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ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads

Author

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  • Christopher A Miller
  • Oliver Hampton
  • Cristian Coarfa
  • Aleksandar Milosavljevic

Abstract

Copy number alterations are important contributors to many genetic diseases, including cancer. We present the readDepth package for R, which can detect these aberrations by measuring the depth of coverage obtained by massively parallel sequencing of the genome. In addition to achieving higher accuracy than existing packages, our tool runs much faster by utilizing multi-core architectures to parallelize the processing of these large data sets. In contrast to other published methods, readDepth does not require the sequencing of a reference sample, and uses a robust statistical model that accounts for overdispersed data. It includes a method for effectively increasing the resolution obtained from low-coverage experiments by utilizing breakpoint information from paired end sequencing to do positional refinement. We also demonstrate a method for inferring copy number using reads generated by whole-genome bisulfite sequencing, thus enabling integrative study of epigenomic and copy number alterations. Finally, we apply this tool to two genomes, showing that it performs well on genomes sequenced to both low and high coverage. The readDepth package runs on Linux and MacOSX, is released under the Apache 2.0 license, and is available at http://code.google.com/p/readdepth/.

Suggested Citation

  • Christopher A Miller & Oliver Hampton & Cristian Coarfa & Aleksandar Milosavljevic, 2011. "ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-7, January.
  • Handle: RePEc:plo:pone00:0016327
    DOI: 10.1371/journal.pone.0016327
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    Cited by:

    1. Yang Guo & Shuzhen Wang & A. K. Alvi Haque & Xiguo Yuan, 2022. "WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
    2. Xue-Ke Zhao & Pengwei Xing & Xin Song & Miao Zhao & Linxuan Zhao & Yonglong Dang & Ling-Ling Lei & Rui-Hua Xu & Wen-Li Han & Pan-Pan Wang & Miao-Miao Yang & Jing-Feng Hu & Kan Zhong & Fu-You Zhou & Xu, 2021. "Focal amplifications are associated with chromothripsis events and diverse prognoses in gastric cardia adenocarcinoma," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Ji Tieming & Chen Jie, 2015. "Modeling the next generation sequencing read count data for DNA copy number variant study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(4), pages 361-374, August.
    4. Love Michael I. & Myšičková Alena & Sun Ruping & Kalscheuer Vera & Vingron Martin & Haas Stefan A., 2011. "Modeling Read Counts for CNV Detection in Exome Sequencing Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-30, November.
    5. Xian F Mallory & Mohammadamin Edrisi & Nicholas Navin & Luay Nakhleh, 2020. "Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-24, July.

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