Answering biological questions by querying k‐mer databases
Identifieur interne : 002161 ( Main/Exploration ); précédent : 002160; suivant : 002162Answering biological questions by querying k‐mer databases
Auteurs : Paul Greenfield [Australie] ; Uwe Roehm [Australie]Source :
- Concurrency and Computation: Practice and Experience [ 1532-0626 ] ; 2013-02.
English descriptors
- Teeft :
- Alignment search tool, Amino acid, Assembly tools, Astronomical databases, Available organism, Bacterial community, Bacterial database, Bacterial genomes, Bioinformatics, Biological questions, Carrier protein, Comparison code, Computat, Concurrency, Concurrency computat, Copyright, Database, Elongation factor, Exper, Faint shadows, Gene, Genes table, Genome, Genome research, Genome string, Helicase ruvb, Holliday junction, Hypothetical protein, Hypothetical proteins, John wiley sons, Large numbers, Length product, Metagenomic, Methanococcus maripaludis, Metric, Microsoft research, Multiple copies, Nding, Nding anchor points, Next query, Next step, Next version, Organism, Other enterobacteriales, Possible functions, Pract, Query, Reference organism, Reference organisms, Relatedness, Ribosomal operon, Roehm figure, Same database, Sequence data, Sequence string, Sequence strings, Single location, Taxonomic, Taxonomic distance, Taxonomy database, Technical report, Transcription termination factor, Transposable elements, Uniqueness property, Unrelated organisms.
Abstract
This paper describes a k‐mer approach to analysing DNA data and quickly answering certain types of ad hoc biological questions. These k‐mers (short DNA strings) are stored in a conventional relational database and indexed to support efficient exact match operations. We show that k‐mers around 20–25 bases long have interesting and useful uniqueness properties that can be used to compute a ‘relatedness’ metric and also allow k‐mers to be used as ‘unique enough’ tags to identify organisms and genes. This relatedness metric is used in SQL queries that can directly answer questions such as how two related species differ, and what genes are unique to an organism. The k‐mer tags have proven useful in applications, largely metagenomic ones that can quickly process large volumes of sequencing data to say something about what organisms and genes might be present in an environmental sample. All of this work is based on simple and fast exact matches of k‐mer strings using a database, rather than conventional alignment based on inexact matches of much longer strings. These k‐mer tools provide ways of rapidly exploring large genome spaces and handling large volumes of sequence data, and complement rather than replace existing alignment and assembly tools. Copyright © 2012 John Wiley & Sons, Ltd.
Url:
DOI: 10.1002/cpe.2938
Affiliations:
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These k‐mers (short DNA strings) are stored in a conventional relational database and indexed to support efficient exact match operations. We show that k‐mers around 20–25 bases long have interesting and useful uniqueness properties that can be used to compute a ‘relatedness’ metric and also allow k‐mers to be used as ‘unique enough’ tags to identify organisms and genes. This relatedness metric is used in SQL queries that can directly answer questions such as how two related species differ, and what genes are unique to an organism. The k‐mer tags have proven useful in applications, largely metagenomic ones that can quickly process large volumes of sequencing data to say something about what organisms and genes might be present in an environmental sample. All of this work is based on simple and fast exact matches of k‐mer strings using a database, rather than conventional alignment based on inexact matches of much longer strings. These k‐mer tools provide ways of rapidly exploring large genome spaces and handling large volumes of sequence data, and complement rather than replace existing alignment and assembly tools. Copyright © 2012 John Wiley & Sons, Ltd.</div></front></TEI><affiliations><list><country><li>Australie</li></country><region><li>Nouvelle-Galles du Sud</li></region><settlement><li>Sydney</li></settlement><orgName><li>Université de Sydney</li></orgName></list><tree><country name="Australie"><noRegion><name sortKey="Greenfield, Paul" sort="Greenfield, Paul" uniqKey="Greenfield P" first="Paul" last="Greenfield">Paul Greenfield</name></noRegion><name sortKey="Greenfield, Paul" sort="Greenfield, Paul" uniqKey="Greenfield P" first="Paul" last="Greenfield">Paul Greenfield</name><name sortKey="Roehm, Uwe" sort="Roehm, Uwe" uniqKey="Roehm U" first="Uwe" last="Roehm">Uwe Roehm</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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