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

15 Citations (Scopus)

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
Pages (from-to)	1689-1691
Number of pages	3
Journal	Bioinformatics
Volume	23
Issue number	13
Early online date	7 May 2007
DOIs	https://doi.org/10.1093/bioinformatics/btm152
Publication status	Published - Jul 2007

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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

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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",

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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

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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.

PY - 2007/7

Y1 - 2007/7

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.

U2 - 10.1093/bioinformatics/btm152

DO - 10.1093/bioinformatics/btm152

M3 - Article

SN - 1367-4803

VL - 23

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EP - 1691

JO - Bioinformatics

JF - Bioinformatics

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ER -