Assessment of transcript reconstruction methods for RNA-seq.

T. Steijger; J.F. Abril; P.G. Engström; F. Kokocinski; M. Akerman; T. Alioto; G. Ambrosini; S.E. Antonarakis; J. Behr; P. Bertone; R. Bohnert; P. Bucher; N. Cloonan; T. Derrien; S. Djebali; J. Du; S. Dudoit; M. Gerstein; T.R. Gingeras; D. Gonzalez; S.M. Grimmond; R. Guigó; L. Habegger; J. Harrow; T.J. Hubbard; C. Iseli; G. Jean; A. Kahles; J. Lagarde; J. Leng; G. Lefebvre; S. Lewis; A. Mortazavi; P. Niermann; G. Rätsch; A. Reymond; P. Ribeca; H. Richard; J. Rougemont; J. Rozowsky; M. Sammeth; A. Sboner; M.H. Schulz; S.M. Searle; N.D. Solorzano; V. Solovyev; M. Stanke; B.J. Stevenson; H. Stockinger; A. Valsesia; D. Weese; S. White; B.J. Wold; J. Wu; T.D. Wu; G. Zeller; D. Zerbino; M.Q. Zhang

Assessment of transcript reconstruction methods for RNA-seq.

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Auteurs

Date

2013

Type de document

Articles

Périmètre

Publications

Langue

Anglais

Identifiants

doi: 10.1038/nmeth.2714
issn: 1548-7091

Source

Serveur académique Lausannois

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Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1038/nmeth.2714

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/24185837

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/1548-7105

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_63C268AABFEF2

Collection

Serveur Académique Lausannois

Organisation

Université de Lausanne et CHUV

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info:eu-repo/semantics/openAccess , CC BY-NC-SA 4.0 , https://creativecommons.org/licenses/by-nc-sa/4.0/

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Algorithms; Animals; Caenorhabditis elegans; Computational Biology/methods; Drosophila melanogaster; Exons; Gene Expression Profiling; Genome; Humans; Introns; RNA Splice Sites; RNA Splicing; RNA, Messenger/metabolism; Sequence Analysis, RNA/methods; Software

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T. Steijger et al., « Assessment of transcript reconstruction methods for RNA-seq. », Serveur académique Lausannois, ID : 10.1038/nmeth.2714

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We evaluated 25 protocol variants of 14 independent computational methods for exon identification, transcript reconstruction and expression-level quantification from RNA-seq data. Our results show that most algorithms are able to identify discrete transcript components with high success rates but that assembly of complete isoform structures poses a major challenge even when all constituent elements are identified. Expression-level estimates also varied widely across methods, even when based on similar transcript models. Consequently, the complexity of higher eukaryotic genomes imposes severe limitations on transcript recall and splice product discrimination that are likely to remain limiting factors for the analysis of current-generation RNA-seq data.

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Assessment of transcript reconstruction methods for RNA-seq.

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