ascatNgs:从全基因组测序数据中识别体细胞获得的拷贝数改变

Q1 Biochemistry, Genetics and Molecular Biology

Current protocols in bioinformatics Pub Date : 2016-12-08 DOI:10.1002/cpbi.17

Keiran M. Raine, Peter Van Loo, David C. Wedge, David Jones, Andrew Menzies, Adam P. Butler, Jon W. Teague, Patrick Tarpey, Serena Nik-Zainal, Peter J. Campbell

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引用次数: 108

摘要

我们开发了ascatgs，以帮助研究人员进行肿瘤等位基因特异性拷贝数分析(ASCAT)。与匹配的正常样本相比，ASCAT能够检测影响肿瘤基因组的DNA拷贝数变化。此外，该算法估计样本中肿瘤DNA的数量，称为异常细胞分数(ACF)。ASCAT本身是一个r包，它需要生成许多文件类型。在这里，我们提供了一套工具来帮助用户处理这个问题。我们的代码可在我们的GitHub网站(https://github.com/cancerit)。本单元描述了“一次性”执行和更适合大规模计算场的方法。©2016 by John Wiley &儿子,Inc。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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ascatNgs: Identifying Somatically Acquired Copy-Number Alterations from Whole-Genome Sequencing Data

We have developed ascatNgs to aid researchers in carrying out Allele-Specific Copy number Analysis of Tumours (ASCAT). ASCAT is capable of detecting DNA copy number changes affecting a tumor genome when comparing to a matched normal sample. Additionally, the algorithm estimates the amount of tumor DNA in the sample, known as Aberrant Cell Fraction (ACF). ASCAT itself is an R-package which requires the generation of many file types. Here, we present a suite of tools to help handle this for the user. Our code is available on our GitHub site (https://github.com/cancerit). This unit describes both ‘one-shot’ execution and approaches more suitable for large-scale compute farms. © 2016 by John Wiley & Sons, Inc.

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Current protocols in bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry

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期刊介绍： With Current Protocols in Bioinformatics, it"s easier than ever for the life scientist to become "fluent" in bioinformatics and master the exciting new frontiers opened up by DNA sequencing. Updated every three months in all formats, CPBI is constantly evolving to keep pace with the very latest discoveries and developments.