Open AccessascatNgs: Identifying Somatically Acquired Copy‐Number Alterations from Whole‐Genome Sequencing Data
Author(s) -
Raine Keiran M.,
Loo Peter,
Wedge David C.,
Jones David,
Menzies Andrew,
Butler Adam P.,
Teague Jon W.,
Tarpey Patrick,
NikZainal Serena,
Campbell Peter J.
Publication year - 2016
Publication title -
current protocols in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.535
H-Index - 57
eISSN - 1934-340X
pISSN - 1934-3396
DOI - 10.1002/cpbi.17
Subject(s) - suite , computer science , genome , sample (material) , dna sequencing , code (set theory) , computational biology , dna , biology , genetics , gene , programming language , history , chemistry , archaeology , set (abstract data type) , chromatography
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.