Hello,
I am comparing the # of uniquely mapped reads from the same fastq (illumina paired end) to 2 reference sequences using BWA-MEM. I expect mapping to one of the reference sequences (let's call it ref A) to be have many multi-mappers and mapping the other (ref B) to be more unique. Ref A has many duplicate sequences.
I'm using the same bwa-mem parameter for alignment:
bwa mem -M -t 16 -B 1 -T 40
I calculated uniquely mapped reads using samtools view:
unique_alignment=$(samtools view -c -F 0x904) # for excluding secondary, supplementary, unmapped reads
mapq30_unique=$(samtools view -c -q 10 -F 0x904)
The results are below:
# mapped reads # uniquely mapped reads % of total seq # of unmapped # of MAPQ=10 % MAPQ=10
refA 15758104 15008036 95.24011264 0 10384098 65.7639396211625
refB 16352354 15632108 95.595459834101 0 14264615 87.9141156068417
As you can see, there isn't any difference in % of uniquely mapped reads (~95% for both) but a difference is seen in MAPQ=10.
From what I read, MAPQ=0 means reads are mapped to many places. I was wondering why using samtools view alone fail to capture uniquely mapped reads, and additional MAPQ filtering is necessary to get the unique mapped reads?
Thanks for the help!
1 answer
All mapped reads have a primary alignment, which is what you end up counting with -F 0x904. You're not just excluding "multimappers" (that's not a terribly useful term) with the addition of -q 10, but also more generically uncertain alignments.
If you really want to see how well your dataset aligns to the two genomes then you need to instead concatenate the genomes together and align against the resulting merged genome. Then split the results by genome.
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