cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data

被引：21

作者：

Bellos, Evangelos ^{[1
]}

Johnson, Michael R. ^{[2
]}

Coin, Lachlan J. M. ^{[3
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Dept Epidemiol & Biostat, London W2 1PG, England

[2] Univ London Imperial Coll Sci Technol & Med, Dept Clin Neurosci, London W6 8RF, England

[3] Univ London Imperial Coll Sci Technol & Med, Dept Genom Common Dis, London W12 0NN, England

来源：

GENOME BIOLOGY | 2012年 / 13卷 / 12期

基金：

英国生物技术与生命科学研究理事会;

关键词：

STRUCTURAL VARIATION; DELETIONS; WILD; SNPS; MAP;

D O I：

10.1186/gb-2012-13-12-r120

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Recent advances in sequencing technologies provide the means for identifying copy number variation (CNV) at an unprecedented resolution. A single next-generation sequencing experiment offers several features that can be used to detect CNV, yet current methods do not incorporate all available signatures into a unified model. cnvHiTSeq is an integrative probabilistic method for CNV discovery and genotyping that jointly analyzes multiple features at the population level. By combining evidence from complementary sources, cnvHiTSeq achieves high genotyping accuracy and a substantial improvement in CNV detection sensitivity over existing methods, while maintaining a low false discovery rate. cnvHiTSeq is available at http://sourceforge.net/projects/cnvhitseq.

引用

页数：11

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