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Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM
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Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM
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Summary: BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For mapping 100bp sequences, BWA-MEM shows better performance than several state-of-art read aligners to date. Availability and implementation: BWA-MEM is implemented as a component of BWA, which is available at http://github.com/lh3/bwa. Contact: hengli@broadinstitute.org
Forward citations
Cited by 5 Pith papers
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Parse indexing for discarding short pseudo-MEMs safely
Parse indexing enables safe choice and filtering of pseudo-MEMs for MEM searches without selecting k.
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Parse indexing for discarding short pseudo-MEMs safely
Parse indexing supplies lower bounds on longest MEM lengths inside pseudo-MEMs, enabling safe discarding of short ones during KeBaB preprocessing.
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LCMC performs categorical matrix completion via latent tensor factorization in a double-loop setup with adaptive dimension estimation and split-merge-refine enhancements, outperforming prior methods on synthetic and q...
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Parse indexing for discarding short pseudo-MEMs safely
The paper shows how parse indexing can select pseudo-MEMs that contain every MEM of length at least k while eliminating the need to choose k and the risk of discarding important matches.
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