Published in

Oxford University Press (OUP), Bioinformatics, 2(27), p. 268-269

DOI: 10.1093/bioinformatics/btq635

Links

Tools

Export citation

Search in Google Scholar

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

Journal article published in 2010 by Valentina Boeva ORCID, Kevin Bleakley, Andrei Zinovyev, Isabelle Janoueix-Lerosey, Jean-Philippe Vert ORCID, Olivier Delattre, Emmanuel Barillot
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

Abstract

Summary: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs.