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Set up pipeline differential splicing analysis #1

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hansenp opened this issue Mar 22, 2018 · 6 comments
Open

Set up pipeline differential splicing analysis #1

hansenp opened this issue Mar 22, 2018 · 6 comments

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@hansenp
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hansenp commented Mar 22, 2018

This recent paper provides an overview of pipelines that can be used differential splicing analysis:

https://www.biorxiv.org/content/early/2017/06/30/156752

Figure 1 shows an overview of different pipelines. The lower part is for differential splicing analysis.

image

@hansenp
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hansenp commented Mar 23, 2018

It seems that we have data for two different mutants. Given the high number of replicates recommended for RNA-seq (https://www.ncbi.nlm.nih.gov/pubmed/27022035) I would suggest to do a joint analysis for the two mutants.

We should first go for DEXseq first and then possibly also for Cufflinks. If we get moderately consistent results, we are done.

@sheridae
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I think the issue is with the underlying hypothesis here. DexSeq is a good method when dealing with inclusion of specific exons or splice sites. The method focusses on exons, but it is biased against short exons. This includes alternate 5′ or 3′ splice sites, which are defined as separate exons. So it is less efficient at recognising this class of variation. rMATS approach provides a precise description of the type of alternative splicing is especially valuable where the experimental question involves splicing mechanisms. I think this is the question to be interrogated in the case of PPIL1 mutations.

@sheridae
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The problem for me is: DEXSeq maps all of the exons in the canonical transcripts of a gene into a single conflated construct. This makes it difficult to work out what actual types of splicing events are being detected as well as the physical locations of the mis-spliced exons since the analysis in DEXSeq is gene-centric. In contrast, rMATS is exon-centric, so gives this additional information, with more obvious measures of statistical significance (q values and FDR) and effect size (included level difference). I find that this is invaluable data in order to prioritize individual mis-splicing events for wet lab testing using rtPCR, and some validation of splicing in candidate exons is essential. The rMATS output also helps if you want to identify cis-actting splicing factor binding sequences eg by kmer analysis.

@sheridae
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Hi Peter, excel spreadsheet from the Americans
RNA-seq samples.xlsx

@hansenp
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hansenp commented Mar 28, 2018

I think there DEXSeq performs the differential analysis on the count data. Before this, scripts are applied that take into account splicing and different isoforms (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136653).

But maybe rMATS is a good alternative. Hopefully it is not too complicated to use. At least it was cited 177 times.

@sheridae
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sheridae commented Mar 28, 2018 via email

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