# Does rRNA depletion protocol give higher number of mapped reads in Intronic regions?

Recently, I have downloaded a publicly available dataset, which are 350 tumor samples. I see the following information from the published paper.

They used Ribo Zero Gold and rRNA was depleted. Strand specific data. After aligning the data I did some alignment quality check with Qualimap RNA-Seq QC tool. I visualised the bam files in IGV. Alignment is good. For all samples 90% alignment rate was seen. I observed that in all samples Higher percentage of mapped reads were originating in Intronic regions. Followed by Exonic and intergenic regions.

I have seen a post here Reads mapped to exonic, intronic and intergenic regions where they say high intronic reads could be because of contamination. I googled about higher reads in intronic regions and found some papers Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion and some other links RIBO-DEPLETION IN RNA-SEQ – WHICH RIBOSOMAL RNA DEPLETION METHOD WORKS BEST? in which they said Greater intronic reads were with rRNA depletion protocol.

And even in this RNA-SEQ tutorial, it is mentioned that - A higher intronic mapping rate is expected for rRNA removal compared to polyA selection.

So, my question:

I am working with lncRNAs. So, I'm using the samples prepared with rRNA depletion protocol. Is this higher intronic rate is common in rRNA depleted dataset or do I have to check anything else to proceed further with these samples?

• Hi @maven .. cool username. I'm not really sure this is a bioinformatics question. – M__ Oct 1 '20 at 22:48

It's not so much that you have "intronic contamination" or "genomic contamination", rather you're not selecting explicitly for full-length mature transcripts with rRNA depletion. That is the most common cause for higher intronic read rates. There's nothing you can do about this post-hoc, just continue along.

BTW, many lncRNA's are polyadenylated, so you'll keep them with poly-A selection.

• Thanks for the answer Devon. I saw this - Intronic and intergenic mapped reads could be long non coding RNAs, most of them are unannotated [researchgate.net/post/…. My approach is to look for novel lncRNAs. – maven Oct 2 '20 at 8:14
• Yes, it's a non-trivial problem if you can't filter out immature sequences before library prep. – Devon Ryan Oct 2 '20 at 8:32
• Just to make sure, I also checked the rRNA contamination. And I observed there is very less percentage. 12408836 + 0 in total (QC-passed reads + QC-failed reads) 0 + 0 secondary 0 + 0 supplementary 0 + 0 duplicates 81734 + 0 mapped (0.66% : N/A) 0 + 0 paired in sequencing 0 + 0 read1 0 + 0 read2 0 + 0 properly paired (N/A : N/A) 0 + 0 with itself and mate mapped 0 + 0 singletons (N/A : N/A) 0 + 0 with mate mapped to a different chr 0 + 0 with mate mapped to a different chr (mapQ>=5) – maven Oct 2 '20 at 10:00
• The read counts I extracted using featureCounts are low compared to total alignments number. Is this because the read counts are from the Exonic regions? – maven Oct 2 '20 at 13:09
• Yes, featureCounts ignore intronic reads by default. – Devon Ryan Oct 2 '20 at 13:38

I can't possibly see how intron contamination is linked to removal of rRNA depletion.

The only reason it would appear to have increased the number of contaminants is because post-rRNA removal the proporation of intron contaminants has increased against the total remaining RNA content. However, the actual total number of contaminants remains exactly the same pre- and post-rRNA removal. By the same token the RNA of interest lncRNAs will also have proportionally increased, so you get a better depth of its predominance and diversity.

Thats just life and perhaps just filter this bioinformatically.

• Hi @Michael thanks for the answer. You mean I should remove these rRNA contamination and then proceed? How do I do that? Any tips please – maven Oct 2 '20 at 6:25