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HadoopCNV: A dynamic programming imputation algorithm to detect copy number variants from sequencing data

Hui Yang, Gary Chen, Leandro Lima, Han Fang, Laura Jimenez, Mingyao Li, Gholson J Lyon, Max He, Kai Wang
doi: https://doi.org/10.1101/124339
Hui Yang
1Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
2Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90007, USA
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Gary Chen
3Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
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Leandro Lima
1Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
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Han Fang
4Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, New York, NY 11797, USA, USA
5Department of Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA, 11794
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Laura Jimenez
4Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, New York, NY 11797, USA, USA
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Mingyao Li
6Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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Gholson J Lyon
4Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, New York, NY 11797, USA, USA
7Utah Foundation for Biomedical Research, 150 S 100 W, Provo, UT, 84601, USA
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Max He
8Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
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  • For correspondence: maxm.he@outlook.com kw2701@cumc.columbia.edu
Kai Wang
1Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
7Utah Foundation for Biomedical Research, 150 S 100 W, Provo, UT, 84601, USA
9Institute for Genomic Medicine, Columbia University Medical Center, New York, NY 10032, USA
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  • For correspondence: maxm.he@outlook.com kw2701@cumc.columbia.edu
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ABSTRACT

BACKGROUND Whole-genome sequencing (WGS) data may be used to identify copy number variations (CNVs). Existing CNV detection methods mostly rely on read depth or alignment characteristics (paired-end distance and split reads) to infer gains/losses, while neglecting allelic intensity ratios and cannot quantify copy numbers. Additionally, most CNV callers are not scalable to handle a large number of WGS samples.

METHODS To facilitate large-scale and rapid CNV detection from WGS data, we developed a Dynamic Programming Imputation (DPI) based algorithm called HadoopCNV, which infers copy number changes through both allelic frequency and read depth information. Our implementation is built on the Hadoop framework, enabling multiple compute nodes to work in parallel.

RESULTS Compared to two widely used tools – CNVnator and LUMPY, HadoopCNV has similar or better performance on both simulated data sets and real data on the NA12878 individual. Additionally, analysis on a 10-member pedigree showed that HadoopCNV has a Mendelian precision that is similar or better than other tools. Furthermore, HadoopCNV can accurately infer loss of heterozygosity (LOH), while other tools cannot. HadoopCNV requires only 1.6 hours for a human genome with 30X coverage, on a 32-node cluster, with a linear relationship between speed improvement and the number of nodes. We further developed a method to combine HadoopCNV and LUMPY result, and demonstrated that the combination resulted in better performance than any individual tools.

CONCLUSIONS The combination of high-resolution, allele-specific read depth from WGS data and Hadoop framework can result in efficient and accurate detection of CNVs.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 05, 2017.
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HadoopCNV: A dynamic programming imputation algorithm to detect copy number variants from sequencing data
Hui Yang, Gary Chen, Leandro Lima, Han Fang, Laura Jimenez, Mingyao Li, Gholson J Lyon, Max He, Kai Wang
bioRxiv 124339; doi: https://doi.org/10.1101/124339
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HadoopCNV: A dynamic programming imputation algorithm to detect copy number variants from sequencing data
Hui Yang, Gary Chen, Leandro Lima, Han Fang, Laura Jimenez, Mingyao Li, Gholson J Lyon, Max He, Kai Wang
bioRxiv 124339; doi: https://doi.org/10.1101/124339

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