Assessment of transcript reconstruction methods for RNA-seq Academic Article uri icon

Overview

MeSH Major

  • Computational Biology
  • RNA Splicing
  • Sequence Analysis, RNA

abstract

  • 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.

authors

publication date

  • December 2013

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3851240

Digital Object Identifier (DOI)

  • 10.1038/nmeth.2714

PubMed ID

  • 24185837

Additional Document Info

start page

  • 1177

end page

  • 84

volume

  • 10

number

  • 12