normalizing EXOM-seq read using DESeq2
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adR ▴ 10
@do-it-23093
Last seen 5 days ago
Germany, M√ľnchen

Hi, I have exom read aligned to hg38. I used featureCounts to quantify the reads. Therefore, I would like to normalize the reads in the different sample using DESeq2. May I know if there especial consideration that I should take before doing it. I mean I did not find anything related to EXOM seq in the DESeq2 vignettes.

Best AD

ExomeSeq DESeq2 • 256 views
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ATpoint ▴ 870
@atpoint-13662
Last seen 37 minutes ago
Germany

What is your analysis goal? Exome-seq is commonly used to call variants in the exome, it is not (at least I have never seen it) a quantification technique in which counts would be relevant. What kind of experiment is this, are we indeed talking about exome sequencing starting from fragmentated and then bead-captures DNA? Sure, put it into DESeq2, the only assumption is that "most genes do not change" or more formally that the median ratio captures the size relationship between the samples.

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@ATpoint , Thanks for your insight! Yes, I have read a few papers, and as you said the goal of the Exome-seq is to call variants. Here the data is from 3 different groups(5 replicates for each group) and the sample was taken from blood. So I want to compare the DNA fragmentation pattern across the group? Then I thought counting the reads of all samples and using DESeq2 for reading normalization might be help full to see a pattern between groups and get condition-specific biomarkers. However, looking at the PCA and the expression heatmap, could not see a difference between samples. Would you recommend me some other options and Tools in the Bioconductor so that I could compare the DNA fragments and see possible biomarkers, please? Best, AD

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So I want to compare the DNA fragmentation pattern across the group?

Never heard of that, what does this mean?

so that I could compare the DNA fragments and see possible biomarkers, please?

Again, what does that mean? Biomarkers are either transcripts, or some kind of gene or protein expression, or something a cell secrets like cytokines or vesicles. Or mutations and strutural variants, the latter is again something to derive from the actual DNA sequence (called variant calling) rather than any digital counts.

I think it might make sense to consult with your supervisor and peers on the exact question you want to answer. It seems to me that you have a misunderstanding what the data you have can actually give you in terms of biological insights.

If you really want to compare counts then just plug it into DESeq2. You need a matrix of raw counts with rows being the regions and columns being the samples, no magic here I guess.

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@ATpoint, Probably I should browse a bit more and come back! Thanks a lot!!

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