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

被引：157

作者：

Boeva, Valentina ^{[1
,2
,3
,4
]}

Zinovyev, Andrei ^{[1
,2
,3
]}

Bleakley, Kevin ^{[1
,2
,3
]}

Vert, Jean-Philippe ^{[1
,2
,3
]}

Janoueix-Lerosey, Isabelle ^{[1
,4
]}

Delattre, Olivier ^{[1
,4
]}

Barillot, Emmanuel ^{[1
,2
,3
]}

机构：

[1] Inst Curie, F-75248 Paris, France

[2] INSERM, U900, F-75248 Paris, France

[3] Mines ParisTech, F-77300 Fontainebleau, France

[4] INSERM, U830, F-75248 Paris, France

来源：

BIOINFORMATICS | 2011年 / 27卷 / 02期

关键词：

GENOME;

D O I：

10.1093/bioinformatics/btq635

中图分类号：

Q5 [生物化学];

学科分类号：

070307 [化学生物学];

摘要：

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.

引用

页码：268 / 269

页数：2

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