Please use pbmarkdup instead.
This is yet another markdup tool (YAMT) that serves much the same purpose as
Picard MarkDuplicates or samtools markdup -- flag reads that are likely
the result of PCR amplification.
For each alignment in an aligned, sorted BAM, I store reference id, alignment
start, alignment end, read quality (priority: rq tag, mean of quality string,
or dropped if missing), number of passes (np tag, dropped if missing), query
length, and the md5 hash of the query name.
Reads are classified as duplicate if:
- reference id matches
- alignment starts and ends within aln_wiggle bp
- read length is within len_wiggle %
I choose a single alignment to represent each set of duplicates by the following rules in descending priority:
- highest read quality
- highest number of passes
- lowest query name md5 hash
The duplicate read frequency is output to stdout. If --output <target> is
specified, a BAM is written to <target> with duplicate reads flagged with the
0x400 bit.
python2 >=2.7
pip install pysam pandas numpyusage: markdup.py [-h] [--outBAM OUTBAM]
[--aln_wiggle ALN_WIGGLE]
[--len_wiggle LEN_WIGGLE]
inBAM
Reads with identical alignments within aln_wiggle on both ends
and with length within len_wiggle % are marked as duplicates.
Alignment with highest read quality, number of passes, and
query name md5 hash (in that order) is chosen as primary.
positional arguments:
inBAM aligned bam
optional arguments:
-h, --help show this help message and exit
--outBAM OUTBAM output bam
--aln_wiggle ALN_WIGGLE bp alignment wiggle at ends, default 2
--len_wiggle LEN_WIGGLE percent read len wiggle, default 10