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

Overview

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

  • Sboner, Andrea
  • Steijger, Tamara
  • Abril, Josep F
  • Engström, Pär G
  • Kokocinski, Felix
  • Hubbard, Tim J
  • Guigó, Roderic
  • Harrow, Jennifer
  • Bertone, Paul

publication date

  • November 3, 2013

Research

keywords

  • Computational Biology
  • RNA Splicing
  • Sequence Analysis, RNA

Identity

PubMed Central ID

  • PMC3851240

Scopus Document Identifier

  • 84888871954

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/bts635

PubMed ID

  • 24185837

Additional Document Info

volume

  • 10

issue

  • 12