[BioC] Measuring shifts in intronic expression independently of exons

Wolfgang Huber whuber at embl.de
Fri Aug 9 16:02:29 CEST 2013

Dear James
have you already checked the DEXSeq package and the paper http://genome.cshlp.org/content/22/10/2008.long 
This does not apply 1:1 to what you are asking for, but afaIcs the main modification would be for you to define "counting bins" (on which the input to DEXSeq is computed by read overlap counting) that represent (i) exons and (ii) introns and then check for changes in relative intro usage (the 'ratios' you mention below).
Let me know how it goes

On 9 Aug 2013, at 10:02, James Perkins <j.perkins at ucl.ac.uk> wrote:

> Dear list,
> I would like to know if an experimental treatment leads to a significant
> shift in intronic expression for some genes.
> Imagine I have an experiment with 6 biological replicates of a given
> tissue. I believe that the treatment might lead to an increased intronic
> expression for some genes, unrelated to exonic expression.
> 3 of these receive no treament, they are used as control. The other 3
> receive experimental treatment.
> I then sequence the mRNA from these samples (Illumina, single end reads,
> ~40 million reads per sample), to obtain 6 fastq files, I align these to a
> refernce genome and get bam files.
> I was thinking that a fairly easy way to see if some genes show a
> consistent increased intronic expression following treatment would be to
> count intronically aligning reads for each gene (e.g. using GenomicRanges)
> and use something like DESeq to look for genes showing a significant change
> in intronic "expression".
> However, the problem is that this might be due to exonic expression, due to
> premature mRNAs etc., so I might end up finding genes that are
> differentially expressed at the exon level, and as a result the increased
> exon expression has caused increased intronic expression as a by product.
> Obviously I am not so interested in these genes wrt this method, I can find
> these using "traditional" DE methods.
> In addition, when I tried profiling intronic regions using reads mapping to
> introns, (using DESeq) it led to dodgy MA plots, where the 0 FC line was
> quite far above the minimum mean expression point, i.e. it didn't go
> through the middle of the clump of data points (if that makes sense). I
> wonder if this is due to the size factor calculation being based on
> intronic "expression" (well, reads mapping to introns), which is generally
> much lower than exon expression and therefore being somewhat unreliable.
> So  I would like to take this exon expression out of the equation, so to
> speak.
> I thought that one way might be to compare the ratios of exonic to intronic
> reads between samples, for each gene.
> For example one gene might have 30, 35 and 33 exonically mapping reads and
> 10,11 and 12 intronically mapping reads for control samples
> For case samples it might have 33, 32 and 34 exonically mapping reads;
> 20,21 and 19 intronically mapping reads.
> So we could compare 10/30, 11/35 and 12/33 for control to 20/33, 21/32  and
> 19/34 for case.
> Does this methodology sound reasonable? It is necessarily based on the
> assumption that intronic "expression" due to unspliced RNAs is correlated
> with exon expression.
> If it sounds reasonable, is there a test that is recommended to compare the
> ratios in such a way, that takes into account the biological replication of
> samples? I could do a simple test (chi squared) to compare the relative
> frequencies, but this wouldn't take into account the replicates.
> I realise this isn't really a specific bioconductor question, but hopefully
> it might be of interest to some of the list subscribers.
> Many thanks,
> Jim
> -- 
> James R Perkins, PhD
> 	[[alternative HTML version deleted]]
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