Journal article

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

Evangelos Bellos, Michael R Johnson, Lachlan JM Coin

Genome Biology | BMC | Published : 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. cnvHi..

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University of Melbourne Researchers

Grants

Awarded by UK Biotechnology and Biological Sciences Research Council (BBSRC)


Awarded by Biotechnology and Biological Sciences Research Council


Funding Acknowledgements

This study makes use of publicly available data generated by the 1000 Genomes Project. We also acknowledge the Genome Structural Variation Consortium (PIs Nigel Carter, Matthew Hurles, Charles Lee and Stephen Scherer) for pre-publication access to their CNV discovery and genotyping data. This work was supported by grant BB/H024808/1 from the UK Biotechnology and Biological Sciences Research Council (BBSRC). The authors would also like to thank Simon Burbidge of the Imperial College High Performance Computing Service for his assistance.