Assessment of transcript reconstruction methods for RNA-seq

• Published in: Nature methods Vol. 10; no. 12; pp. 1177 - 1184
• Main Authors: Steijger, Tamara, Abril, Josep F, Engström, Pär G, Kokocinski, Felix, Hubbard, Tim J, Guigó, Roderic, Harrow, Jennifer, Bertone, Paul
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.12.2013; Nature Publishing Group
• Subjects: 631/114/2785; Algorithms; analysis; Animals; Bioinformatics; Biologia computacional; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; Caenorhabditis elegans; Computational biology; Computational Biology - methods; Computer Science; Computers; Drosophila melanogaster; Empalmament (Genètica); Exon (Molecular genetics); Exons; Gene expression; Gene Expression Profiling; Genetic research; Genetic transcription; Genome; Humans; Introns; Life Sciences; Limiting factors; Methods; Proteomics; Quantitative Methods; Ribonucleic acid; RNA; RNA sequencing; RNA Splice Sites; RNA Splicing; RNA, Messenger - metabolism; Sequence Analysis, RNA - methods; Software
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/nmeth.2714

The RGASP consortium compared 25 RNA-seq analysis programs in their ability to identify exons, reconstruct transcripts and quantify expression levels. Assembly of isoforms and their expression levels in higher eukaryotes remains a challenge. 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.