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I need some help understanding the SAM format for 454 sequencing alignment reads. In the MAPQ field I have values like 37,0,25,23,20,18. How do I interpret these values in terms of mapping quality of my reads?
Thanks
biobee
Thanks
biobee
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Try googling for "mapping quality". The first link works for me... -
Originally posted by nilshomer View PostThanks a lot your reply. I followed your suggestion and the first hit I got was :http://maq.sourceforge.net/qual.shtmlOriginally posted by nilshomer View Post
I hope this is the one you were alluding to.
The gives a pretty neat explanation of MAPQ in SAM format. Based on my understanding of the webpage, I can conclude that:
When I have a MAPQ =0 ; this means that the read maps to two or more places with equal score
when I have a MAPQ >= 30 then I have a good quality mapping. However I am still not clear what values of 25,23,20,18 indicates in terms of the mapping quality?Comment
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Do you mean 25, 23, 20, and 18 individually? Use the calculation from the web page to calculate the probability of mismapping. A q value of 20 means that there is a 1/100 chance of it being mapped incorrectly.Comment
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Do you mean you have not just one number per line but a comma-separated list of values in the MAPQ field of your SAM file? That would be strange indeed.Comment
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Have a look at the SAM specifications (same link as above). Each line in the SAM file represents one mapping with one unique quality.
Multiple mappings per read are represented by multiple lines (unless you run a best-match or random-match strategy), ideally with MAPQ=0 but there are some aligners out there that assign MAPQ scores even for multiple mappings, hence they may have different scores.
[edit: misread the last post, thought it was from biobee, sorry about that]
If you actually get multiple mapq values per line, i'd love to know which aligner you are using.Comment
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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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