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WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering

Author

Listed:
  • Yang Guo

    (The School of Computer Science and Technology, Xidian University, Xi’an 710071, China)

  • Shuzhen Wang

    (The School of Computer Science and Technology, Xidian University, Xi’an 710071, China)

  • A. K. Alvi Haque

    (The School of Computer Science and Technology, Xidian University, Xi’an 710071, China)

  • Xiguo Yuan

    (The School of Computer Science and Technology, Xidian University, Xi’an 710071, China
    Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China)

Abstract

Copy number variation (CNV) detection based on second-generation sequencing technology is the basis of much gene research, but the read depth is affected by mapping errors, repeated reads, and GC bias. The existing methods have low sensitivity to variation regions with a short length and small variation range. Therefore, it is necessary to improve the sensitivity of algorithms to short-variation fragments. This study proposes a new CNV-detection method named WAVECNV to solve this issue. The algorithm uses wavelet clustering to process the read depth and determine the normal cluster and abnormal cluster according to the size of the cluster. Then, according to the distance between genome bins and normal clusters, the outlier of each genome bin is evaluated. Finally, a statistical model is established, and the p -value test is used for calling CNVs. Through this method, the information of the short variation region is retained. WAVECNV was tested and compared with peer methods in terms of simulated data and real cancer-sequencing data. The results show that the sensitivity of WAVECNV is better than the existing methods. It also has high precision in data with low purity and coverage. In real data experiments, WAVECNV can detect more cancer genes than existing methods. Therefore, this method can be regarded as a conventional method in the field of genomic mutation analysis of cancer samples.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2151-:d:843394
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    References listed on IDEAS

    as
    1. 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.
    2. Simone Zaccaria & Benjamin J. Raphael, 2020. "Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    3. Richard Redon & Shumpei Ishikawa & Karen R. Fitch & Lars Feuk & George H. Perry & T. Daniel Andrews & Heike Fiegler & Michael H. Shapero & Andrew R. Carson & Wenwei Chen & Eun Kyung Cho & Stephanie Da, 2006. "Global variation in copy number in the human genome," Nature, Nature, vol. 444(7118), pages 444-454, November.
    Full references (including those not matched with items on IDEAS)

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