BWA-SW

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Application data

Created by Heng Li and Richard Durbin
Biological application domain(s) Mapping
Principal bioinformatics method(s) Read mapping
Technology Sanger, Illumina, 454
Created at Sanger Institute
Maintained? Yes
Input format(s) FASTQ, FASTA
Output format(s) SAM
Software features Gapped alignment, Local alignment
Programming language(s) C
Licence GPLv3, MIT
Operating system(s) UNIX

Summary: Fast, accurate, memory-efficient aligner for long sequencing reads

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BWA-SW (Burrows-Wheeler Aligner, Smith-Waterman alignment) is an aligner that is distinct from BWA although it is included in the same downloadable package. It uses the same on-disk index as BWA, but the alignment algorithm is different. The BWA-SW algorithm is invoked by a BWA subcommand via bwa bwasw.

Features and limitations

BWA-SW

  • computes local alignments (it finds alignments for parts of the input reads)
  • works well on long reads
  • is a bit slower than BWA
  • does not support colorspace reads
  • helps to find chimeric reads: these are reads resulting from structural variations that map partially to one location on the reference and partially to a different location


Local alignment with BWA-SW

The following paragraph was by the author of BWA. Copied from a forum thread.

In BWA-SW, we say two alignments are distinct if the length of the overlapping region on the query is less than half of the length of the shorter query segment. We aim to find a set of distinct alignments which maximizes the sum of scores of each alignment in the set. This problem can be solved by dynamic programming, but as in our case a read is usually aligned entirely, a greedy approximation would work well. In the practical implementation, we sort the local alignments based on their alignment scores, scan the sorted list from the best one and keep an alignment if it is distinct from all the kept alignments with larger scores; if alignment a_2 is rejected because it is not distinctive from a_1, we regard a_2 to be a suboptimal alignment to a_1 and use this information to approximate the mapping quality.

Links


References

  1. . 2010. Bioinformatics


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