Assessment of transcript reconstruction methods for RNA-seq

TitleAssessment of transcript reconstruction methods for RNA-seq
Publication TypeJournal Article
Year of Publication2013
AuthorsSteijger T, Abril JF, Engstr, Kokocinski F, Consortium TRGASP, Abril JF, Akerman M, Alioto T, Ambrosini G, Antonarakis SE, Behr J, Bertone P, Bohnert R, Bucher P, Cloonan N, Derrien T, Djebali S, Du J, Dudoit S, Engstr, Gerstein M, Gingeras TR, Gonzalez D, Grimmond SM, Guig, Habegger L, Harrow J, Hubbard TJ, Iseli C, Jean G, Kahles A, Kokocinski F, Lagarde J, Leng J, Lefebvre G, Lewis S, Mortazavi A, Niermann P, R, Reymond A, Ribeca P, Richard H, Rougemont J, Rozowsky J, Sammeth M, Sboner A, Schulz MH, Searle SM, Solorzano ND, Solovyev V, Stanke M, Steijger T, Stevenson BJ, Stockinger H, Valsesia A, Weese D, White S, Wold BJ, Wu J, Wu TD, Zeller G, Zerbino D, Zhang MQ, Hubbard TJ, Guig, Harrow J, Bertone P
JournalNat. Methods
Date PublishedNov

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.