Hello everyone,
We are using directional RNA-seq (Illumina TruSeq® Stranded mRNA Sample Preparation Low Sample (LS) kit) to look at transcriptomes of marine microbes and have noticed that ~3-6% of our reads (after removal of rRNA both experimentally and computationally) map to the antisense of known genes. This can be very interesting biologically, but in some cases the antisense reads seem "sporadic" when compared to the sense reads, so we are looking for a computational method to differentiate between real antisense transcripts and "noise" generated during the library preparation.
Does anyone have any idea what is the level of "strand fidelity" one can expect during library prep, or ideas on how to computationally select "bona-fide" antisense transcripts?
Thanks
Daniel
We are using directional RNA-seq (Illumina TruSeq® Stranded mRNA Sample Preparation Low Sample (LS) kit) to look at transcriptomes of marine microbes and have noticed that ~3-6% of our reads (after removal of rRNA both experimentally and computationally) map to the antisense of known genes. This can be very interesting biologically, but in some cases the antisense reads seem "sporadic" when compared to the sense reads, so we are looking for a computational method to differentiate between real antisense transcripts and "noise" generated during the library preparation.
Does anyone have any idea what is the level of "strand fidelity" one can expect during library prep, or ideas on how to computationally select "bona-fide" antisense transcripts?
Thanks
Daniel
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