 3D structure (if it works) (Nicolas) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Annotation of genes involved in biochemical, metabolic processes and signature for processes-for instance signature for tumors – expression based GO terms (Tom Freeman) (Richard Baldarelli, Jackson and GO groups) (David Hume) ==

*Outline: bla
*Group members: xxx, yyy, zzz

 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Blood group: fill in the holes, more discussion =

== Granulopoiesis analysis (Andreas Lenn.+Erik Arner) ==

*Outline:
*Group members: Andreas Lennartsson, Erik Arner

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Erythropoiesis (Peter K) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== HSC (Sugiyama san) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Macrophages (DH) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Subpopulations T cells and monocytes (Michael R) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Brain groups 4 papers Other priority areas in brain: discuss other brain and diseases (YH) =

== Evolution gene expression in vertebrates Martin + Peter Heutink ==

*Outline: Study the evolution of gene expression combining insights from each of the following:
**Gene/transcript level changes in expression (and estimating it's constraint/diversification).
**TSS/promoter turnover: orthologous genes using non-orthologous promoters, or changes in promoter-preference for one cell type between species.
**Sequence evolution of core promoters and distant regulatory blocks correlated with changes in gene expression.
*Focus of the paper on the well matched cells between ((Human, (Macaque?)),(Mouse, Rat),Dog),Chicken) for which we have hCAGE data. (Cell types: Hepatocytes, Aortic smooth muscle cells, mesenchymal stem cells).
*Group members: Martin Taylor, Peter Heutink, Alison Meynert
*Details: [[Evolution in gene expression]].

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Nature Genetics / Genome Research / Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Jan 2012'''

== Transcriptional constrains seq evolution [Martin+Michiel talk] ==

*Outline: [Michiel:] Network analysis across organisms & evolution of regulatory networks, in particular of developmental networks. Are there any subnetworks particularly conserved between organisms? What does this tell us about the functional importance and relevance of specific subnetworks? Do we see any recurring patterns in the network (Uri Alon-type feed-forward loops)? What are the conservation patterns and rates of divergence of transcription factors and specific regulatory relations? Do we see turnover of TFBSs, or do we see conservation of TFBSs in alignments? This can be applied specifically to brain, or more generally to all CAGE samples. TFBS prediction in Neanderthal compared to Homo sapiens would be really cool.
*Group members: Martin, Michiel, Peter Heutink
*Martin's and Michiel's idea for this paper may overlap or may be complementary to each other; we need to discuss this. This may end up as two satellite papers or one integrated one.

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Genome Research / Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Jan 2012'''

== Tfbs turnover in liver (integrate with ChIP seq) Martin ==

*Outline: Integration of cross-species ChIP-seq data in liver with proximal CAGE tag cluster responses in the same species. Data on liver ChIP-seq for the transcription factors HNF1A and CEBPA in human/mouse/dog/(chicken) Schmidt et al, Science 2010 has been obtained. The questions we can address with this study are:
**Are conserved binding sites more likely than non-conserved sites to elicit a local, hepatocyte specific ranscriptional response? (Use CEBPA non-expressing cells to generate a background model of proximal transcriptional responses). This could be used to estimate "functional turnover" as opposed to the "binding turnover" as reported by Duncan Odom.
**Does hepatocyte specific expression (around binding sites) segregate through species lineages with the experimentally defined binding site?
**If there is apparent turn-over of binding sites, is the pattern of local responsive transcription conserved?
**Do we see conservation of hepatocyte specific transcriptional responses even in the absence of binding site conservation?
*Group members: Martin Taylor, Alison Meynert

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Genome Research /
Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Dec 2011'''

== Disease paper (brain): human post mortem, … comparison healthy-disease Peter Heutink + Gustincich group ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Rett syndrome and visual cortex Alka ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Genomic architecture in 3 genes involved in Rett syndrome Alka ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Genomic architecture of neurodegenerative disease (Gustincich talk P.H., etc.) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Others =

== [[Olfactory receptors]] ==

*Outline: Promoters of [[Olfactory receptors]] (ORs) are still poorly documented. We have an unpublished promoter list for mouse, and CAGE libraries from human olfactory mucosa will be made. We will identify the promoters of the human ORs and analyse their structure. Many ORs have [[Alternative Promoters|alternative promoters]] and this is a potential example for the promotorome paper. Human-specific OR promoters might be found. There is evidence of expression of the ORs outside the mouse and human olfactory mucosa, and this satellite paper will report this. Experiments to find a ligand and propose a function may be carried out. More information on the page: [[Olfactory receptors]].
*Group members: [[User:Plessy|Charles Plessy]], Giovanni Pascarella, Stefano Gustincich and others, but I am too shy to add their name without asking.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cell-Cell communicatome (Al forrest) ==

*Outline: We have possibly the broadest expression map to date. The idea behind this paper will look at the expression of cell surface receptors and ligands (secreted and membrane bound) in the primary cell collection to examine which cells can communicate with which other cells. The receptor-ligand pairing will rely on published interactions. This will build a network where primary cell types are the nodes and receptor-ligand interactions are the edges.

*Group members: none assigned yet

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article: '''Genome Research, Molecular Systems Biology, Genome Biology'''

3. Estimated month of submission: '''month/year'''

== Drugable cells: drug targets (Al Forrest) ==

*Outline: We have possibly the broadest expression map to date. The idea behind this paper will look at the cell(and tissue) restriction of known drug targets. Given that we generally want to target one cell type (cancer) or organ, what does this expression profile tell us about undesired side effects due to the drug affecting other cells expressing the drug target.

*Group members: none assigned yet

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''Genome Research, Genome Biology'''

3. Estimated month of submission: '''month/year'''

== Definition of stem or precursors relationship (Claudio Schneider) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Gene regulation in cells of connective tissues (Vlad, Kim) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Transdifferentiation and network rewiring (Haru; WP6 + others) POTENTIAL main paper for later stage ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Network in cancer (Rama, win’s group) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulatory network in cell lineage tree (Carsten wp5) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Determination of conserved CAGE (Vlad) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Promoting human uniqueness: human-specific promoters of regulatory lncRNA genes drive cis- and trans-regulation. (LL) ==

*some BACKGROUND on our past work and F5 plans here: [[Media:FANTOM5-LL.ppt]] 
*More information, anticipated Abstract, Definitions, Plan of Work at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] 
*Outline: In FANTOM3, we described complex loci -- sense-antisense pairs [[Media:F5_human_sense-antisense_pairs_hg19.zip]], bidirectional promoters, and gene chains -- prevalent in mammalian genomes. These complex loci often contain long non-coding RNA (lncRNA) genes. Please see [[Media:F5_human_lncRNAome%28Jia%26Lipovich_Gencode_Lander%29.xls]] for our complete reference list of human lncRNA genes, the human lncRNAome; and [[Media:F5_human_lncRNAome(Jia%26Lipovich_Gencode)BED.zip]] for our BED file of the Gencode and our Jia et al lncRNAs. LncRNA Genes are often not conserved between mouse and human. Now in FANTOM5, our goal is to functionally characterize the specific contribution of non-conserved sequences in human, particularly promoters of lncRNA genes, to gene regulation at complex loci. We will reach this goal by:  

#identifying all "human-specific" (definition = primate-specific; thus absent in the F5 nonhuman species) promoters in CAGE and CAGEscan data. 
#using F5 Cluster Annotation results to find all lncRNA genes whose promoters are human-specific. 
#determining which lncRNA genes with human-specific promoters are in complex loci, as defined in the first sentence of this Outline. 
#testing each complex locus from #4 for the existence of a unique cis-regulatory expression signature (simple e.g.: all genes in the complex locus are on, all off, or some on and specific others off) that corresponds to a specific cell type, tissue type, or steady state. Signatures are defined both by an expression pattern and by an adjacency, overlap, and specific order / orientation of the co-expressed genes neighboring along the genome. 
#determining whether, and how, each complex-locus steady-state-specific expression signature is dependent upon the human-specific promoter of the lncRNA within that signature. (Implementation details are at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] )  
#identifying lncRNA genes whose human-specific promoters have evidence of recent functional constraint or recent positive selection. 
#performing, for lncRNAs of exceptional interest based on #5 and #6, reverse-genetic experiments in cell culture to validate whether the human-specific promoter of the lncRNA really has a regulatory impact that contributes to defining a particular steady state. (Note: we would need the OSC's direct help with wet-lab validations. Let's discuss.)  
#defining the unique functional proteome space (e.g. gene ontologies? positive selection? brain genes? etc) cis-regulated by human-specific lncRNA promoters. 
#finally, deriving a multidimensional unified cis- and trans-regulatory network that describes human-specific and lncRNA-mediated gene regulation in specific cellular states. Nodes (regulatory lncRNAs and transcription factors) will be shared between the cis-regulatory network (regulation based on genomic overlap or adjacency) and the trans-regulatory network (regulation based on TF-target or known lncRNA-TF relationships). (Definition of such a network is at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] ) 

*Group members: Leonard Lipovich, Yulia Medvedeva, Vlad Bajic and lab, Nicolas Bertin ([[User:Nbertin]]), (inviting you to join - please confirm: Jess Mar & colleagues), and I am also too shy to name (or invite) potential others. Please email me or the F5 list, or please just add yourselves to this page, if you would like to join this effort.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Using single direction promoter ti eliminate noise (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Specific transcript (Human) regulation and what are nover TF in human (Haru) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification of TFBS by de novo methods (Vlad; Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Predicted homotypic clusters and motif prediction (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification fo features of primates specific promoters (?; together with LL) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulation specificity of cells and tissues (Vlad’ s group) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Host pathogen infection relationship; influenza virus, Mycobacteria, Salomonella (Arnab) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Searching for viruses, cryptic viruses (Arnab, mamoon, al, nico) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Deorphanizing transcription factors (Vlad) ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Variation in small RNA population and variation in siRNA machinery (Max) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cellular restriction of terminal ligases in ubiquitin system (Max) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Papers of individual cells time courses ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Extend rat gene models with CAGEscan]] ==

*Outline: Rat gene models sometimes lack a proper 5′ UTR. CAGEscan data has been produced using the same RNA (10009-101B8) as the reference FANTOM5 Helicos CAGE library CNhs10612. This experimental data can be used to propose an update of the rat gene models.
*Group members: [[User:Plessy|Charles Plessy]], Albin Sandelin, Mette Jørgensen, Johannes Waage, other people, please list yourself.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Novel metrics for promoter activity profiles]] ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Pathway Fingerprinting]] ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cellular restriction of epigenomic regulation Erik A. + Andreas Lenn. ==

*Outline: We aim to map cell and differentiation specific expression and usage of alternative transcription start sites of chromatin remodellers, histone chaperones and other chromatin modifying enzymes.
*Group members: Erik Arner, Andreas Lennartsson

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Issues: negotiation with sample providers =

== Encouraged to write paper, but larger stronger papers is perhaps better? ==

The above is a tentative list of satellite manuscripts listed at the Ume meeting. Please take the time to fill in a brief description/abstract of the manuscript and read what others are proposing. Consider whether working together on a combined manuscript will generate a stronger (higher impact) manuscript.

== Talk with collaborator before the datasets is published ==

As mentioned at the Ume meeting. We are working together as a consortium which shares samples, analyses and ideas. Please talk to the sample providers and analysts if you are using a particular subset of the data. If you are uncertain about whether a dataset has particular strings attached please check the collaborator column on the Helicos data production schedule file on the front page. Samples labelled as "FANTOM5 OSC core" are largely free for use. If you want to use data generated on samples by a particular collaborator please contact them.

Satellite papers

2011-08-03T10:17:23Z

Martin: /* Transcriptional constrains seq evolution [Martin+Michiel talk] */

= Instructions =

Below you can find the list of satellite paper proposals collected in the February meeting. Please add the following information to each of the proposals

*Check the title
*provide brief outline of the proposal
*add/remove your name in case you are interested to work on this satellite paper

Proposal for satellites papers 2/25/2011 Purpose: list up potential satellites; avoid redundancies, make better papers Figure out potential titles to discuss how to negotiate with specific journals.

Add a set of sentences (mini abstract) on the wiki and write an abstract

= Bioinformatics analysis methods =

== Normalization and clustering issues ==

*Outline: bla
*Group members: Tom Freeman

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Modulation of gene expression (Jess Mar) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Expanding transcriptional reg. networks (Vlad) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tag clustering in helicos CAGE (Cesare) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Computational methods for networks comparisons (cesare) ==

*Outline: bla
*Group members: FBK (C. Furlanello, G: Jurman, ...) 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tool to make the promoter subsets at will (do not ask us datasets!) (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Delve tag mapping paper (Timo L) ==

*Outline: Methods paper on Delve: a probabilistic read mapper.
*Group members: Timo Lassmann, Carsten Daub

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Classification of CAGE peaks (Timo L) ==

*Outline: Deeply sequenced CAGE libraries capture signals on many non-promoter regions. The purpose of this paper is to describe a strategy to separate TSS from non-TSS CAGE peaks (see: [[Media:CAGE_classification.pdf]]). Preliminary work suggest that further sub-classicifation of promoters based on the shape of the CAGE signal is possible (see: [[Media:Brood_october_2010.pdf]]).
*Group members: Timo Lassmann, Ben Brown, Colin Semple

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Peak finder-noise elimination contest paper (all runners) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Genomics-broad scale analysis =

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Alternative TSS in cancer relevant to biomarkers (Win Hide) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Impact of alternative promoters on biology of genes (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== How much do we need to sequence? Complexity of the transcriptome (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Revised analysis of zinc finger proteins (Tim Ravasi, David Hume) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification of distal regulation elements: role of enhancers in differentiation (Carsten, Boris, Ana P, Jose, YH) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== miRNA promoters (Hideya K, Eivind Al, ) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== CAGE tags on Pigs: Gain and loss of promoters (David Hume) [satellite of the pig genome] ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulatory transcription outside canonical promoters (Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Transcription initiation in embryo development (Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Promoters with multiple TSS configuration-multiple ways to use the same promoters (Boris, Kawaji) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Link Fantom 5 to genetic datasets (Peter Heutink; Juha K) ==

*Outline: Genome Wide Association Studies (GWAS) have been very succesfull in identifying new risk loci for multifactorial human disease. It has however been very difficult to identify the true biologically relevant variant. GWAS studies in general do not directly test the unknown causal variant but a variant that is in Linkage Disequilibrium with the causal variant. Studies to identify the causal variants are complicated by the observation that most signals from GWAS studies point to non-coding regions of the genome for which the functions are currently unknown. The dataset generated by FANTOM5 now allows to investigate the regions around the association signal for functional elements involved in transcription.

Research method: We have developed a statistical method to delineate the critical region for GWAS loci (Bochdanovits et al. Submitted). We aim to use this method on all publically available GWAS datasets in order to obtain the boundaries of identified GWAS loci. We will then superinpose these genomic region on FANTOM5 data from relevant tissues/celltypes for the disease and identify possible promoters. By using data from the 1000 Genomes project we will investigate if genomic variation exists in the identified promoters. These variants can then be tested for functional effects in cellular reporter assays.

*Group members: Peter Heutink, Juha Kere, Zoltan Bochdanovits and ......please sign up if you are interested. 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== DNA methylation affects TF binding and transcription (Yulia) ==

*Outline: It's commonly accepted that DNA methylation of a promoter repress transcription of this gene in normal tissues. Recently, a class of actively expressed genes having relatively methylated promoters has been discovered. The purpose of this research is to explore the idea that DNA methylation affects CG-rich TFBS, preventing some TF from binding to DNA, and therefore represses transcription.

*Details: [[DNA_methylation_and_transcription]]

*Group members: Yulia Medvedeva

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Comparison of different types/feature of promoters and genome features to study specific differences (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Multiple genomics analysis on multiple datasets (Haru) Extension of the validation? ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Prediction of cell transformation states (Win Hide) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Convergent evolution of retrotransposon promoters (Geoff) ==

*Outline: Following the model for the anti-apoptosis gene NAIP (Romanish et al., PLoS Genetics, 2007), we will start by screening the mouse and human genomes for instances where two different retrotransposons occupy the same or similar location in protein-coding genes (e.g. an Alu in human, a B2 in mouse). If this happens frequently enough to be interesting, we will overlay the F5 data onto the "convergent" retrotransposons to see how many are transcribed, what role they may have in regulation (e.g. Lunyak et al., Science, 2007) and if the events are more common for some pathways than others (e.g. in embryogenesis or brain development). 
*Group members: Geoff, Piero 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Alternative TSS and alternative splicing (Nicolas) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Chimaeric RNA and 3D structure (if it works) (Nicolas) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Annotation of genes involved in biochemical, metabolic processes and signature for processes-for instance signature for tumors – expression based GO terms (Tom Freeman) (Richard Baldarelli, Jackson and GO groups) (David Hume) ==

*Outline: bla
*Group members: xxx, yyy, zzz

 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Blood group: fill in the holes, more discussion =

== Granulopoiesis analysis (Andreas Lenn.+Erik Arner) ==

*Outline:
*Group members: Andreas Lennartsson, Erik Arner

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Erythropoiesis (Peter K) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== HSC (Sugiyama san) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Macrophages (DH) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Subpopulations T cells and monocytes (Michael R) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Brain groups 4 papers Other priority areas in brain: discuss other brain and diseases (YH) =

== Evolution gene expression in vertebrates Martin + Peter Heutink ==

*Outline: Study the evolution of gene expression combining insights from each of the following:
**Gene/transcript level changes in expression (and estimating it's constraint/diversification).
**TSS/promoter turnover: orthologous genes using non-orthologous promoters, or changes in promoter-preference for one cell type between species.
**Sequence evolution of core promoters and distant regulatory blocks correlated with changes in gene expression.
*Focus of the paper on the well matched cells between ((Human, (Macaque?)),(Mouse, Rat),Dog),Chicken) for which we have hCAGE data. (Cell types: Hepatocytes, Aortic smooth muscle cells, mesenchymal stem cells).
*Group members: Martin Taylor, Peter Heutink, Alison Meynert
*Details: [[Evolution in gene expression]].

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Nature Genetics / Genome Research / Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Jan 2012'''

== Transcriptional constrains seq evolution [Martin+Michiel talk] ==

*Outline: [Michiel:] Network analysis across organisms & evolution of regulatory networks, in particular of developmental networks. Are there any subnetworks particularly conserved between organisms? What does this tell us about the functional importance and relevance of specific subnetworks? Do we see any recurring patterns in the network (Uri Alon-type feed-forward loops)? What are the conservation patterns and rates of divergence of transcription factors and specific regulatory relations? Do we see turnover of TFBSs, or do we see conservation of TFBSs in alignments? This can be applied specifically to brain, or more generally to all CAGE samples. TFBS prediction in Neanderthal compared to Homo sapiens would be really cool.
*Group members: Martin, Michiel, Peter Heutink
*Martin's and Michiel's idea for this paper may overlap or may be complementary to each other; we need to discuss this. This may end up as two satellite papers or one integrated one.

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Genome Research / Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Jan 2012'''

== Tfbs turnover in liver (integrate with ChIP seq) Martin ==

*Outline: Integration of cross-species ChIP-seq data in liver with proximal CAGE tag cluster responses in the same species. Data on liver ChIP-seq for the transcription factors HNF1A and CEBPA in human/mouse/dog/(chicken) Schmidt et al, Science 2010 has been obtained. The questions we can address with this study are:
**Are conserved binding sites more likely than non-conserved sites to elicit a local, hepatocyte specific ranscriptional response? (Use CEBPA non-expressing cells to generate a background model of proximal transcriptional responses). This could be used to estimate "functional turnover" as opposed to the "binding turnover" as reported by Duncan Odom.
**Does hepatocyte specific expression (around binding sites) segregate through species lineages with the experimentally defined binding site?
**If there is apparent turn-over of binding sites, is the pattern of local responsive transcription conserved?
**Do we see conservation of hepatocyte specific transcriptional responses even in the absence of binding site conservation?
*Group members: Martin Taylor, Alison Meynert

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Disease paper (brain): human post mortem, … comparison healthy-disease Peter Heutink + Gustincich group ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Rett syndrome and visual cortex Alka ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Genomic architecture in 3 genes involved in Rett syndrome Alka ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Genomic architecture of neurodegenerative disease (Gustincich talk P.H., etc.) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Others =

== [[Olfactory receptors]] ==

*Outline: Promoters of [[Olfactory receptors]] (ORs) are still poorly documented. We have an unpublished promoter list for mouse, and CAGE libraries from human olfactory mucosa will be made. We will identify the promoters of the human ORs and analyse their structure. Many ORs have [[Alternative Promoters|alternative promoters]] and this is a potential example for the promotorome paper. Human-specific OR promoters might be found. There is evidence of expression of the ORs outside the mouse and human olfactory mucosa, and this satellite paper will report this. Experiments to find a ligand and propose a function may be carried out. More information on the page: [[Olfactory receptors]].
*Group members: [[User:Plessy|Charles Plessy]], Giovanni Pascarella, Stefano Gustincich and others, but I am too shy to add their name without asking.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cell-Cell communicatome (Al forrest) ==

*Outline: We have possibly the broadest expression map to date. The idea behind this paper will look at the expression of cell surface receptors and ligands (secreted and membrane bound) in the primary cell collection to examine which cells can communicate with which other cells. The receptor-ligand pairing will rely on published interactions. This will build a network where primary cell types are the nodes and receptor-ligand interactions are the edges.

*Group members: none assigned yet

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article: '''Genome Research, Molecular Systems Biology, Genome Biology'''

3. Estimated month of submission: '''month/year'''

== Drugable cells: drug targets (Al Forrest) ==

*Outline: We have possibly the broadest expression map to date. The idea behind this paper will look at the cell(and tissue) restriction of known drug targets. Given that we generally want to target one cell type (cancer) or organ, what does this expression profile tell us about undesired side effects due to the drug affecting other cells expressing the drug target.

*Group members: none assigned yet

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''Genome Research, Genome Biology'''

3. Estimated month of submission: '''month/year'''

== Definition of stem or precursors relationship (Claudio Schneider) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Gene regulation in cells of connective tissues (Vlad, Kim) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Transdifferentiation and network rewiring (Haru; WP6 + others) POTENTIAL main paper for later stage ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Network in cancer (Rama, win’s group) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulatory network in cell lineage tree (Carsten wp5) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Determination of conserved CAGE (Vlad) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Promoting human uniqueness: human-specific promoters of regulatory lncRNA genes drive cis- and trans-regulation. (LL) ==

*some BACKGROUND on our past work and F5 plans here: [[Media:FANTOM5-LL.ppt]] 
*More information, anticipated Abstract, Definitions, Plan of Work at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] 
*Outline: In FANTOM3, we described complex loci -- sense-antisense pairs [[Media:F5_human_sense-antisense_pairs_hg19.zip]], bidirectional promoters, and gene chains -- prevalent in mammalian genomes. These complex loci often contain long non-coding RNA (lncRNA) genes. Please see [[Media:F5_human_lncRNAome%28Jia%26Lipovich_Gencode_Lander%29.xls]] for our complete reference list of human lncRNA genes, the human lncRNAome; and [[Media:F5_human_lncRNAome(Jia%26Lipovich_Gencode)BED.zip]] for our BED file of the Gencode and our Jia et al lncRNAs. LncRNA Genes are often not conserved between mouse and human. Now in FANTOM5, our goal is to functionally characterize the specific contribution of non-conserved sequences in human, particularly promoters of lncRNA genes, to gene regulation at complex loci. We will reach this goal by:  

#identifying all "human-specific" (definition = primate-specific; thus absent in the F5 nonhuman species) promoters in CAGE and CAGEscan data. 
#using F5 Cluster Annotation results to find all lncRNA genes whose promoters are human-specific. 
#determining which lncRNA genes with human-specific promoters are in complex loci, as defined in the first sentence of this Outline. 
#testing each complex locus from #4 for the existence of a unique cis-regulatory expression signature (simple e.g.: all genes in the complex locus are on, all off, or some on and specific others off) that corresponds to a specific cell type, tissue type, or steady state. Signatures are defined both by an expression pattern and by an adjacency, overlap, and specific order / orientation of the co-expressed genes neighboring along the genome. 
#determining whether, and how, each complex-locus steady-state-specific expression signature is dependent upon the human-specific promoter of the lncRNA within that signature. (Implementation details are at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] )  
#identifying lncRNA genes whose human-specific promoters have evidence of recent functional constraint or recent positive selection. 
#performing, for lncRNAs of exceptional interest based on #5 and #6, reverse-genetic experiments in cell culture to validate whether the human-specific promoter of the lncRNA really has a regulatory impact that contributes to defining a particular steady state. (Note: we would need the OSC's direct help with wet-lab validations. Let's discuss.)  
#defining the unique functional proteome space (e.g. gene ontologies? positive selection? brain genes? etc) cis-regulated by human-specific lncRNA promoters. 
#finally, deriving a multidimensional unified cis- and trans-regulatory network that describes human-specific and lncRNA-mediated gene regulation in specific cellular states. Nodes (regulatory lncRNAs and transcription factors) will be shared between the cis-regulatory network (regulation based on genomic overlap or adjacency) and the trans-regulatory network (regulation based on TF-target or known lncRNA-TF relationships). (Definition of such a network is at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] ) 

*Group members: Leonard Lipovich, Yulia Medvedeva, Vlad Bajic and lab, Nicolas Bertin ([[User:Nbertin]]), (inviting you to join - please confirm: Jess Mar & colleagues), and I am also too shy to name (or invite) potential others. Please email me or the F5 list, or please just add yourselves to this page, if you would like to join this effort.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Using single direction promoter ti eliminate noise (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Specific transcript (Human) regulation and what are nover TF in human (Haru) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification of TFBS by de novo methods (Vlad; Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Predicted homotypic clusters and motif prediction (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification fo features of primates specific promoters (?; together with LL) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulation specificity of cells and tissues (Vlad’ s group) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Host pathogen infection relationship; influenza virus, Mycobacteria, Salomonella (Arnab) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Searching for viruses, cryptic viruses (Arnab, mamoon, al, nico) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Deorphanizing transcription factors (Vlad) ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Variation in small RNA population and variation in siRNA machinery (Max) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cellular restriction of terminal ligases in ubiquitin system (Max) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Papers of individual cells time courses ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Extend rat gene models with CAGEscan]] ==

*Outline: Rat gene models sometimes lack a proper 5′ UTR. CAGEscan data has been produced using the same RNA (10009-101B8) as the reference FANTOM5 Helicos CAGE library CNhs10612. This experimental data can be used to propose an update of the rat gene models.
*Group members: [[User:Plessy|Charles Plessy]], Albin Sandelin, Mette Jørgensen, Johannes Waage, other people, please list yourself.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Novel metrics for promoter activity profiles]] ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Pathway Fingerprinting]] ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cellular restriction of epigenomic regulation Erik A. + Andreas Lenn. ==

*Outline: We aim to map cell and differentiation specific expression and usage of alternative transcription start sites of chromatin remodellers, histone chaperones and other chromatin modifying enzymes.
*Group members: Erik Arner, Andreas Lennartsson

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Issues: negotiation with sample providers =

== Encouraged to write paper, but larger stronger papers is perhaps better? ==

The above is a tentative list of satellite manuscripts listed at the Ume meeting. Please take the time to fill in a brief description/abstract of the manuscript and read what others are proposing. Consider whether working together on a combined manuscript will generate a stronger (higher impact) manuscript.

== Talk with collaborator before the datasets is published ==

As mentioned at the Ume meeting. We are working together as a consortium which shares samples, analyses and ideas. Please talk to the sample providers and analysts if you are using a particular subset of the data. If you are uncertain about whether a dataset has particular strings attached please check the collaborator column on the Helicos data production schedule file on the front page. Samples labelled as "FANTOM5 OSC core" are largely free for use. If you want to use data generated on samples by a particular collaborator please contact them.

Satellite papers

2011-08-03T10:12:47Z

Martin: /* Evolution gene expression in vertebrates Martin + Peter Heutink */

= Instructions =

Below you can find the list of satellite paper proposals collected in the February meeting. Please add the following information to each of the proposals

*Check the title
*provide brief outline of the proposal
*add/remove your name in case you are interested to work on this satellite paper

Proposal for satellites papers 2/25/2011 Purpose: list up potential satellites; avoid redundancies, make better papers Figure out potential titles to discuss how to negotiate with specific journals.

Add a set of sentences (mini abstract) on the wiki and write an abstract

= Bioinformatics analysis methods =

== Normalization and clustering issues ==

*Outline: bla
*Group members: Tom Freeman

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Modulation of gene expression (Jess Mar) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Expanding transcriptional reg. networks (Vlad) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tag clustering in helicos CAGE (Cesare) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Computational methods for networks comparisons (cesare) ==

*Outline: bla
*Group members: FBK (C. Furlanello, G: Jurman, ...) 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tool to make the promoter subsets at will (do not ask us datasets!) (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Delve tag mapping paper (Timo L) ==

*Outline: Methods paper on Delve: a probabilistic read mapper.
*Group members: Timo Lassmann, Carsten Daub

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Classification of CAGE peaks (Timo L) ==

*Outline: Deeply sequenced CAGE libraries capture signals on many non-promoter regions. The purpose of this paper is to describe a strategy to separate TSS from non-TSS CAGE peaks (see: [[Media:CAGE_classification.pdf]]). Preliminary work suggest that further sub-classicifation of promoters based on the shape of the CAGE signal is possible (see: [[Media:Brood_october_2010.pdf]]).
*Group members: Timo Lassmann, Ben Brown, Colin Semple

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Peak finder-noise elimination contest paper (all runners) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Genomics-broad scale analysis =

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Alternative TSS in cancer relevant to biomarkers (Win Hide) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Impact of alternative promoters on biology of genes (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== How much do we need to sequence? Complexity of the transcriptome (Albin) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Revised analysis of zinc finger proteins (Tim Ravasi, David Hume) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification of distal regulation elements: role of enhancers in differentiation (Carsten, Boris, Ana P, Jose, YH) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== miRNA promoters (Hideya K, Eivind Al, ) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== CAGE tags on Pigs: Gain and loss of promoters (David Hume) [satellite of the pig genome] ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulatory transcription outside canonical promoters (Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Transcription initiation in embryo development (Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Promoters with multiple TSS configuration-multiple ways to use the same promoters (Boris, Kawaji) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Link Fantom 5 to genetic datasets (Peter Heutink; Juha K) ==

*Outline: Genome Wide Association Studies (GWAS) have been very succesfull in identifying new risk loci for multifactorial human disease. It has however been very difficult to identify the true biologically relevant variant. GWAS studies in general do not directly test the unknown causal variant but a variant that is in Linkage Disequilibrium with the causal variant. Studies to identify the causal variants are complicated by the observation that most signals from GWAS studies point to non-coding regions of the genome for which the functions are currently unknown. The dataset generated by FANTOM5 now allows to investigate the regions around the association signal for functional elements involved in transcription.

Research method: We have developed a statistical method to delineate the critical region for GWAS loci (Bochdanovits et al. Submitted). We aim to use this method on all publically available GWAS datasets in order to obtain the boundaries of identified GWAS loci. We will then superinpose these genomic region on FANTOM5 data from relevant tissues/celltypes for the disease and identify possible promoters. By using data from the 1000 Genomes project we will investigate if genomic variation exists in the identified promoters. These variants can then be tested for functional effects in cellular reporter assays.

*Group members: Peter Heutink, Juha Kere, Zoltan Bochdanovits and ......please sign up if you are interested. 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== DNA methylation affects TF binding and transcription (Yulia) ==

*Outline: It's commonly accepted that DNA methylation of a promoter repress transcription of this gene in normal tissues. Recently, a class of actively expressed genes having relatively methylated promoters has been discovered. The purpose of this research is to explore the idea that DNA methylation affects CG-rich TFBS, preventing some TF from binding to DNA, and therefore represses transcription.

*Details: [[DNA_methylation_and_transcription]]

*Group members: Yulia Medvedeva

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Comparison of different types/feature of promoters and genome features to study specific differences (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Multiple genomics analysis on multiple datasets (Haru) Extension of the validation? ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Prediction of cell transformation states (Win Hide) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Convergent evolution of retrotransposon promoters (Geoff) ==

*Outline: Following the model for the anti-apoptosis gene NAIP (Romanish et al., PLoS Genetics, 2007), we will start by screening the mouse and human genomes for instances where two different retrotransposons occupy the same or similar location in protein-coding genes (e.g. an Alu in human, a B2 in mouse). If this happens frequently enough to be interesting, we will overlay the F5 data onto the "convergent" retrotransposons to see how many are transcribed, what role they may have in regulation (e.g. Lunyak et al., Science, 2007) and if the events are more common for some pathways than others (e.g. in embryogenesis or brain development). 
*Group members: Geoff, Piero 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Alternative TSS and alternative splicing (Nicolas) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Chimaeric RNA and 3D structure (if it works) (Nicolas) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Annotation of genes involved in biochemical, metabolic processes and signature for processes-for instance signature for tumors – expression based GO terms (Tom Freeman) (Richard Baldarelli, Jackson and GO groups) (David Hume) ==

*Outline: bla
*Group members: xxx, yyy, zzz

 

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Blood group: fill in the holes, more discussion =

== Granulopoiesis analysis (Andreas Lenn.+Erik Arner) ==

*Outline:
*Group members: Andreas Lennartsson, Erik Arner

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Erythropoiesis (Peter K) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== HSC (Sugiyama san) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Macrophages (DH) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Subpopulations T cells and monocytes (Michael R) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Brain groups 4 papers Other priority areas in brain: discuss other brain and diseases (YH) =

== Evolution gene expression in vertebrates Martin + Peter Heutink ==

*Outline: Study the evolution of gene expression combining insights from each of the following:
**Gene/transcript level changes in expression (and estimating it's constraint/diversification).
**TSS/promoter turnover: orthologous genes using non-orthologous promoters, or changes in promoter-preference for one cell type between species.
**Sequence evolution of core promoters and distant regulatory blocks correlated with changes in gene expression.
*Focus of the paper on the well matched cells between ((Human, (Macaque?)),(Mouse, Rat),Dog),Chicken) for which we have hCAGE data. (Cell types: Hepatocytes, Aortic smooth muscle cells, mesenchymal stem cells).
*Group members: Martin Taylor, Peter Heutink, Alison Meynert
*Details: [[Evolution in gene expression]].

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''(Nature Genetics / Genome Research / Genome Biology / PLoS Genetics)'''

3. Estimated month of submission: '''Jan 2012'''

== Transcriptional constrains seq evolution [Martin+Michiel talk] ==

*Outline: [Michiel:] Network analysis across organisms & evolution of regulatory networks, in particular of developmental networks. Are there any subnetworks particularly conserved between organisms? What does this tell us about the functional importance and relevance of specific subnetworks? Do we see any recurring patterns in the network (Uri Alon-type feed-forward loops)? What are the conservation patterns and rates of divergence of transcription factors and specific regulatory relations? Do we see turnover of TFBSs, or do we see conservation of TFBSs in alignments? This can be applied specifically to brain, or more generally to all CAGE samples. TFBS prediction in Neanderthal compared to Homo sapiens would be really cool.
*Group members: Martin, Michiel, Peter Heutink
*Martin's and Michiel's idea for this paper may overlap or may be complementary to each other; we need to discuss this. This may end up as two satellite papers or one integrated one.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Tfbs turnover in liver (integrate with ChIP seq) Martin ==

*Outline: Integration of cross-species ChIP-seq data in liver with proximal CAGE tag cluster responses in the same species. Data on liver ChIP-seq for the transcription factors HNF1A and CEBPA in human/mouse/dog/(chicken) Schmidt et al, Science 2010 has been obtained. The questions we can address with this study are:
**Are conserved binding sites more likely than non-conserved sites to elicit a local, hepatocyte specific ranscriptional response? (Use CEBPA non-expressing cells to generate a background model of proximal transcriptional responses). This could be used to estimate "functional turnover" as opposed to the "binding turnover" as reported by Duncan Odom.
**Does hepatocyte specific expression (around binding sites) segregate through species lineages with the experimentally defined binding site?
**If there is apparent turn-over of binding sites, is the pattern of local responsive transcription conserved?
**Do we see conservation of hepatocyte specific transcriptional responses even in the absence of binding site conservation?
*Group members: Martin Taylor, Alison Meynert

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Disease paper (brain): human post mortem, … comparison healthy-disease Peter Heutink + Gustincich group ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Rett syndrome and visual cortex Alka ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Genomic architecture in 3 genes involved in Rett syndrome Alka ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Genomic architecture of neurodegenerative disease (Gustincich talk P.H., etc.) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Others =

== [[Olfactory receptors]] ==

*Outline: Promoters of [[Olfactory receptors]] (ORs) are still poorly documented. We have an unpublished promoter list for mouse, and CAGE libraries from human olfactory mucosa will be made. We will identify the promoters of the human ORs and analyse their structure. Many ORs have [[Alternative Promoters|alternative promoters]] and this is a potential example for the promotorome paper. Human-specific OR promoters might be found. There is evidence of expression of the ORs outside the mouse and human olfactory mucosa, and this satellite paper will report this. Experiments to find a ligand and propose a function may be carried out. More information on the page: [[Olfactory receptors]].
*Group members: [[User:Plessy|Charles Plessy]], Giovanni Pascarella, Stefano Gustincich and others, but I am too shy to add their name without asking.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cell-Cell communicatome (Al forrest) ==

*Outline: We have possibly the broadest expression map to date. The idea behind this paper will look at the expression of cell surface receptors and ligands (secreted and membrane bound) in the primary cell collection to examine which cells can communicate with which other cells. The receptor-ligand pairing will rely on published interactions. This will build a network where primary cell types are the nodes and receptor-ligand interactions are the edges.

*Group members: none assigned yet

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article: '''Genome Research, Molecular Systems Biology, Genome Biology'''

3. Estimated month of submission: '''month/year'''

== Drugable cells: drug targets (Al Forrest) ==

*Outline: We have possibly the broadest expression map to date. The idea behind this paper will look at the cell(and tissue) restriction of known drug targets. Given that we generally want to target one cell type (cancer) or organ, what does this expression profile tell us about undesired side effects due to the drug affecting other cells expressing the drug target.

*Group members: none assigned yet

1. Interested in including in special issue: '''Y'''

2. Appropriate journal for this article:'''Genome Research, Genome Biology'''

3. Estimated month of submission: '''month/year'''

== Definition of stem or precursors relationship (Claudio Schneider) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Gene regulation in cells of connective tissues (Vlad, Kim) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Transdifferentiation and network rewiring (Haru; WP6 + others) POTENTIAL main paper for later stage ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Network in cancer (Rama, win’s group) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulatory network in cell lineage tree (Carsten wp5) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Determination of conserved CAGE (Vlad) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Promoting human uniqueness: human-specific promoters of regulatory lncRNA genes drive cis- and trans-regulation. (LL) ==

*some BACKGROUND on our past work and F5 plans here: [[Media:FANTOM5-LL.ppt]] 
*More information, anticipated Abstract, Definitions, Plan of Work at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] 
*Outline: In FANTOM3, we described complex loci -- sense-antisense pairs [[Media:F5_human_sense-antisense_pairs_hg19.zip]], bidirectional promoters, and gene chains -- prevalent in mammalian genomes. These complex loci often contain long non-coding RNA (lncRNA) genes. Please see [[Media:F5_human_lncRNAome%28Jia%26Lipovich_Gencode_Lander%29.xls]] for our complete reference list of human lncRNA genes, the human lncRNAome; and [[Media:F5_human_lncRNAome(Jia%26Lipovich_Gencode)BED.zip]] for our BED file of the Gencode and our Jia et al lncRNAs. LncRNA Genes are often not conserved between mouse and human. Now in FANTOM5, our goal is to functionally characterize the specific contribution of non-conserved sequences in human, particularly promoters of lncRNA genes, to gene regulation at complex loci. We will reach this goal by:  

#identifying all "human-specific" (definition = primate-specific; thus absent in the F5 nonhuman species) promoters in CAGE and CAGEscan data. 
#using F5 Cluster Annotation results to find all lncRNA genes whose promoters are human-specific. 
#determining which lncRNA genes with human-specific promoters are in complex loci, as defined in the first sentence of this Outline. 
#testing each complex locus from #4 for the existence of a unique cis-regulatory expression signature (simple e.g.: all genes in the complex locus are on, all off, or some on and specific others off) that corresponds to a specific cell type, tissue type, or steady state. Signatures are defined both by an expression pattern and by an adjacency, overlap, and specific order / orientation of the co-expressed genes neighboring along the genome. 
#determining whether, and how, each complex-locus steady-state-specific expression signature is dependent upon the human-specific promoter of the lncRNA within that signature. (Implementation details are at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] )  
#identifying lncRNA genes whose human-specific promoters have evidence of recent functional constraint or recent positive selection. 
#performing, for lncRNAs of exceptional interest based on #5 and #6, reverse-genetic experiments in cell culture to validate whether the human-specific promoter of the lncRNA really has a regulatory impact that contributes to defining a particular steady state. (Note: we would need the OSC's direct help with wet-lab validations. Let's discuss.)  
#defining the unique functional proteome space (e.g. gene ontologies? positive selection? brain genes? etc) cis-regulated by human-specific lncRNA promoters. 
#finally, deriving a multidimensional unified cis- and trans-regulatory network that describes human-specific and lncRNA-mediated gene regulation in specific cellular states. Nodes (regulatory lncRNAs and transcription factors) will be shared between the cis-regulatory network (regulation based on genomic overlap or adjacency) and the trans-regulatory network (regulation based on TF-target or known lncRNA-TF relationships). (Definition of such a network is at: [[Regulatory lncRNAs: 'promoting' human uniqueness]] ) 

*Group members: Leonard Lipovich, Yulia Medvedeva, Vlad Bajic and lab, Nicolas Bertin ([[User:Nbertin]]), (inviting you to join - please confirm: Jess Mar & colleagues), and I am also too shy to name (or invite) potential others. Please email me or the F5 list, or please just add yourselves to this page, if you would like to join this effort.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Using single direction promoter ti eliminate noise (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Specific transcript (Human) regulation and what are nover TF in human (Haru) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification of TFBS by de novo methods (Vlad; Boris) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Predicted homotypic clusters and motif prediction (Yulia) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Identification fo features of primates specific promoters (?; together with LL) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Regulation specificity of cells and tissues (Vlad’ s group) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Host pathogen infection relationship; influenza virus, Mycobacteria, Salomonella (Arnab) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Searching for viruses, cryptic viruses (Arnab, mamoon, al, nico) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Deorphanizing transcription factors (Vlad) ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Variation in small RNA population and variation in siRNA machinery (Max) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cellular restriction of terminal ligases in ubiquitin system (Max) ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Papers of individual cells time courses ==

*Outline: bla
*Group members: xxx, yyy, zzz

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Extend rat gene models with CAGEscan]] ==

*Outline: Rat gene models sometimes lack a proper 5′ UTR. CAGEscan data has been produced using the same RNA (10009-101B8) as the reference FANTOM5 Helicos CAGE library CNhs10612. This experimental data can be used to propose an update of the rat gene models.
*Group members: [[User:Plessy|Charles Plessy]], Albin Sandelin, Mette Jørgensen, Johannes Waage, other people, please list yourself.

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Novel metrics for promoter activity profiles]] ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== [[Pathway Fingerprinting]] ==

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

== Cellular restriction of epigenomic regulation Erik A. + Andreas Lenn. ==

*Outline: We aim to map cell and differentiation specific expression and usage of alternative transcription start sites of chromatin remodellers, histone chaperones and other chromatin modifying enzymes.
*Group members: Erik Arner, Andreas Lennartsson

1. Interested in including in special issue: '''Y/N'''

2. Appropriate journal for this article:'''(provide name please)'''

3. Estimated month of submission: '''month/year'''

= Issues: negotiation with sample providers =

== Encouraged to write paper, but larger stronger papers is perhaps better? ==

The above is a tentative list of satellite manuscripts listed at the Ume meeting. Please take the time to fill in a brief description/abstract of the manuscript and read what others are proposing. Consider whether working together on a combined manuscript will generate a stronger (higher impact) manuscript.

== Talk with collaborator before the datasets is published ==

As mentioned at the Ume meeting. We are working together as a consortium which shares samples, analyses and ideas. Please talk to the sample providers and analysts if you are using a particular subset of the data. If you are uncertain about whether a dataset has particular strings attached please check the collaborator column on the Helicos data production schedule file on the front page. Samples labelled as "FANTOM5 OSC core" are largely free for use. If you want to use data generated on samples by a particular collaborator please contact them.

Evolution in gene expression

2011-02-28T17:39:25Z

Martin: Created page with 'This is a stub, to be expanded soon.'

This is a stub, to be expanded soon.

Satellite papers

2011-02-28T17:38:52Z

Martin: /* Evolution gene expression in vertebrates Martin + Peter Heutink */

Satellite papers

2011-02-28T12:11:13Z

Martin: /* Tfbs turnover in liver (integrate with ChIP seq) Martin */

Satellite papers

2011-02-28T12:10:10Z

Martin: /* Tfbs turnover in liver (integrate with ChIP seq) Martin T */

Discussion/Promoter definition

2011-01-12T14:35:40Z

Martin: /* Data format (proposal) */

* See ML thread starting from [fantom5:00032] and [fantom5:00064]

== Data format (proposal) ==
A [http://fantom.gsc.riken.jp/4/download/Tables/doc/ OSCtable] format. Specifically,
* A tab delimited file
* At the beginning of the file, these lines should appear:
##ProtocolREF = ARBITRARY_NAME_TO_DISTINGUISH_MEHTODS
##Date = 2011-01-12
##InputFile = https://fantom5-collaboration.gsc.riken.jp/files/data/shared/UPDATE_008/f5pipeline/human.cell_line.hCAGE/*.ctss.bed.gz
##InputFile = https://fantom5-collaboration.gsc.riken.jp/files/data/shared/UPDATE_008/f5pipeline/human.primary_cell.hCAGE/*.ctss.bed.gz
...
##ContactName = YOUR_NAME
##ContactEmail = YOUR_MAIL
* The first 4 columns should be: chrom, start.0base, stop, strand
* The subsequent columns (arbitrary order) should include the number of reads in a following way: counts.RNA_DESCRIPTION.CNhsXXXX.XXXX-XXXX
* The subsequent columns (arbitrary order) should include the tpm (tags per million) in a following way: tpm.RNA_DESCRIPTION.CNhsXXXX.XXXX-XXXX

for example,
{|border="1"
!chrom
!start.0base
!stop
!strand
!counts.Burkitt%27s%20lymphoma%20cell%20line%3aDAUDI.CNhs10739.10422-106C8
!counts.acute%20lymphoblastic%20leukemia%20%28B-ALL%29%20cell%20line%3aBALL-1.CNhs11251.10455-106G5
!counts.acute%20lymphoblastic%20leukemia%20%28B-ALL%29%20cell%20line%3aNALM-6.CNhs11282.10534-107G3
!tpm.Burkitt%27s%20lymphoma%20cell%20line%3aDAUDI.CNhs10739.10422-106C8
!tpm.acute%20lymphoblastic%20leukemia%20%28B-ALL%29%20cell%20line%3aBALL-1.CNhs11251.10455-106G5
!tpm.acute%20lymphoblastic%20leukemia%20%28B-ALL%29%20cell%20line%3aNALM-6.CNhs11282.10534-107G3
|-
| chr1
| 10
| 20
| +
| 8
| 14
| 103
| 0.8
| 1.4
| 10.3
|-
| chr1
| 30
| 40
| -
| 24
| 3
| 7
| 2.4
| 0.3
| 0.7
|-
|}

===Suggestions/requests===
* Many analyses will require a single point coordinate for a TSS, rather than a range. Previously we have used modal tag position. Modal tag may or may not end up being the right choice for Fantom5 but the reference position probably won't be the 5' or 3' edge of the tag distribution. So a useful additional field in this format would be "refPos".

Discussion/Promoter definition

2011-01-12T14:34:03Z

Martin: /* Data format (proposal) */

* See ML thread starting from [fantom5:00032] and [fantom5:00064]

== Data format (proposal) ==
A [http://fantom.gsc.riken.jp/4/download/Tables/doc/ OSCtable] format. Specifically,
* A tab delimited file
* At the beginning of the file, these lines should appear:
##ProtocolREF = ARBITRARY_NAME_TO_DISTINGUISH_MEHTODS
##Date = 2011-01-12
##InputFile = https://fantom5-collaboration.gsc.riken.jp/files/data/shared/UPDATE_008/f5pipeline/human.cell_line.hCAGE/*.ctss.bed.gz
##InputFile = https://fantom5-collaboration.gsc.riken.jp/files/data/shared/UPDATE_008/f5pipeline/human.primary_cell.hCAGE/*.ctss.bed.gz
...
##ContactName = YOUR_NAME
##ContactEmai = YOUR_MAIL
* The first 4 columns should be: chrom, start.0base, stop, strand
* The subsequent columns (arbitrary order) should include the number of reads in a following way: counts.RNA_DESCRIPTION.CNhsXXXX.XXXX-XXXX
* The subsequent columns (arbitrary order) should include the tpm (tags per million) in a following way: tpm.RNA_DESCRIPTION.CNhsXXXX.XXXX-XXXX

for example,
{|border="1"
!chrom
!start.0base
!stop
!strand
!counts.Burkitt%27s%20lymphoma%20cell%20line%3aDAUDI.CNhs10739.10422-106C8
!counts.acute%20lymphoblastic%20leukemia%20%28B-ALL%29%20cell%20line%3aBALL-1.CNhs11251.10455-106G5
!counts.acute%20lymphoblastic%20leukemia%20%28B-ALL%29%20cell%20line%3aNALM-6.CNhs11282.10534-107G3
!tpm.Burkitt%27s%20lymphoma%20cell%20line%3aDAUDI.CNhs10739.10422-106C8
!tpm.acute%20lymphoblastic%20leukemia%20%28B-ALL%29%20cell%20line%3aBALL-1.CNhs11251.10455-106G5
!tpm.acute%20lymphoblastic%20leukemia%20%28B-ALL%29%20cell%20line%3aNALM-6.CNhs11282.10534-107G3
|-
| chr1
| 10
| 20
| +
| 8
| 14
| 103
| 0.8
| 1.4
| 10.3
|-
| chr1
| 30
| 40
| -
| 24
| 3
| 7
| 2.4
| 0.3
| 0.7
|-
|}

===Suggestions/requests===
* Many analyses will require a single point coordinate for a TSS, rather than a range. Previously we have used modal tag position. Modal tag may or may not end up being the right choice for Fantom5 but the reference position probably won't be the 5' or 3' edge of the tag distribution. So a useful additional field in this format would be "refPos".

User:Martin

2011-01-05T14:59:12Z

Martin: Created page with '[http://www.hgu.mrc.ac.uk/people/taylor.html Martin S. Taylor] martin.taylor@hgu.mrc.ac.uk ===Interests=== * Relating gene regulatory and genome sequence evolution. * Promoter se…'

[http://www.hgu.mrc.ac.uk/people/taylor.html Martin S. Taylor] martin.taylor@hgu.mrc.ac.uk
===Interests===
* Relating gene regulatory and genome sequence evolution.
* Promoter sequence evolution.
* Regulatory "classes" of promoters and enhancers.
* Measuring the "complexity" of a transcriptional program.
===Role in F5===
* Bioinformatics analysis.
* Comparative genomics.
===Team===
* Martin Taylor (PI).
* Alison Meynert (Post Doc).
* Sarah Baker (PhD student).