Copy number variation detection workflow using next generation sequencing data

Prashanthi Dharanipragada, N. Parekh
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引用次数: 1

Abstract

In the last decade, discovery of copy number variations (CNVs) have dramatically changed our understanding of differences between individuals. CNVs include both additional copies of sequence (duplications) and loss of genetic material (deletions) and provide an alternate paradigm for the genetic basis of human diseases. Genome-wide CNV detection is now possible using high-throughput, low-cost next generation sequencing (NGS) methods. Nature of NGS data demands various preprocessing and pretreatment steps before extracting any meaningful information. Among the plethora of variant calling methods available, R-based methods offer flexible environment, facilitating choice of various methods depending on the type of data or type of analysis to be performed. Here we give a pipeline for various steps involved in CNV detection in NGS data using R-based algorithms and packages.
使用下一代测序数据的拷贝数变异检测工作流程
在过去的十年中,拷贝数变异(CNVs)的发现极大地改变了我们对个体差异的理解。CNVs包括序列的额外拷贝(重复)和遗传物质的损失(缺失),并为人类疾病的遗传基础提供了另一种范式。使用高通量、低成本的下一代测序(NGS)方法,全基因组CNV检测现在成为可能。NGS数据的性质要求在提取有意义的信息之前进行各种预处理和预处理。在大量可用的变量调用方法中,基于r的方法提供了灵活的环境,便于根据数据类型或要执行的分析类型选择各种方法。在这里,我们给出了使用基于r的算法和包在NGS数据中进行CNV检测的各个步骤的管道。
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