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Hi, I am new to RNAseq and not a bioinformatician so please take my apologies if these are basic questions. After mRNA Illumina PE sequencing of 6 brain tissue samples (3 test, 3 controls), de novo assembly with Trinity (no reference genome) and DEG with bowtie2 we got: 1. a high number of very similar contigs (putative isoforms). Strangely, in each cluster of isoforms some contigs would be significantly differentially expressed in one direction while other contigs would be significantly differentially expressed in the opposite direction. I don't understand how this is possible. Having more than 90% similarity, often >99%, and assuming reads that map perfectly multiple times are distributed randomly, shouldn't read counts between very similar contigs also be similar? The end result is that at the pathway analysis step we end up with DEG showing, simultaneously, up and down regulation (as a consequence of opposite counts for isoforms that have the same functional annotation). 2. a significant number of reverse complement sequences. In this case the counts are similar and point in the same direction. However, I don't understand how these reverse complement sequences end up in the unigene list
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM