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

Evangelos Bellos1, Michael R Johnson2 and Lachlan J M Coin3*

Author Affiliations

1 Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK

2 Department of Clinical Neurosciences Imperial College London, London W6 8RF, UK

3 Department of Genomics of Common Disease, Imperial College London, London W12 0NN, UK

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Genome Biology 2012, 13:R120  doi:10.1186/gb-2012-13-12-r120


This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Published: 22 December 2012

Abstract

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 webcite