原文链接
参考文献
RNA-seq is a recent and immensely popular technology for cataloging and comparing gene expression. Two papers from the international RGASP consortium report on large-scale competitions to identify the best algorithms for RNA-seq analysis, with surprising variability in the results.
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.
