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I mean, how do I see if two reads come from the same sample? The manual only tells me that 2 reads having identical QNAME are paired-end, right? Could someone tell how to read QNAME, above all it's such a long string of words that must tell something other than that...
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I just wanted to know if there's barcode for sample information (in cases of pooled sample), where would it be? Thx~~ -
What manual?Comment
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Often the QNAME is info about the instrument and the date of run, and the lane and tile and xy coordinates. The read group might tell you the sample name. That might be stored in an NM tag in the .sam file, or in the @RG header of the .sam fileComment
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The NM tag is for the # of mismatches. Look in the RG header, if present.Originally posted by swbarnes2 View PostComment
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Right, typo on my part. The SM tag might contain a sample name.Originally posted by nilshomer View PostComment
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Well, the description for the SM tag is"SM:Sample. Use pool name where a pool is being sequenced." I wasn''t putting it clearly...what I meant was, if this sample is pooled, and the origins of the sample content each is barcoded, is it possible that this infomation is contained in .SAM format? And this must be contained in the non-header sectoin...Originally posted by swbarnes2 View Post
If it is not, how is that usually done? Is it customary that I ligate an adaptor to each sample origin, pool them, sequence, and look at the beginning of each read to see which origin of sample it came from?
Lots of thx!!!Comment
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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.
...06-02-2026, 10:05 AM -
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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