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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 (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Published: 22 December 2012

Additional files

Additional file 1:

Supplementary material. This file contains Figures S1, S2, S3 and S4, and Tables S1, S2, S3, S4 and S5. Figure S1 presents a schematic of our pre-processing pipeline. Figure S2 presents the length distribution of our CNV calls. Figure S3 presents the cumulative length distribution of cnvHiTSeq calls that were validated with array-CGH data. Figure S4 presents a heatmap of the genotyping concordance between cnvHiTSeq and a benchmark dataset. Table S1 presents the array-CGH validation results. Table S2 describes the computational requirements of our pipeline. Table S3 presents a comparison of our deletion calls with those of Genome STRiP for sample NA12878. Table S4 presents a comparison of our Mendelian inconsistency results for different criteria. Table S5 presents a comparison of the sensitivity of various methods on low-coverage samples.

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