IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i12p2151-d843394.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/12/2151/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/12/2151/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yichen Henry Liu & Can Luo & Staunton G. Golding & Jacob B. Ioffe & Xin Maizie Zhou, 2024. "Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    2. Albertas Dvirnas & Callum Stewart & Vilhelm Müller & Santosh Kumar Bikkarolla & Karolin Frykholm & Linus Sandegren & Erik Kristiansson & Fredrik Westerlund & Tobias Ambjörnsson, 2021. "Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-15, November.
    3. 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.
    4. Yu Chen & Amy Y. Wang & Courtney A. Barkley & Yixin Zhang & Xinyang Zhao & Min Gao & Mick D. Edmonds & Zechen Chong, 2023. "Deciphering the exact breakpoints of structural variations using long sequencing reads with DeBreak," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Jae Eun Lee & Jung Hye Sung & Daniel Sarpong & Jimmy T. Efird & Paul B. Tchounwou & Elizabeth Ofili & Keith Norris, 2018. "Knowledge Management for Fostering Biostatistical Collaboration within a Research Network: The RTRN Case Study," IJERPH, MDPI, vol. 15(11), pages 1-13, November.
    6. 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.
    7. 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.
    8. 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.
    9. repec:jss:jstsof:40:i12 is not listed on IDEAS

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2151-:d:843394. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.