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

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

doi:10.1093/bioinformatics/btm152

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

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

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

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.

Original language	English (US)
Pages (from-to)	1689-1691
Number of pages	3
Journal	Bioinformatics
Volume	23
Issue number	13
DOIs	https://doi.org/10.1093/bioinformatics/btm152
State	Published - Jul 1 2007
Externally published	Yes

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

Access to Document

10.1093/bioinformatics/btm152

Cite this

Dicks, E., Teague, J. W., Stephens, P., Raine, K., Yates, A., Mattocks, C., Tarpey, P., Butler, A., Menzies, A., Richardson, D., Jenkinson, A., Davies, H., Edkins, S., Forbes, S., Gray, K., Greenman, C., Shepherd, R., Stratton, M. R., Futreal, P. A., & Wooster, R. (2007). AutoCSA, an algorithm for high throughput DNA sequence variant detection in cancer genomes. Bioinformatics, 23(13), 1689-1691. https://doi.org/10.1093/bioinformatics/btm152

Dicks, E, Teague, JW, Stephens, P, Raine, K, Yates, A, Mattocks, C, Tarpey, P, Butler, A, Menzies, A, Richardson, D, Jenkinson, A, Davies, H, Edkins, S, Forbes, S, Gray, K, Greenman, C, Shepherd, R, Stratton, MR, Futreal, PA & Wooster, R 2007, 'AutoCSA, an algorithm for high throughput DNA sequence variant detection in cancer genomes', Bioinformatics, vol. 23, no. 13, pp. 1689-1691. https://doi.org/10.1093/bioinformatics/btm152

@article{66bcab4f49354042961f02b756862835,

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

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.",

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

note = "Funding Information: The authors would like to thank the Wellcome Trust for funding the Cancer Genome Project.",

year = "2007",

month = jul,

day = "1",

doi = "10.1093/bioinformatics/btm152",

language = "English (US)",

volume = "23",

pages = "1689--1691",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "13",

}

TY - JOUR

T1 - AutoCSA, an algorithm for high throughput DNA sequence variant detection in cancer genomes

AU - Dicks, E.

AU - Teague, J. W.

AU - Stephens, P.

AU - Raine, K.

AU - Yates, A.

AU - Mattocks, C.

AU - Tarpey, P.

AU - Butler, A.

AU - Menzies, A.

AU - Richardson, D.

AU - Jenkinson, A.

AU - Davies, H.

AU - Edkins, S.

AU - Forbes, S.

AU - Gray, K.

AU - Greenman, C.

AU - Shepherd, R.

AU - Stratton, M. R.

AU - Futreal, P. A.

AU - Wooster, R.

N1 - Funding Information: The authors would like to thank the Wellcome Trust for funding the Cancer Genome Project.

PY - 2007/7/1

Y1 - 2007/7/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=34547830420&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34547830420&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btm152

DO - 10.1093/bioinformatics/btm152

M3 - Article

C2 - 17485433

AN - SCOPUS:34547830420

SN - 1367-4803

VL - 23

SP - 1689

EP - 1691

JO - Bioinformatics

JF - Bioinformatics

IS - 13

ER -

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

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this