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Originally posted by Xi Wang
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Alignment score and mapping quality are not the same. The former is a measurement of the distance (or log-odds see:BLOSUM) between the observed sequence and reference sequence while the latter is generally the Phred-scaled probability of mismapping. In the SAM format, the alignment score can be stored in the "AS" tag while the fifth column is the mapping quality. -
Thank you very much for the reply. I was referring to the 5th column. It IS the mapping quality according to SAM manual. But I guess this mapping quality must be correlated with alignment score for some way.
I have seen a lot of alignment with low mapping quality (0 or 1). For the input of cufflinks, do you usually filter out these low quality mapping reads? what is the cutoff you usually use?
Thanks
Originally posted by Haneko View PostComment
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Hi clariet,
Actually I've never considered this column to be a factor in filtering, so i can't give you a definite answer. But I think it'd be good to filter away those low quality mappings, perhaps someone could give a recommendation on the threshold?Comment
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I am also ready for analysis of AB solid data with TopHat and later Cufflinks, hope for successful example hereComment
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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.Yesterday, 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... -
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
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