SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier
Identifieur interne : 000163 ( Pmc/Checkpoint ); précédent : 000162; suivant : 000164SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier
Auteurs : Xiao Hu ; Iddo FriedbergSource :
- GigaScience [ 2047-217X ] ; 2019.
Abstract
Gene homology type classification is required for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. Consequently, a large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters.
Here we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long
SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at
Url:
DOI: 10.1093/gigascience/giz118
PubMed: 31648300
PubMed Central: 6812468
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier</title><author><name sortKey="Hu, Xiao" sort="Hu, Xiao" uniqKey="Hu X" first="Xiao" last="Hu">Xiao Hu</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Friedberg, Iddo" sort="Friedberg, Iddo" uniqKey="Friedberg I" first="Iddo" last="Friedberg">Iddo Friedberg</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31648300</idno><idno type="pmc">6812468</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812468</idno><idno type="RBID">PMC:6812468</idno><idno type="doi">10.1093/gigascience/giz118</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">000D02</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000D02</idno><idno type="wicri:Area/Pmc/Curation">000D02</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000D02</idno><idno type="wicri:Area/Pmc/Checkpoint">000163</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000163</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier</title><author><name sortKey="Hu, Xiao" sort="Hu, Xiao" uniqKey="Hu X" first="Xiao" last="Hu">Xiao Hu</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Friedberg, Iddo" sort="Friedberg, Iddo" uniqKey="Friedberg I" first="Iddo" last="Friedberg">Iddo Friedberg</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">GigaScience</title><idno type="eISSN">2047-217X</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Abstract</title><sec id="abs1"><title>Background</title><p>Gene homology type classification is required for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. Consequently, a large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters.</p></sec><sec id="abs2"><title>Findings</title><p>Here we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long <italic>k</italic>-mers to speed up homology search, while using a reduced amino acid alphabet and spaced seeds to compensate for the loss of sensitivity due to long <italic>k</italic>-mers. In addition, it uses an affinity propagation algorithm to reduce the memory usage when clustering large-scale orthology relationships into orthologous groups. In our tests, SwiftOrtho was the only tool that completed orthology analysis of proteins from 1,760 bacterial genomes on a computer with only 4 GB RAM. Using various standard orthology data sets, we also show that SwiftOrtho has a high accuracy.</p></sec><sec id="abs3"><title>Conclusions</title><p>SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at <ext-link ext-link-type="uri" xlink:href="https://github.com/Rinoahu/SwiftOrtho">https://github.com/Rinoahu/SwiftOrtho</ext-link></p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Koonin, Ev" uniqKey="Koonin E">EV Koonin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fitch, Wm" uniqKey="Fitch W">WM Fitch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Overbeek, R" uniqKey="Overbeek R">R Overbeek</name></author><author><name sortKey="Fonstein, M" uniqKey="Fonstein M">M Fonstein</name></author><author><name sortKey="D Ouza, M" uniqKey="D Ouza M">M D’souza</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rivera, Mc" uniqKey="Rivera M">MC Rivera</name></author><author><name sortKey="Jain, R" uniqKey="Jain R">R Jain</name></author><author><name sortKey="Moore, Je" uniqKey="Moore J">JE 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Gigascience</journal-id><journal-id journal-id-type="iso-abbrev">Gigascience</journal-id><journal-id journal-id-type="publisher-id">gigascience</journal-id><journal-title-group><journal-title>GigaScience</journal-title></journal-title-group><issn pub-type="epub">2047-217X</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">31648300</article-id><article-id pub-id-type="pmc">6812468</article-id><article-id pub-id-type="doi">10.1093/gigascience/giz118</article-id><article-id pub-id-type="publisher-id">giz118</article-id><article-categories><subj-group subj-group-type="heading"><subject>Technical Note</subject></subj-group></article-categories><title-group><article-title>SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Hu</surname><given-names>Xiao</given-names></name><xref ref-type="aff" rid="aff1"></xref><xref ref-type="author-notes" rid="afn1"></xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">http://orcid.org/0000-0002-1789-8000</contrib-id><name><surname>Friedberg</surname><given-names>Iddo</given-names></name><pmc-comment>idoerg@iastate.edu </pmc-comment>
<xref ref-type="aff" rid="aff1"></xref><xref ref-type="corresp" rid="cor1"></xref></contrib></contrib-group><aff id="aff1"><institution>Department of Veterinary Microbiology and Preventive Medicine, 2118 Veterinary Medicine, College of Veterinary Medicine, Iowa State University</institution>, Ames, IA, 50011,<country country="US">USA</country></aff><author-notes><corresp id="cor1">Correspondence address. Iddo Friedberg, Department of Veterinary Microbiology and Preventive Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, USA. E-mail: <email>idoerg@iastate.edu</email></corresp><fn id="afn1"><p>Present address: Gianforte School of Computing, 357 Barnard Hall Montana State University, Bozeman, MT, 59717 USA</p></fn></author-notes><pub-date pub-type="collection"><month>10</month><year>2019</year></pub-date><pub-date pub-type="epub" iso-8601-date="2019-10-24"><day>24</day><month>10</month><year>2019</year></pub-date><pub-date pub-type="pmc-release"><day>24</day><month>10</month><year>2019</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>8</volume><issue>10</issue><elocation-id>giz118</elocation-id><history><date date-type="received"><day>13</day><month>2</month><year>2019</year></date><date date-type="rev-recd"><day>07</day><month>6</month><year>2019</year></date><date date-type="accepted"><day>05</day><month>9</month><year>2019</year></date></history><permissions><copyright-statement>© The Author(s) 2019. Published by Oxford University Press.</copyright-statement><copyright-year>2019</copyright-year><license license-type="cc-by" xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:href="giz118.pdf"></self-uri><abstract><title>Abstract</title><sec id="abs1"><title>Background</title><p>Gene homology type classification is required for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. Consequently, a large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters.</p></sec><sec id="abs2"><title>Findings</title><p>Here we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long <italic>k</italic>-mers to speed up homology search, while using a reduced amino acid alphabet and spaced seeds to compensate for the loss of sensitivity due to long <italic>k</italic>-mers. In addition, it uses an affinity propagation algorithm to reduce the memory usage when clustering large-scale orthology relationships into orthologous groups. In our tests, SwiftOrtho was the only tool that completed orthology analysis of proteins from 1,760 bacterial genomes on a computer with only 4 GB RAM. Using various standard orthology data sets, we also show that SwiftOrtho has a high accuracy.</p></sec><sec id="abs3"><title>Conclusions</title><p>SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at <ext-link ext-link-type="uri" xlink:href="https://github.com/Rinoahu/SwiftOrtho">https://github.com/Rinoahu/SwiftOrtho</ext-link></p></sec></abstract><kwd-group kwd-group-type="keywords"><kwd>orthology analysis</kwd><kwd>homology search</kwd><kwd>orthology inference</kwd><kwd>clustering</kwd><kwd>orthologs</kwd><kwd>paralogs</kwd></kwd-group><funding-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">National Science Foundation</named-content><named-content content-type="funder-identifier">10.13039/100000001</named-content></funding-source><award-id>ABI 1458359</award-id></award-group></funding-group><counts><page-count count="12"></page-count></counts></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Friedberg, Iddo" sort="Friedberg, Iddo" uniqKey="Friedberg I" first="Iddo" last="Friedberg">Iddo Friedberg</name><name sortKey="Hu, Xiao" sort="Hu, Xiao" uniqKey="Hu X" first="Xiao" last="Hu">Xiao Hu</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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