COSINE: non-seeding method for mapping long noisy sequences
Identifieur interne : 000E71 ( Main/Exploration ); précédent : 000E70; suivant : 000E72COSINE: non-seeding method for mapping long noisy sequences
Auteurs : Pegah Tootoonchi Afshar [États-Unis] ; Wing Hung Wong [États-Unis]Source :
- Nucleic Acids Research [ 0305-1048 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- methods : Computational Biology, High-Throughput Nucleotide Sequencing, Sequence Alignment.
- statistics & numerical data : High-Throughput Nucleotide Sequencing.
- Algorithms, Base Sequence, Reproducibility of Results, Software.
Abstract
Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors. COSINE computes the context similarity of two stretches of nucleobases given the similarity over distributions of their short
Url:
DOI: 10.1093/nar/gkx511
PubMed: 28586438
PubMed Central: 5737678
Affiliations:
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xml:lang="en" level="a" type="main">COSINE: non-seeding method for mapping long noisy sequences</title><author><name sortKey="Afshar, Pegah Tootoonchi" sort="Afshar, Pegah Tootoonchi" uniqKey="Afshar P" first="Pegah Tootoonchi" last="Afshar">Pegah Tootoonchi Afshar</name><affiliation wicri:level="2"><nlm:aff id="AFF1">Department of Electrical Engineering, School of Engineering, Stanford University, Stanford, CA 94305, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, School of Engineering, Stanford University, Stanford, CA 94305</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Wong, Wing Hung" sort="Wong, Wing Hung" uniqKey="Wong W" first="Wing Hung" last="Wong">Wing Hung Wong</name><affiliation wicri:level="2"><nlm:aff id="AFF2">Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></analytic><series><title level="j">Nucleic Acids Research</title><idno type="ISSN">0305-1048</idno><idno type="eISSN">1362-4962</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Base Sequence</term><term>Computational Biology (methods)</term><term>High-Throughput Nucleotide Sequencing (methods)</term><term>High-Throughput Nucleotide Sequencing (statistics & numerical data)</term><term>Reproducibility of Results</term><term>Sequence Alignment (methods)</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences ()</term><term>Biologie informatique ()</term><term>Logiciel</term><term>Reproductibilité des résultats</term><term>Séquence nucléotidique</term><term>Séquençage nucléotidique à haut débit ()</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>High-Throughput Nucleotide Sequencing</term><term>Sequence Alignment</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>High-Throughput Nucleotide Sequencing</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Base Sequence</term><term>Reproducibility of Results</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences</term><term>Biologie informatique</term><term>Logiciel</term><term>Reproductibilité des résultats</term><term>Séquence nucléotidique</term><term>Séquençage nucléotidique à haut débit</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Abstract</title><p>Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors. COSINE computes the context similarity of two stretches of nucleobases given the similarity over distributions of their short <italic>k</italic>-mers (<italic>k</italic> = 3–4) along the sequences. The results on simulated and real data show that COSINE achieves high sensitivity and specificity under a wide range of read accuracies. When the error rate is high, COSINE can offer substantial advantages over existing alignment methods.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Zhang, J" uniqKey="Zhang J">J. Zhang</name></author><author><name sortKey="Chiodini, R" uniqKey="Chiodini R">R. Chiodini</name></author><author><name sortKey="Badr, A" uniqKey="Badr A">A. Badr</name></author><author><name sortKey="Zhangd, G" uniqKey="Zhangd G">G. 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