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short-paper

Copy Number Variation Detection Using Total Variation

Authors:

Sheida NabaviAuthors Info & Claims

BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Pages 423 - 428

https://doi.org/10.1145/3307339.3342181

Published: 04 September 2019 Publication History

Abstract

Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.

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Cited By

Zare FStark JNabavi SJiang HHuang XZhang J(2021)Copy number variation detection using single cell sequencing dataProceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3459930.3469556(1-6)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1145/3459930.3469556

Index Terms

Copy Number Variation Detection Using Total Variation
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics

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Information

Published In

cover image ACM Conferences

BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 2019

716 pages

ISBN:9781450366663

DOI:10.1145/3307339

General Chairs:
Xinghua (Mindy) Shi
Temple University, USA
,
Michael Buck
University of Buffalo, USA
,
Program Chairs:
Jian Ma
Carnegie Mellon University, USA
,
Pierangelo Veltri
University Magna Graecia of Catanzaro, Italy

Copyright © 2019 ACM.

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Published: 04 September 2019

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BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 7 - 10, 2019

NY, Niagara Falls, USA

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BCB '19 Paper Acceptance Rate 42 of 157 submissions, 27%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Zare FStark JNabavi SJiang HHuang XZhang J(2021)Copy number variation detection using single cell sequencing dataProceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3459930.3469556(1-6)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1145/3459930.3469556

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