Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms
Identifieur interne : 001461 ( Main/Exploration ); précédent : 001460; suivant : 001462Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms
Auteurs : Rob Patro [États-Unis] ; Stephen M. Mount [États-Unis] ; Carl Kingsford [États-Unis]Source :
- Nature biotechnology [ 1087-0156 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
- analyse : Isoformes d'ARN.
- génétique : Isoformes d'ARN.
- Algorithmes, Analyse de séquence d'ARN, Biochimie de l'encéphale, Biologie informatique, Humains, Isoformes d'ARN, Logiciel, Modèles biologiques.
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : RNA Isoforms.
- chemical , chemistry : RNA Isoforms.
- chemical , genetics : RNA Isoforms.
- methods : Computational Biology, Sequence Analysis, RNA.
- Algorithms, Brain Chemistry, Humans, Models, Biological, Software.
Abstract
We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.
Url:
DOI: 10.1038/nbt.2862
PubMed: 24752080
PubMed Central: 4077321
Affiliations:
- États-Unis
- Maryland, Pennsylvanie
- College Park (Maryland), Pittsburgh
- Université Carnegie-Mellon, Université du Maryland
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Le document en format XML
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Mount</name><affiliation wicri:level="4"><nlm:aff id="A2">Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland</wicri:regionArea><placeName><region type="state">Maryland</region><settlement type="city">College Park (Maryland)</settlement></placeName><orgName type="university">Université du Maryland</orgName></affiliation><affiliation wicri:level="4"><nlm:aff id="A3">Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland</wicri:regionArea><placeName><region type="state">Maryland</region><settlement type="city">College Park (Maryland)</settlement></placeName><orgName type="university">Université du Maryland</orgName></affiliation></author><author><name sortKey="Kingsford, Carl" sort="Kingsford, Carl" uniqKey="Kingsford C" first="Carl" last="Kingsford">Carl Kingsford</name><affiliation wicri:level="4"><nlm:aff id="A1">Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author></analytic><series><title level="j">Nature biotechnology</title><idno type="ISSN">1087-0156</idno><idno type="eISSN">1546-1696</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Brain Chemistry</term><term>Computational Biology (methods)</term><term>Humans</term><term>Models, Biological</term><term>RNA Isoforms (analysis)</term><term>RNA Isoforms (chemistry)</term><term>RNA Isoforms (genetics)</term><term>Sequence Analysis, RNA (methods)</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ARN ()</term><term>Biochimie de l'encéphale</term><term>Biologie informatique ()</term><term>Humains</term><term>Isoformes d'ARN ()</term><term>Isoformes d'ARN (analyse)</term><term>Isoformes d'ARN (génétique)</term><term>Logiciel</term><term>Modèles biologiques</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>RNA Isoforms</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>RNA Isoforms</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>RNA Isoforms</term></keywords><keywords scheme="MESH" qualifier="analyse" xml:lang="fr"><term>Isoformes d'ARN</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Isoformes d'ARN</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>Sequence Analysis, RNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Brain Chemistry</term><term>Humans</term><term>Models, Biological</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ARN</term><term>Biochimie de l'encéphale</term><term>Biologie informatique</term><term>Humains</term><term>Isoformes d'ARN</term><term>Logiciel</term><term>Modèles biologiques</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Soneson, C" uniqKey="Soneson C">C Soneson</name></author><author><name sortKey="Delorenzi, M" uniqKey="Delorenzi M">M Delorenzi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Roychowdhury, S" uniqKey="Roychowdhury S">S Roychowdhury</name></author><author><name sortKey="Iyer, Mk" uniqKey="Iyer M">MK Iyer</name></author><author><name sortKey="Robinson, Dr" uniqKey="Robinson D">DR Robinson</name></author><author><name sortKey="Lonigro, Rj" uniqKey="Lonigro R">RJ Lonigro</name></author><author><name sortKey="Wu, Y M" uniqKey="Wu Y">Y-M Wu</name></author><author><name sortKey="Cao, X" uniqKey="Cao X">X Cao</name></author><author><name sortKey="Kalyana 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