Bias and Noise Cancellation for Robust Copy Number Variation Detection

被引：0

作者：

Zare, Fatima ^{[1
]}

Ansari, Sardar ^{[2
]}

Najarian, Kayvan ^{[2
]}

Nabavi, Sheida ^{[1
]}

机构：

[1] Univ Connecticut, Storrs, CT 06269 USA

[2] Univ Michigan, Ann Arbor, MI 48109 USA

来源：

ACM-BCB' 2017: PROCEEDINGS OF THE 8TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY,AND HEALTH INFORMATICS | 2017年

关键词：

Copy Number Variation; Whole-Exome Sequencing; Signal Processing; Taut String; Cancer; Normalization; Denoising;

D O I：

10.1145/3107411.3108199

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

High-throughput next generation sequencing (NGS) technologies have created an opportunity for detecting copy number variations (CNVs) more accurately. In this work, we introduce a novel preprocessing pipeline to improve the detection accuracy of CNVs in heterogeneous NGS data such as cancer whole exome sequencing data. We employ several normalizations to reduce biases due to GC contents, mappability and tumor contamination. We also utilize the Taut String method as an efficient effective smoothing approach to reduce noise.

引用

页码：591 / 591

页数：1

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