CircMarker: a fast and accurate algorithm for circular RNA detection
Identifieur interne : 000A43 ( Main/Exploration ); précédent : 000A42; suivant : 000A44CircMarker: 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.
Descripteurs français
- KwdFr :
- MESH :
- analyse : ARN.
- ARN, Algorithmes, Analyse de séquence d'ARN, Humains, Logiciel.
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : RNA.
- chemical , chemistry : RNA.
- methods : Sequence Analysis, RNA.
- Algorithms, Humans, RNA, Circular, Software.
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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type="wicri:Area/Main/Exploration">000A43</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><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Humans</term><term>RNA (analysis)</term><term>RNA (chemistry)</term><term>RNA, Circular</term><term>Sequence Analysis, RNA (methods)</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ARN ()</term><term>ARN (analyse)</term><term>Algorithmes</term><term>Analyse de séquence d'ARN ()</term><term>Humains</term><term>Logiciel</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>RNA</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>RNA</term></keywords><keywords scheme="MESH" qualifier="analyse" xml:lang="fr"><term>ARN</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Sequence Analysis, RNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Humans</term><term>RNA, Circular</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>ARN</term><term>Algorithmes</term><term>Analyse de séquence d'ARN</term><term>Humains</term><term>Logiciel</term></keywords></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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Li</name></author><author><name sortKey="Pesavento, Pa" uniqKey="Pesavento P">PA Pesavento</name></author><author><name sortKey="Leutenegger, Cm" uniqKey="Leutenegger C">CM Leutenegger</name></author><author><name sortKey="Estrada, M" uniqKey="Estrada M">M Estrada</name></author><author><name sortKey="Coffey, Ll" uniqKey="Coffey L">LL Coffey</name></author><author><name sortKey="Naccache, Sn" uniqKey="Naccache S">SN Naccache</name></author><author><name sortKey="Samayoa, E" uniqKey="Samayoa E">E Samayoa</name></author><author><name sortKey="Chiu, C" uniqKey="Chiu C">C Chiu</name></author><author><name sortKey="Qiu, J" uniqKey="Qiu J">J Qiu</name></author><author><name sortKey="Wang, C" uniqKey="Wang C">C Wang</name></author></analytic></biblStruct></listBibl></div1></back></TEI><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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|texte= CircMarker: a fast and accurate algorithm for circular RNA detection
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:30367583" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a MersV1
| This area was generated with Dilib version V0.6.33. |  |