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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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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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With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.
Introduction
Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...05-22-2026, 06:42 AM
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