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Originally posted by Richard Finney
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They modified the settings to make sure lots of valid alignments are not reported, just like (bowtie -m 1 -v 3). Then they use a better aligner and find more usable reads. Some interesting things about contaminants and quite ambitious to reanalyze all datasets but why not use a better aligner in the first place. -
Well, it's very arguable that Shrimp2 is "better" than BWA/Bowtie2 (whatever that means).
Thank you for the paper though, definitely interesting take on the problem.Comment
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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
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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...
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