CircMarker: a fast and accurate algorithm for circular RNA detection
Identifieur interne : 000624 ( Pmc/Checkpoint ); précédent : 000623; suivant : 000625CircMarker: a fast and accurate algorithm for circular RNA detection
Auteurs : Xin Li [États-Unis] ; Chong Chu [États-Unis] ; Jingwen Pei [États-Unis] ; Ion M Ndoiu [États-Unis] ; Yufeng Wu [États-Unis]Source :
- BMC Genomics [ 1471-2164 ] ; 2018.
Abstract
While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3’ and 5’ and forms the so-called circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility of some diseases. During the past several years, multiple experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circular RNA detection have been developed as well, these tools are based on reads mapping may miss many circular RNA. Also, existing tools are slow for large data due to their dependence on reads mapping.
In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create the k-mer table for circular RNA detection.
Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets.
We develop a new circular RNA detection method called CircMarker based on k-mer analysis. Our results on both simulation data and real data demonstrate that CircMarker runs much faster and can find more circular RNA with higher consensus-based sensitivity and high accuracy ratio compared with existing tools.
The online version of this article (10.1186/s12864-018-4926-0) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12864-018-4926-0
PubMed: 30367583
PubMed Central: 6101086
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">CircMarker: a fast and accurate algorithm for circular RNA detection</title><author><name sortKey="Li, Xin" sort="Li, Xin" uniqKey="Li X" first="Xin" last="Li">Xin Li</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:regionArea>Storrs</wicri:regionArea></affiliation></author><author><name sortKey="Chu, Chong" sort="Chu, Chong" uniqKey="Chu C" first="Chong" last="Chu">Chong Chu</name><affiliation wicri:level="1"><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">000000041936754X</institution-id><institution-id institution-id-type="GRID">grid.38142.3c</institution-id><institution>Department of Biomedical Informatics, Harvard Medical School,</institution></institution-wrap>Boston, 02115 MA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:regionArea>Boston</wicri:regionArea></affiliation></author><author><name sortKey="Pei, Jingwen" sort="Pei, Jingwen" uniqKey="Pei J" first="Jingwen" last="Pei">Jingwen Pei</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:regionArea>Storrs</wicri:regionArea></affiliation></author><author><name sortKey="M Ndoiu, Ion" sort="M Ndoiu, Ion" uniqKey="M Ndoiu I" first="Ion" last="M Ndoiu">Ion M Ndoiu</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:regionArea>Storrs</wicri:regionArea></affiliation></author><author><name sortKey="Wu, Yufeng" sort="Wu, Yufeng" uniqKey="Wu Y" first="Yufeng" last="Wu">Yufeng Wu</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:regionArea>Storrs</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">30367583</idno><idno type="pmc">6101086</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6101086</idno><idno type="RBID">PMC:6101086</idno><idno type="doi">10.1186/s12864-018-4926-0</idno><date when="2018">2018</date><idno type="wicri:Area/Pmc/Corpus">000299</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000299</idno><idno type="wicri:Area/Pmc/Curation">000299</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000299</idno><idno type="wicri:Area/Pmc/Checkpoint">000624</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000624</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">CircMarker: a fast and accurate algorithm for circular RNA detection</title><author><name sortKey="Li, Xin" sort="Li, Xin" uniqKey="Li X" first="Xin" last="Li">Xin Li</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:regionArea>Storrs</wicri:regionArea></affiliation></author><author><name sortKey="Chu, Chong" sort="Chu, Chong" uniqKey="Chu C" first="Chong" last="Chu">Chong Chu</name><affiliation wicri:level="1"><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">000000041936754X</institution-id><institution-id institution-id-type="GRID">grid.38142.3c</institution-id><institution>Department of Biomedical Informatics, Harvard Medical School,</institution></institution-wrap>Boston, 02115 MA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Massachusetts</region></placeName><wicri:regionArea>Boston</wicri:regionArea></affiliation></author><author><name sortKey="Pei, Jingwen" sort="Pei, Jingwen" uniqKey="Pei J" first="Jingwen" last="Pei">Jingwen Pei</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:regionArea>Storrs</wicri:regionArea></affiliation></author><author><name sortKey="M Ndoiu, Ion" sort="M Ndoiu, Ion" uniqKey="M Ndoiu I" first="Ion" last="M Ndoiu">Ion M Ndoiu</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:regionArea>Storrs</wicri:regionArea></affiliation></author><author><name sortKey="Wu, Yufeng" sort="Wu, Yufeng" uniqKey="Wu Y" first="Yufeng" last="Wu">Yufeng Wu</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:regionArea>Storrs</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC Genomics</title><idno type="eISSN">1471-2164</idno><imprint><date when="2018">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3’ and 5’ and forms the so-called circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility of some diseases. During the past several years, multiple experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circular RNA detection have been developed as well, these tools are based on reads mapping may miss many circular RNA. Also, existing tools are slow for large data due to their dependence on reads mapping.</p></sec><sec><title>Method</title><p>In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create the k-mer table for circular RNA detection.</p></sec><sec><title>Results</title><p>Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets.</p></sec><sec><title>Conclusions</title><p>We develop a new circular RNA detection method called CircMarker based on k-mer analysis. Our results on both simulation data and real data demonstrate that CircMarker runs much faster and can find more circular RNA with higher consensus-based sensitivity and high accuracy ratio compared with existing tools.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (10.1186/s12864-018-4926-0) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Nigro, Jm" uniqKey="Nigro J">JM Nigro</name></author><author><name sortKey="Cho, Kr" uniqKey="Cho K">KR Cho</name></author><author><name sortKey="Fearon, Er" uniqKey="Fearon E">ER Fearon</name></author><author><name sortKey="Kern, Se" uniqKey="Kern S">SE Kern</name></author><author><name sortKey="Ruppert, Jm" uniqKey="Ruppert J">JM Ruppert</name></author><author><name sortKey="Oliner, Jd" uniqKey="Oliner J">JD Oliner</name></author><author><name sortKey="Kinzler, 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<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Genomics</journal-id><journal-id journal-id-type="iso-abbrev">BMC Genomics</journal-id><journal-title-group><journal-title>BMC Genomics</journal-title></journal-title-group><issn pub-type="epub">1471-2164</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">30367583</article-id><article-id pub-id-type="pmc">6101086</article-id><article-id pub-id-type="publisher-id">4926</article-id><article-id pub-id-type="doi">10.1186/s12864-018-4926-0</article-id><article-categories><subj-group subj-group-type="heading"><subject>Software</subject></subj-group></article-categories><title-group><article-title>CircMarker: a fast and accurate algorithm for circular RNA detection</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Li</surname><given-names>Xin</given-names></name><address><email>xin.li@uconn.edu</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Chu</surname><given-names>Chong</given-names></name><address><email>chong_chu@hms.harvard.edu</email></address><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Pei</surname><given-names>Jingwen</given-names></name><address><email>jingwen.pei@uconn.edu</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Măndoiu</surname><given-names>Ion</given-names></name><address><email>ion.mandoiu@uconn.edu</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Wu</surname><given-names>Yufeng</given-names></name><address><email>yufeng.wu@uconn.edu</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0860 4915</institution-id><institution-id institution-id-type="GRID">grid.63054.34</institution-id><institution>Department of Computer Science and Engineering, University of Connecticut,</institution></institution-wrap>Storrs, 06269 CT USA</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="ISNI">000000041936754X</institution-id><institution-id institution-id-type="GRID">grid.38142.3c</institution-id><institution>Department of Biomedical Informatics, Harvard Medical School,</institution></institution-wrap>Boston, 02115 MA USA</aff></contrib-group><pub-date pub-type="epub"><day>13</day><month>8</month><year>2018</year></pub-date><pub-date pub-type="pmc-release"><day>13</day><month>8</month><year>2018</year></pub-date><pub-date pub-type="collection"><year>2018</year></pub-date><volume>19</volume><issue>Suppl 6</issue><issue-sponsor>Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.</issue-sponsor><elocation-id>572</elocation-id><permissions><copyright-statement>© The Author(s) 2018</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold> This article is distributed under the terms of the Creative Commons Attribution 4.0 International 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 use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3’ and 5’ and forms the so-called circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility of some diseases. During the past several years, multiple experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circular RNA detection have been developed as well, these tools are based on reads mapping may miss many circular RNA. Also, existing tools are slow for large data due to their dependence on reads mapping.</p></sec><sec><title>Method</title><p>In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create the k-mer table for circular RNA detection.</p></sec><sec><title>Results</title><p>Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets.</p></sec><sec><title>Conclusions</title><p>We develop a new circular RNA detection method called CircMarker based on k-mer analysis. Our results on both simulation data and real data demonstrate that CircMarker runs much faster and can find more circular RNA with higher consensus-based sensitivity and high accuracy ratio compared with existing tools.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (10.1186/s12864-018-4926-0) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Circular RNA</kwd><kwd>High-throughput sequencing</kwd><kwd>Genomics</kwd><kwd>RNA-Seq</kwd></kwd-group><conference><conf-name>13th International Symposium on Bioinformatics Research and Applications (ISBRA 2017)</conf-name><conf-loc>Honolulu, Hawaii, USA</conf-loc><conf-date>30 May - 2 June 2017</conf-date></conference><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2018</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc><affiliations><list><country><li>États-Unis</li></country><region><li>Connecticut</li><li>Massachusetts</li></region></list><tree><country name="États-Unis"><region name="Connecticut"><name sortKey="Li, Xin" sort="Li, Xin" uniqKey="Li X" first="Xin" last="Li">Xin Li</name></region><name sortKey="Chu, Chong" sort="Chu, Chong" uniqKey="Chu C" first="Chong" last="Chu">Chong Chu</name><name sortKey="M Ndoiu, Ion" sort="M Ndoiu, Ion" uniqKey="M Ndoiu I" first="Ion" last="M Ndoiu">Ion M Ndoiu</name><name sortKey="Pei, Jingwen" sort="Pei, Jingwen" uniqKey="Pei J" first="Jingwen" last="Pei">Jingwen Pei</name><name sortKey="Wu, Yufeng" sort="Wu, Yufeng" uniqKey="Wu Y" first="Yufeng" last="Wu">Yufeng Wu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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