ascatNgs: Identifying Somatically Acquired Copy‐Number Alterations from Whole‐Genome Sequencing Data | Zendy

Raine Keiran M. | Zendy; Loo Peter | Zendy; Wedge David C. | Zendy; Jones David | Zendy; Menzies Andrew | Zendy; Butler Adam P. | Zendy; Teague Jon W. | Zendy; Tarpey Patrick | Zendy; NikZainal Serena | Zendy; Campbell Peter J. | Zendy

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ascatNgs: 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.

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