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分析和解读全外显子组测序数据的计算工具综述

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data.

作者信息

Hintzsche Jennifer D, Robinson William A, Tan Aik Choon

机构信息

Division of Medical Oncology, Department of Medicine, School of Medicine, Aurora, CO 80045, USA.

Division of Medical Oncology, Department of Medicine, School of Medicine, Aurora, CO 80045, USA; University of Colorado Cancer Center, Aurora, CO 80045, USA.

出版信息

Int J Genomics. 2016;2016:7983236. doi: 10.1155/2016/7983236. Epub 2016 Dec 14.

Abstract

Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.

摘要

全外显子组测序(WES)是应用新一代技术来确定外显子组中的变异,并且正在成为研究疾病中基因变异的标准方法。以单碱基分辨率了解个体的外显子组有助于识别可用于疾病治疗和管理的可操作突变。WES技术已将实验数据生成的瓶颈转移到基于计算的密集型信息数据分析上。现已开发出新颖的计算工具和方法来分析和解释WES数据。在此,我们综述了一些目前用于分析WES数据的工具。这些工具涵盖从原始测序读数的比对一直到将变异与可操作的治疗方法相联系。讨论了每种工具的优缺点,目的是帮助研究人员在选择最佳工具来分析其WES数据时做出更明智的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7462/5192301/abce11a9c517/IJG2016-7983236.001.jpg

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