Comrad: detection of expressed rearrangements by integrated analysis of RNA-Seq and low coverage genome sequence data. Academic Article uri icon

Overview

abstract

  • MOTIVATION: Comrad is a novel algorithmic framework for the integrated analysis of RNA-Seq and whole genome shotgun sequencing (WGSS) data for the purposes of discovering genomic rearrangements and aberrant transcripts. The Comrad framework leverages the advantages of both RNA-Seq and WGSS data, providing accurate classification of rearrangements as expressed or not expressed and accurate classification of the genomic or non-genomic origin of aberrant transcripts. A major benefit of Comrad is its ability to accurately identify aberrant transcripts and associated rearrangements using low coverage genome data. As a result, a Comrad analysis can be performed at a cost comparable to that of two RNA-Seq experiments, significantly lower than an analysis requiring high coverage genome data. RESULTS: We have applied Comrad to the discovery of gene fusions and read-throughs in prostate cancer cell line C4-2, a derivative of the LNCaP cell line with androgen-independent characteristics. As a proof of concept, we have rediscovered in the C4-2 data 4 of the 6 fusions previously identified in LNCaP. We also identified six novel fusion transcripts and associated genomic breakpoints, and verified their existence in LNCaP, suggesting that Comrad may be more sensitive than previous methods that have been applied to fusion discovery in LNCaP. We show that many of the gene fusions discovered using Comrad would be difficult to identify using currently available techniques. AVAILABILITY: A C++ and Perl implementation of the method demonstrated in this article is available at http://compbio.cs.sfu.ca/.

publication date

  • April 9, 2011

Research

keywords

  • Algorithms
  • Chromosome Breakpoints
  • Gene Fusion
  • Sequence Analysis, DNA
  • Sequence Analysis, RNA

Identity

Scopus Document Identifier

  • 79957875348

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btr184

PubMed ID

  • 21478487

Additional Document Info

volume

  • 27

issue

  • 11