Mash: fast genome and metagenome distance estimation using MinHash

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from whic...

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Vydáno v:	Genome Biology Ročník 17; číslo 1; s. 132
Hlavní autoři:	Ondov, Brian D., Treangen, Todd J., Melsted, Páll, Mallonee, Adam B., Bergman, Nicholas H., Koren, Sergey, Phillippy, Adam M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 20.06.2016 Springer Nature B.V
Témata:	Animal Genetics and Genomics Automation Bioinformatics Biomedical and Life Sciences Cluster Analysis Data collection Databases, Nucleic Acid Estimates Evolution, Molecular Evolutionary Biology Genome Genomes Genomics Genomics - methods Human Genetics Library collections Life Sciences mash Metagenome metagenomics Metagenomics - methods Microbial Genetics and Genomics Mutation nanopores Phylogeny Plant Genetics and Genomics Privacy Software Comparative genomics Metagenomics Alignment Nanopore Genomic distance Sequencing
ISSN:	1474-760X, 1474-7596, 1474-760X
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Popis
Shrnutí:	Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license ( https://github.com/marbl/mash ).
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1474-760X 1474-7596 1474-760X
DOI:	10.1186/s13059-016-0997-x