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Bioinformatics Advance Access originally published online on May 7, 2007
Bioinformatics 2007 23(13):1689-1691; doi:10.1093/bioinformatics/btm152
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

AutoCSA, an algorithm for high throughput DNA sequence variant detection in cancer genomes

E. Dicks 1, J. W. Teague 1, P. Stephens 1, K. Raine 1, A. Yates 1, C. Mattocks 2, P. Tarpey 1, A. Butler 1, A. Menzies 1, D. Richardson 1, A. Jenkinson 1, H. Davies 1, S. Edkins 1, S. Forbes 1, K. Gray 1, C. Greenman 1, R. Shepherd 1, M. R. Stratton 1,*, P. A. Futreal 1 and R. Wooster 1

1Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK and 2NGRL (Wessex), Salisbury District Hospital, Salisbury, SP2 8BJ, UK

*To whom correspondence should be addressed.


  Abstract

The undertaking of large-scale DNA sequencing screens for somatic variants in human cancers requires accurate and rapid processing of traces for variants. Due to their often aneuploid nature and admixed normal tissue, heterozygous variants found in primary cancers are often subtle and difficult to detect. To address these issues, we have developed a mutation detection algorithm, AutoCSA, specifically optimized for the high throughput screening of cancer samples.

Availability: http://www.sanger.ac.uk/genetics/CGP/Software/AutoCSA.

Contact: mrs{at}sanger.ac.uk

Associate Editor: Dmitrij Frishman

Received on February 19, 2007; revised on April 2, 2007; accepted on April 13, 2007

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