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

Autor: Raine KM; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom., Van Loo P; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom.; The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, United Kingdom., Wedge DC; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom.; Oxford Big Data Institute, Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom., Jones D; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom., Menzies A; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom., Butler AP; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom., Teague JW; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom., Tarpey P; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom., Nik-Zainal S; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom., Campbell PJ; Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, United Kingdom.
Jazyk: angličtina
Zdroj: Current protocols in bioinformatics [Curr Protoc Bioinformatics] 2016 Dec 08; Vol. 56, pp. 15.9.1-15.9.17. Date of Electronic Publication: 2016 Dec 08.
DOI: 10.1002/cpbi.17
Abstrakt: 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.
(Copyright © 2016 John Wiley & Sons, Inc.)
Databáze: MEDLINE
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