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Old 03-02-2014, 12:36 PM   #2
Brian Bushnell
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Location: Walnut Creek, CA

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Quote:
Originally Posted by Ohad View Post
Considering all that, I would like to share some questions:
1) Should I filter out QC failed reads ?
Yes, unless that somehow biases the data or drops your coverage too low. Otherwise you'll be adding a lot of noise, and the noise may be biased in some way. Do you know why the %failed is so high?

Quote:
I looked inside the file, how can a 51bp read with a running Phred score of 2 (ASCII chars consist only of "#" so I caculated 35 -33 = 2 , right?) get a CIGAR 51M ? and can I assume that it is a good alignment after all ?
Look at the mapping score, not the cigar string. Old-style cigar strings are worthless without context: M stands for Match or Mismatch. New-style cigar strings use '=' for match and 'X' for mismatch, and therefore actually give you useful information. Also, 2 is a "special" quality score for Illumina. 5 to 41 are normal Phred values, but 2 indicates something else (like, that two clusters are too close together to distinguish) and is usually much more accurate than a true Q2 base, up to a measured Q11~Q15 in my tests on HighSeq data. Bases with Q2 should not be used for error-sensitive things like assembly and variation calling, but they can increase alignment accuracy if the aligner is error-tolerant, because longer reads have less ambiguity. BWA is not very error-tolerant, though.

Quote:
3) If I decide to filter out some reads. What are the consequences of removing a read that is a part of a pair ? I suspect that read2 will be left with a wrong flag, indicating it is paired while read1 is no longer present. Can this flag thing create some problems downstream along the pipeline ?
I do not recommend removing singletons from a paired dataset. Use a tool that can process paired reads in their original fastq, and if either fails your filtering criteria are, then remove both. I like to create 2 outputs, one for good pairs and one for orphaned singletons, so as to conserve all of the good data (though in practice the orphaned singletons file usually gets ignored unless there is a severe lack of coverage otherwise). Then map the clean files. This way saves a lot of processing and results in smaller intermediate files. And it's much easier, since pairs in the original fastq pairs will be together, and thus can be processed as a stream, whereas in a BAM they could be anywhere.
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