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Hi all, I'm using Ribopicker to remove rRNA sequences from RNA-Seq data but so far the amount of sequences being identified as rRNA seems to be very low (considering that >90% of the RNA is supposed to be rRNA) even in samples which haven't received any sort of treatment to reduce the rRNA content before cDNA library creation and sequencing. The parameters which I've used so far are -c (Alignment coverage threshold in percent. ) 50 -i (Alignment identity threshold in percent. )70 and -l (Alignment length threshold in percentage) 30. FYI The samples I'm looking at contain both human and bacterial rRNA. Any help would be appreciated.
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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.Yesterday, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...