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Short Communication | Open Access

AluScanCNV2: An R package for copy number variation calling and cancer risk prediction with next-generation sequencing data

Taobo Hua, Si Chena, Ata Ullaha, Hong Xuea,b,( )
Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China

Peer review under responsibility of Chongqing Medical University.

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Abstract

The usage of next-generation sequencing (NGS) to detect copy number variation (CNV) is widely accepted in cancer research. Based on an AluScanCNV software developed by us previously, an AluScanCNV2 software has been developed in the present study as an R package that performs CNV detection from NGS data obtained through AluScan, wholegenome sequencing or other targeted NGS platforms. Its applications would include the expedited usage of somatic CNVs for cancer subtyping, and usage of recurrent germline CNVs to perform machine learning-assisted prediction of a test subject’s susceptibility to cancer.

Keywords

Machine learningBioinformaticsAluScanCancer subtypingCancer predisposition

References

1

Zarrei M, MacDonald JR, Merico D, Scherer SW. A copy number variation map of the human genome. Nat Rev Genet. 2015;16(3):172-183.
Crossref Google Scholar
2

Nagao Y. Copy number variations play important roles in heredity of common diseases: a novel method to calculate heritability of a polymorphism. Sci Rep. 2015;5:17156. https://doi.org/10.1038/srep17156.
Crossref Google Scholar
3

Xie C, Tammi MT. CNV-seq, a new method to detect copy number variation using high-throughput sequencing. BMC Bioinf. 2009;10:80. https://doi.org/10.1186/1471-2105-10-80.
Crossref Google Scholar
4

Yang JF, Ding XF, Chen L, et al. Copy number variation analysis based on AluScan sequences. J Clin Bioinf. 2014;4(1):15.
Crossref Google Scholar
5

Ding X, Tsang SY, Ng SK, Xue H. Application of machine learning to development of copy number variation-based prediction of cancer risk. Genomics Insights. 2014;7(Supplementary Files 15002):1-11.
Crossref Google Scholar
6

Kuhn M. Caret: Classification and Regression Training. Astrophysics Source Code Library; 2015.
Google Scholar
7

Mei L, Ding X, Tsang SY, et al. AluScan: a method for genome-wide scanning of sequence and structure variations in the human genome. BMC Genom. 2011;12:564. https://doi.org/10.1186/1471-2164-12-564.
Crossref Google Scholar
8

Kumar Y, Yang J, Hu T, et al. Massive interstitial copy-neutral loss-of-heterozygosity as evidence for cancer being a disease of the DNA-damage response. Bmc Med Genomics. 2015;8:42. https://doi.org/10.1186/s12920-015-0104-2.
Crossref Google Scholar
9

Hu T, Kumar Y, Shazia I, et al. Forward-reverse mutations in stages of cancer development. bioRxiv. 2017:198309. https://doi.org/10.1101/198309.
Crossref Google Scholar
10

Genomes Project C, Auton A, Brooks LD, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68-74.
Crossref Google Scholar
11

Greenblum S, Carr R, Borenstein E. Extensive strain-level copynumber variation across human gut microbiome species. Cell. 2015;160(4):583-594.
Crossref Google Scholar
Genes & Diseases
Volume 6 Issue 1,
March 2019
Pages 43-46
DOI: 10.1016/j.gendis.2018.09.001
Cite this article:
Hu T, Chen S, Ullah A, et al. AluScanCNV2: An R package for copy number variation calling and cancer risk prediction with next-generation sequencing data. Genes & Diseases, 2019, 6(1): 43-46. https://doi.org/10.1016/j.gendis.2018.09.001

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Received: 06 June 2018
Accepted: 04 September 2018
Published: 08 September 2018
© 2018, Chongqing Medical University.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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