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If you do happen to perform such a comparison, please do post a link to it here and/or on biostars, since I expect many people would find it interesting.
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Why not download a real data set from NCBI and then randomly sample from that to derive pseudo-data sets of varying read depths? That way you would have an realistic baseline to compare to.Originally posted by Zimbobo View PostMichael Black, Ph.D.
ScitoVation LLC. RTP, N.C.Comment
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Note that the person you're replying to posted that ~4 years ago...Originally posted by mbblack View PostComment
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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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