Time-courses analysis overview

Here you find a description of the analyses applied consistently to all (or certain classes of) time-courses. Follow the links in this overview page to get to the detailed descriptions.

Analysis providers: please edit this page to establish downstream pages detailing your analysis. You can change the suggested links below before you establish the page!

Quality control with a set of basic analyses (Erik Arner's analysis)

DPI clustering (Hideya Kawaji)

TSS classifier (Timo Lassmann)

Novel motifs in time-courses (Vsevolod Makeev, Ivan Kulakovskiy, Ulf Schaefer, Yulia Medvedeva, Michael Rehli)

Expression analysis by CAGE (Break don to smaller items here? Or establish many sub-pages). (Erik Arner, Owen Rackham, Sarah, Wyeth Wasserman, Anthony Mathelier, Boris Lenhard, Vanja Haberle, Finn Drablos, Morten Rye, Carlo Cannistraci, Tim Ravasi, Kim-Anh Le Cao, others)

Enhancers and their RNA (Albin et al.)

Alternative promoters (Emmanuel Dimont; Martin Taylor, Sarah Baker)

MARA (Erik van Nimwegen, Peter Pemberton-Ross, Erik Arner)

Genome variation and expression (Kenneth Baillie)

miRNA (Erik Arner, Pal Saetrom, others)

Other topics (establish page yourself):

• TF analysis expression, TF based network
• TF that characterize a given cellular state (and drive it?);
• TF and diseases literature search
• Biolayout to all samples
• Antisense promoters
• Enhancers connection to target (Hi-C, Chia-PET. Etc.)
• SNP of each promoter, enhancer, ncRNAs at each isolated key feature in time courses
• Noncoding RNA: long and small; including Retrotransposon elements expression
• The above non-coding RNA that characterize a cellular state
• Retrotransposons retrotransposition (targeted RE capture)
• Antisense and ncRNAs associated to TF and other protein coding RNAs
• ncRNAs that show up in specific time course points, candidate for specific validation
• Seed for future mechanistic studies by each laboratory
• Human versus mouse, data analysis for both systems
• Concordant-discordant promoters in the time course (within time course; what is their biology)
• Ripple of transcription (and what is the relation with enhancer?)

Guidelines for the content

• A description of the analysis - not all the maths or programming, but a simple word description of what the analysis does, what it is likely to tell us, what are the assumptions and limitations
• What their analysis already provides at the practical level: visualizations/images, tables, statistics, lists of genes etc. If the output is a table or text file, where can we go to get images or further analysis? Links to the outputs within the wiki.
• Reference papers where this analysis or a similar analysis has been used, for those who want further information behind the mathematics and statistics or examples of the interpretation.
• Who to contact if trouble arises. Level of collaboration available (eg is the provider able to do custom analysis if we can't manage it ourselves).
• Links to webpages that were used to develop the resource/output.
• Links to websites that provide results or data or analysis.
• Maybe a Standard Operating Procedure of how to perform the analysis? (ie scripts, user guide for web-based analysis....etc.)