KAnalyze: a fast versatile pipelined k-mer toolkit.
Identifieur interne : 001A25 ( PubMed/Corpus ); précédent : 001A24; suivant : 001A26KAnalyze: a fast versatile pipelined k-mer toolkit.
Auteurs : Peter Audano ; Fredrik VannbergSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2014.
English descriptors
- KwdEn :
- MESH :
- chemistry : Chromosomes, Human, Pair 1.
- methods : Sequence Analysis, DNA.
- Algorithms, Humans, Software.
Abstract
Converting nucleotide sequences into short overlapping fragments of uniform length, k-mers, is a common step in many bioinformatics applications. While existing software packages count k-mers, few are optimized for speed, offer an application programming interface (API), a graphical interface or contain features that make it extensible and maintainable. We designed KAnalyze to compete with the fastest k-mer counters, to produce reliable output and to support future development efforts through well-architected, documented and testable code. Currently, KAnalyze can output k-mer counts in a sorted tab-delimited file or stream k-mers as they are read. KAnalyze can process large datasets with 2 GB of memory. This project is implemented in Java 7, and the command line interface (CLI) is designed to integrate into pipelines written in any language.
DOI: 10.1093/bioinformatics/btu152
PubMed: 24642064
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pubmed:24642064Le document en format XML
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While existing software packages count k-mers, few are optimized for speed, offer an application programming interface (API), a graphical interface or contain features that make it extensible and maintainable. We designed KAnalyze to compete with the fastest k-mer counters, to produce reliable output and to support future development efforts through well-architected, documented and testable code. Currently, KAnalyze can output k-mer counts in a sorted tab-delimited file or stream k-mers as they are read. KAnalyze can process large datasets with 2 GB of memory. This project is implemented in Java 7, and the command line interface (CLI) is designed to integrate into pipelines written in any language.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">24642064</PMID><DateCompleted><Year>2014</Year><Month>09</Month><Day>18</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>30</Volume><Issue>14</Issue><PubDate><Year>2014</Year><Month>Jul</Month><Day>15</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>KAnalyze: a fast versatile pipelined k-mer toolkit.</ArticleTitle><Pagination><MedlinePgn>2070-2</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btu152</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">Converting nucleotide sequences into short overlapping fragments of uniform length, k-mers, is a common step in many bioinformatics applications. While existing software packages count k-mers, few are optimized for speed, offer an application programming interface (API), a graphical interface or contain features that make it extensible and maintainable. We designed KAnalyze to compete with the fastest k-mer counters, to produce reliable output and to support future development efforts through well-architected, documented and testable code. Currently, KAnalyze can output k-mer counts in a sorted tab-delimited file or stream k-mers as they are read. KAnalyze can process large datasets with 2 GB of memory. This project is implemented in Java 7, and the command line interface (CLI) is designed to integrate into pipelines written in any language.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">As a k-mer counter, KAnalyze outperforms Jellyfish, DSK and a pipeline built on Perl and Linux utilities. Through extensive unit and system testing, we have verified that KAnalyze produces the correct k-mer counts over multiple datasets and k-mer sizes.</AbstractText><AbstractText Label="AVAILABILITY AND IMPLEMENTATION" NlmCategory="METHODS">KAnalyze is available on SourceForge: https://sourceforge.net/projects/kanalyze/.</AbstractText><CopyrightInformation>© The Author 2014. Published by Oxford University Press.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Audano</LastName><ForeName>Peter</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vannberg</LastName><ForeName>Fredrik</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>T32 GM105490</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>03</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Bioinformatics</MedlineTA><NlmUniqueID>9808944</NlmUniqueID><ISSNLinking>1367-4803</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002878" MajorTopicYN="N">Chromosomes, Human, Pair 1</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="Y">Software</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>3</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>3</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>9</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24642064</ArticleId><ArticleId IdType="pii">btu152</ArticleId><ArticleId IdType="doi">10.1093/bioinformatics/btu152</ArticleId><ArticleId IdType="pmc">PMC4080738</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Nat Biotechnol. 2013 Apr;31(4):325-30</Citation><ArticleIdList><ArticleId IdType="pubmed">23475072</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS Biol. 2014 Jan;12(1):e1001745</Citation><ArticleIdList><ArticleId IdType="pubmed">24415924</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2013 Mar 1;29(5):652-3</Citation><ArticleIdList><ArticleId IdType="pubmed">23325618</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2011 Mar 15;27(6):764-70</Citation><ArticleIdList><ArticleId IdType="pubmed">21217122</ArticleId></ArticleIdList></Reference><Reference><Citation>Nucleic Acids Res. 2009 Jan;37(Database issue):D77-82</Citation><ArticleIdList><ArticleId IdType="pubmed">18842628</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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