have you already checked the DEXSeq package and the paper
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
> 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
> expression for some genes, unrelated to exonic expression.
> 3 of these receive no treament, they are used as control. The other
> receive experimental treatment.
> I then sequence the mRNA from these samples (Illumina, single end
> ~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
> count intronically aligning reads for each gene (e.g. using
> and use something like DESeq to look for genes showing a significant
> in intronic "expression".
> However, the problem is that this might be due to exonic expression,
> premature mRNAs etc., so I might end up finding genes that are
> differentially expressed at the exon level, and as a result the
> exon expression has caused increased intronic expression as a by
> Obviously I am not so interested in these genes wrt this method, I
> these using "traditional" DE methods.
> In addition, when I tried profiling intronic regions using reads
> introns, (using DESeq) it led to dodgy MA plots, where the 0 FC line
> 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
> wonder if this is due to the size factor calculation being based on
> intronic "expression" (well, reads mapping to introns), which is
> much lower than exon expression and therefore being somewhat
> So I would like to take this exon expression out of the equation,
> I thought that one way might be to compare the ratios of exonic to
> reads between samples, for each gene.
> For example one gene might have 30, 35 and 33 exonically mapping
> 10,11 and 12 intronically mapping reads for control samples
> For case samples it might have 33, 32 and 34 exonically mapping
> 20,21 and 19 intronically mapping reads.
> So we could compare 10/30, 11/35 and 12/33 for control to 20/33,
> 19/34 for case.
> Does this methodology sound reasonable? It is necessarily based on
> assumption that intronic "expression" due to unspliced RNAs is
> with exon expression.
> If it sounds reasonable, is there a test that is recommended to
> ratios in such a way, that takes into account the biological
> samples? I could do a simple test (chi squared) to compare the
> frequencies, but this wouldn't take into account the replicates.
> I realise this isn't really a specific bioconductor question, but
> it might be of interest to some of the list subscribers.
> Many thanks,
> James R Perkins, PhD
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