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 PostHello,
does anyone know of any software that produces simulated RNA-Seq data. I am interested in questions like how many reads are needed for a good assembly with velvet for example, what read errors produce which problems in the assembly. Thanks in advance for any pointers.Michael Black, Ph.D.
ScitoVation LLC. RTP, N.C.
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Note that the person you're replying to posted that ~4 years ago...Originally posted by mbblack View PostWhy 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.
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by SEQadmin2
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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06-18-2026, 07:11 AM -
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by SEQadmin2
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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06-02-2026, 10:05 AM -
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