Analyst experimentalist pairing

Analysis that will be done for all time-course

Please refer to Timecourses page.

List of experimentalists

UBC Dan Goldowitz, Thomas Ha, Peter Zhang

• B6 Mouse Cerebellum development (12 time points, 3 replicates each)
• Interest: Primary goal is the Identification of Transcriptional Network active during development.
• However, we would like to see various computational analyses applied to the cerebellar development time course data.

We just have scratched the surface and need computational biologists help to delve deep into the data. Comparison with other time series such as Testis development, thymus development and inner hair cell development as well. There are many biologically relevant samples that are not time course, but would be still interesting to compare.

List of Bioinformatics groups

Bristol (Owen Rackham, Julian Gough)

• Cell reprogramming and trans-differentiation
• "Mogrify" prediction of transcription factors for cell reprogramming. Also prediction and scoring of which trans-differentiations are feasible, i.e. the map of the landscape of (inducable) cellular differentiation.

CBRC Martin Frith

• Promoter motifs, general CAGE analysis
• Interested in helping to analyze a specific dataset, with focused questions. Interested to work with experimentalist who doesn't already have lots of analyst suitors ("nokori-mono").

(Win Hide) Harvard School of Public Health

• TSS switching , Pathway fingerprinting , lineage reconstruction and ontogeny
• Interested in analysis of stem cell systems in particular, willing to perform above activities on collaborator datasets

VIGG (Seva Makeev, vsevolod.makeev@gmail.com)

• TFBS, de novo motif discovery with ChIPMunk, TFBS arrangements in promoters, HoCoMoCo
• We are doing a lot with motif discovery\finding problems, especially using ChIP-seq data.
• Interested in helping to analyze a specific dataset with an accent on TF interaction. Have an experience in analysing oxidative stress datasets (hypo- and hyper-oxia) in human and rat.

Computational Bioscience Research Center (CBRC), KAUST (vladimir.bajic@kaust.edu.sa)

• Time-course regulatory networks including ncRNA
• transcription factor (TF) usage, TF binding sites (ab initio and known motifs), transcription co-factors
• data normalization, differential expression analysis, machine learning problems

BiGR/NTNU (Finn Drablos, finn.drablos@ntnu.no)

• Analysis of co-regulated gene sets, TFBS mapping and de novo discovery
• Using histone modifications and TFBS ChIP-Seq data to define and analyse genomic (in particular regulatory) regions
• ncRNA genes, in particular miRNA (including target prediction)
• The group is part of a bioinformatics core facility, giving access to general bioinformatics resources
• We are located in a cancer research / DNA repair department, making collaborations in that area particularly relevant

Timo Lassmann (Riken)

• Selecting genes / promoters in specific biological states.
• Attempts at linking de-novo motifs with TFs

FBK (Cesare Furlanello)

• Methods and pipeline are available to identify network components associated to different biological states and to target phenotypes, including evolution of networks in time course experiments. We can help assessing stability and reproducibility of networks found by reconstruction or other types of analysis. See: http://prezi.com/dqlbioyuazfv/biological-network-comparison-via-ipsen-mikhailov-distance
• We are interested to study the association between promoter pattern structure and the evolution of promoter-based networks.

Stanford (Sofia Kyriazopoulou, sofiakp@stanford.edu)

• Combine CAGE with sequence information (i.e. motif scores) to find the triggering events of the observed up/down-regulation.