Paper Info
Title
Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization.
Abstract
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.Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/.
Year
DOI
Venue
2011
10.1093/bioinformatics/btq635
Bioinformatics
Keywords
Field
DocType
supplementary data,absolute copy number,control-free copy number alteration,number profile,sample data,gc-content normalization,deep-sequencing data,cancer study,control-free copy number caller,cancer cell,control dataset,algorithms,genomics,gc content
Deep sequencing,Normalization (statistics),Source code,Computer science,Genomics,Bioinformatics,GC-content,Copy number analysis,Copy Number Alteration
Journal
Volume
Issue
ISSN
27
2
1367-4811
Citations 
PageRank 
References 
31
3.34
2
Authors
7
Name
Order
Citations
PageRank
Valentina Boeva117214.86
Andrei Zinovyev228227.30
Bleakley, Kevin328516.82
Jean-philippe Vert42754158.52
Isabelle Janoueix-Lerosey512010.88
Olivier Delattre612710.75
Emmanuel Barillot7950165.00