Small RNA paper page

Hey! Welcome to the small RNA FANTOM paper page. As discussed at the FANTOM5 Kouyou meeting, this paper is being headed up by Helena Persson, Eivind Valen, and Max Burroughs. Anyone interested is free to join in, just shoot an email over to Max (burrough@gsc.riken.jp) with your ideas for analyses, contributions for the paper.

Overview

The paper is currently designed to match the strengths of the FANTOM5 small RNA dataset which include 1) the range of the samples (not the depth in any one sample) and 2) matching hCAGE expression data.

Data

Contrary to what was said at the Kouyou meeting, we have ~300 total samples sequenced, not total number of primary cells (the ~300 number includes replicates). Here is a breakdown of where the "unique" libraries are coming from in reference to colloquial FANTOM5 data types:

small RNA samples

species	primary cell	timecourse	tissue
human	119	1	4
mouse	1	0	0
rat	1	0	1
dog	1	0	1
chicken	1	0	1

So this is a bit deflating, perhaps, but still a lot of primary cell types. Note the lone human timecourse sample does not mean we have sequenced an entire timecourse, just a single time point (single replicate) from an embryonic stem cell. For cross-species analysis we have Universal tissue in all species except mouse and Aortic smooth muscle in all species (although mouse has only a single replicate). I'm afraid the Universal tissue is not of much interest although the aortic smooth muscle could be quite interesting in terms of conservation of small RNA regulatory processes.

In the ~300 samples, there are 10 that failed completely but if I recall correctly all of these had additional replicates so the total unique primary cell count should remain at 119.

Data processing

Mapping

Re-extraction and mapping will hopefully be underway within 2 weeks, taking another week or two to finish. We'll provide both BWA and Delve mappings (BWA is used to "seed" the iterative Delve mapping, so its easy to keep the initial mapping in sam format). I'll keep updating.

Normalization

I will rerun the EdgeR analysis similar to last time and generate an miRNA expression table as well as a table with total tags, total miRNA tags, and RLE/TMM normalization factors for all libraries. I will also try to generate a table containing dispersion values for each set of replicates. For exploratory work (e.g. novel miRNA-hunting) it probably makes sense to keep all libraries (except maybe the 10 mentioned above) but for differential expression analysis, we probably want to identify and remove "bad" replicates. I'll look into this as well...

Quality control

Right now we are planning to flag libraries meeting the following conditions:

1. low total counts (<1,000 tags)
2. low percentage of miRNA in the library
3. poorly-reproducible across replicates

Libraries falling under 1 will be thrown out. 2 and 3 will be kept, but we recommend throwing out 3 (and probably 2) for expression analyses. We will make a list of the libraries falling under these condition available here.

Analysis for paper

I think we can safely divide the paper into two general areas: miRNA-related analysis and other small RNA analysis. The miRNA-related analysis will give us a "safety net"--something that can definitely be published without worrying about extensive validation. The other small RNA analysis is probably more exciting but might require more validation and all of its portending issues.

For differential expression, perhaps we start with a more naive approach and if/when something develops from the promoterome paper which adjusts for clusters of similar tissues, we switch to this. I don't think much would change, however...

Basic statistics, annotation of tags

Since I'm handling the processing over here I'll just compile most of this, if there are no objections. Most of the numbers will be based on Michiel de Hoon's processing pipeline.

1. (Max) percentage of classes of RNA across libraries
2. (Max/Helena?) size distributions for classes across libraries
3. (Max) expression locus counts at individual non-miRNA loci
4. (Helena) refined miRNA expression including exact and approximate expression number counts

miRNA

1. (Eivind, Helena?) Functional analysis of miRNAs entailing:
   1. differential expression analysis (deliver excel file to sample providers)
   2. hCAGE co-expression analysis to reveal miRNA function
   3. miRNA target analysis
2. (Helena) novel miRNA, tissue specificity of these, target gene predictions (TargetScan)
3. (Max) differences in post-transcriptional modifications across tissues
4. (Max) hCAGE co-expression analysis to identify factors involved in miRNA regulation
5. (Max?) differential isomiR usage across tissues

other small RNA

Results of the first three (?) points below can be summarized in excel format and provided to sample providers

1. (Max) tag-based differential expression analysis of "novel" short RNA populations
2. (Eivind) endo-siRNA from coding regions, discovery, relative location, etc.
   1. small RNA anti-sense to coding regions along length of transcript, not just promoter
3. sense/antisense short RNA overlapping lncRNA, specifically in Leonard's "chains" (this will likely be folded into main paper, but we can refer to it if something interesting comes out of it
4. (Helena) differential expression, novel small RNA derived from non-coding precursors
5. (Eivind?) promoter RNA
   1. differential expression
   2. affected promoter GO-term enrichment (variation across cell type?)
   3. effects of promoter RNA expression on gene expression in hCAGE across cell types
   4. teasing out possible differences in effects of sense/antisense promoter sequences
   5. conservation (or lack of conservation) of promoter RNA across species in same cell type
6. general conservation in small RNAs derived from larger noncoding precursors in animals?

Miscellaneous

• currently there are some plans to get sRNA in timecourses (cross your fingers). This would give the paper another angle and Helena points out it would be useful to show important RNAs for wet-bench collaborators.
• expression comparisons between small RNA libraries and hCAGE mature miRNA peaks, could be an additional form of validation for novel stuff
• reference to miRNA promoter satellite paper by Eivind and Kawaji-san
• Helena might be able to provide (limited) validation for novel miRNAs

General Timeline

Looking at dependencies and prioritization. The lists below are structured to imply dependency (indented tasks follow non-indented tasks...)

Differential expression analysis will ideally take into account clusters of related cell types. This is being worked out in promoterome paper so we should probably use the same method. As a naive initial approach we can identify based on average pooled expression.

• Max/Timo re-processing of the data
  • Max/Helena annotation/expression tasks
    • Max/Helena make expression tables available to computational researchers interested in overlap of shortRNA
  • miRNA analysis
    • Helena novel miRNA analysis
      • Max/Helena differential expression analysis of miRNAs (may need to include novel miRNA identified above)
      • Eivind hCAGE co-expression to predict function
      • Eivind effects of miRNA expression on predicted targets
      • Max post-transcriptional modifications to miRNA/isomir differences
      • Max anti-hCAGE expression of regulatory factors
  • non-miRNA analysis
    • Max/Helena differential expression of small RNA derived from non-coding RNA
      • RNAi active differentiation based on length?
    • Max unannotated populations of cell-specific small RNA populations
    • Eivind endo-siRNAs
    • Eivind small RNAs derived from coding regions/lncRNAs
    • Eivind promoter small RNAs differential expression
    • Eivind promoter small RNAs effect on expression
    • Eivind/Robin eRNA small RNA overlap