000196476 001__ 196476
000196476 005__ 20190316235838.0
000196476 022__ $$a1548-7091
000196476 02470 $$2ISI$$a000327698100016
000196476 02470 $$2PMID$$a24185837
000196476 0247_ $$2doi$$a10.1038/NMETH.2714
000196476 037__ $$aARTICLE
000196476 245__ $$aAssessment of transcript reconstruction methods for RNA-seq
000196476 269__ $$a2013
000196476 260__ $$bNature Publishing Group$$c2013
000196476 336__ $$aJournal Articles
000196476 520__ $$aWe 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
000196476 700__ $$aSteijger, Tamara
000196476 700__ $$aAbril, Josep F.
000196476 700__ $$aEngstrom, Par G.
000196476 700__ $$aKokocinski, Felix
000196476 700__ $$aHubbard, Tim J.
000196476 700__ $$aGuigo, Roderic
000196476 700__ $$aHarrow, Jennifer
000196476 700__ $$aBertone, Paul
000196476 710__ $$aRGASP Consortium
000196476 773__ $$j10$$tNature Methods$$k12$$q1177–1184
000196476 8564_ $$uhttps://infoscience.epfl.ch/record/196476/files/nmeth.2714.pdf$$zPublisher's version$$s714345$$yPublisher's version
000196476 909C0 $$xU11780$$0252244$$pGR-BUCHER
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000196476 917Z8 $$x182396
000196476 917Z8 $$x182396
000196476 937__ $$aEPFL-ARTICLE-196476
000196476 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000196476 980__ $$aARTICLE