AscatNgs: Identifying Somatically Acquired Copy‐Number Alterations from Whole‐Genome Sequencing Data. Issue 1 (8th December 2016)

• Record Type: Journal Article
• Authors: Raine, Keiran M.; Van Loo, Peter; Wedge, David C.; Jones, David; Menzies, Andrew; Butler, Adam P.; Teague, Jon W.; Tarpey, Patrick; Nik‐Zainal, Serena; Campbell, Peter J.
• Editors: Bateman, Alex; Pearson, William R.; Stein, Lincoln D.; Stormo, Gary D.; Yates, John R.
• Is Part Of: Current protocols in bioinformatics. Volume 56:Issue 1(2016)
• Page Start: 15.9.1
• Page End: 15.9.17
• Publication Date: 2016-12-08
• Subjects: somatic -- sequencing -- cancer -- copy‐number; Bioinformatics -- Laboratory manuals; Nucleotide sequence -- Laboratory manuals; Amino acid sequence -- Laboratory manuals; Base Sequence; Amino Acid Sequence; Computational Biology -- methods; Databases, Genetic; Proteins -- analysis; Sequence Analysis -- methods; Sequence Homology; Amino acid sequence; Bioinformatics; Nucleotide sequence; Laboratory Manuals; Laboratory manuals; 570.285
• DOI: 10.1002/cpbi.17
• Languages: English
• ISSNs: 1934-3396

Abstract

Abstract: 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.