Long noncoding RNA main paper
Objectives
This paper aims to capture the complete breadth and diversity of long noncoding RNAs (lncRNAs) while leveraging the unique qualities of FANTOM5 to understand their cellular restriction and evolutionary impact on the human genome. In addition, it uses genome organization and context to probe functional properties and provide the first comprehensive classification scheme for lncRNAs.
Current Status **please read before making changes or commenting on the list**
This is a paper we can (and need) to move very quickly on. Much discussion in various forums has already been devoted to what analysis should/should not be performed for and included in this paper. This wikipage represents an up-to-date summary of specific tasks for the paper that have been approved of by the FANTOM WP9 members.
This is say that we have largely moved beyond the point of introducing previously undiscussed ideas for the final product. It does not say great new ideas will not be considered, but at this point if you are introducing a new idea it should be with the intention of personally carrying out the analysis.
If you are interested in assisting with a task listed below, please add your name after the task in parantheses (you will be contacted shortly...) Some people who have already expressed interest in a task have already had their names added (feel free to remove this).
Tasks for the Paper
Inclusion of novel noncoding RNAs in existing lncRNA set
Leonard Lipovich's lab has undertaken and completed the Herculean task of assembling and annotating the set of known lncRNAs and supplemented this with the set provided by Gencode <link here>. To leverage FANTOM5 data, we need to identify novel lncRNAs based on hCAGE peaks from Kawaji-san's clustering combined with data from RNA-seq. Specific tasks:
- selection of FANTOM5 samples for RNA-seq, sequencing, RNA-seq processing, and transcript assembly (Max, RIKEN OSC)
- collection and formatting of publicly-available RNA-seq data for further assistance in 'validation' (Max, open to recommendations)
- based on RNA-seq-derived transcript definitions, use translational analysis to assess coding potential (Ben Brown)
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