Computer Science and Engineering, Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA.
Adv Exp Med Biol. 2022;1361:55-74. doi: 10.1007/978-3-030-91836-1_4.
Copy number variation (CNV), which is deletion and multiplication of segments of a genome, is an important genomic alteration that has been associated with many diseases including cancer. In cancer, CNVs are mostly somatic aberrations that occur during cancer evolution. Advances in sequencing technologies and arrival of next-generation sequencing data (whole-genome sequencing and whole-exome sequencing or targeted sequencing) have opened up an opportunity to detect CNVs with higher accuracy and resolution. Many computational methods have been developed for somatic CNV detection, which is a challenging task due to complexity of cancer sequencing data, high level of noise and biases in the sequencing process, and big data nature of sequencing data. Nevertheless, computational detection of CNV in sequencing data has resulted in the discovery of actionable cancer-specific CNVs to be used to guide cancer therapeutics, contributing to significant progress in precision oncology. In this chapter, we start by introducing CNVs. Then, we discuss the main approaches and methods developed for detecting somatic CNV for next-generation sequencing data, along with its challenges. Finally, we describe the overall workflow for CNV detection and introduce the most common publicly available software tools developed for somatic CNV detection and analysis.
拷贝数变异 (CNV)，即基因组片段的缺失和重复，是一种重要的基因组改变，与许多疾病有关，包括癌症。在癌症中，CNVs 主要是体细胞异常，发生在癌症演变过程中。测序技术的进步和新一代测序数据（全基因组测序、全外显子组测序或靶向测序）的出现，为更高精度和分辨率的 CNV 检测提供了机会。已经开发了许多用于体细胞 CNV 检测的计算方法，由于癌症测序数据的复杂性、测序过程中的高噪声和偏差以及测序数据的大数据性质，这是一项具有挑战性的任务。然而，测序数据中 CNV 的计算检测导致了可操作的癌症特异性 CNV 的发现，可用于指导癌症治疗，为精准肿瘤学的发展做出了重大贡献。在本章中，我们首先介绍 CNV。然后，我们讨论了为下一代测序数据检测体细胞 CNV 而开发的主要方法和方法，以及它们所面临的挑战。最后，我们描述了 CNV 检测的整体工作流程，并介绍了为体细胞 CNV 检测和分析开发的最常用的公开可用软件工具。
