DeNovoGear: de novo indel and point mutation discovery and phasing

被引：128

作者：

Ramu, Avinash ^{[1
]}

Noordam, Michiel J. ^{[1
]}

Schwartz, Rachel S. ^{[2
]}

Wuster, Arthur ^{[3
]}

Hurles, Matthew E. ^{[3
]}

Cartwright, Reed A. ^{[2
,4
]}

Conrad, Donald F. ^{[1
,5
]}

机构：

[1] Washington Univ, Sch Med, Dept Genet, St Louis, MO 63110 USA

[2] Arizona State Univ, Biodesign Inst, Ctr Evolutionary Med & Informat, Tempe, AZ USA

[3] Wellcome Trust Sanger Inst, Genome Mutat & Genet Dis Grp, Cambridge, England

[4] Arizona State Univ, Sch Life Sci, Tempe, AZ USA

[5] Washington Univ, Dept Pathol & Immunol, Sch Med, St Louis, MO USA

来源：

NATURE METHODS | 2013年 / 10卷 / 10期

基金：

英国惠康基金;

关键词：

GENOME; FRAMEWORK; SPECTRUM; RATES;

D O I：

10.1038/nmeth.2611

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We present DeNovoGear software for analyzing de novo mutations from familial and somatic tissue sequencing data. DeNovoGear uses likelihood-based error modeling to reduce the false positive rate of mutation discovery in exome analysis and fragment information to identify the parental origin of germ-line mutations. We used DeNovoGear on human whole-genome sequencing data to produce a set of predicted de novo insertion and/or deletion (indel) mutations with a 95% validation rate.

引用

页码：985 / +

页数：5

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