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用于下一代测序数据的体细胞单核苷酸变异检测算法综述。

A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data.

作者信息

Xu Chang

机构信息

Life Science Research and Foundation, Qiagen Sciences, Inc., 6951 Executive Way, Frederick, Maryland 21703, USA.

出版信息

Comput Struct Biotechnol J. 2018 Feb 6;16:15-24. doi: 10.1016/j.csbj.2018.01.003. eCollection 2018.

Abstract

Detection of somatic mutations holds great potential in cancer treatment and has been a very active research field in the past few years, especially since the breakthrough of the next-generation sequencing technology. A collection of variant calling pipelines have been developed with different underlying models, filters, input data requirements, and targeted applications. This review aims to enumerate these unique features of the state-of-the-art variant callers, in the hope to provide a practical guide for selecting the appropriate pipeline for specific applications. We will focus on the detection of somatic single nucleotide variants, ranging from traditional variant callers based on whole genome or exome sequencing of paired tumor-normal samples to recent low-frequency variant callers designed for targeted sequencing protocols with unique molecular identifiers. The variant callers have been extensively benchmarked with inconsistent performances across these studies. We will review the reference materials, datasets, and performance metrics that have been used in the benchmarking studies. In the end, we will discuss emerging trends and future directions of the variant calling algorithms.

摘要

体细胞突变的检测在癌症治疗中具有巨大潜力,并且在过去几年一直是一个非常活跃的研究领域,特别是自下一代测序技术取得突破以来。已经开发了一系列具有不同基础模型、过滤器、输入数据要求和目标应用的变异检测流程。本综述旨在列举这些最先进的变异检测工具的独特特征,希望为针对特定应用选择合适的流程提供实用指南。我们将专注于体细胞单核苷酸变异的检测,范围从基于配对肿瘤-正常样本的全基因组或外显子组测序的传统变异检测工具到为具有独特分子标识符的靶向测序方案设计的近期低频变异检测工具。这些变异检测工具在这些研究中经过了广泛的基准测试,但性能并不一致。我们将回顾基准测试研究中使用的参考材料、数据集和性能指标。最后,我们将讨论变异检测算法的新兴趋势和未来方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3e8/5852328/8508f2cd6497/gr1.jpg

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