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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

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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