GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples

Jean-Baptiste Cazier; Chris C. Holmes; John Broxholme

doi:10.1093/bioinformatics/bts547

GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples

Jean-Baptiste Cazier^*, Chris C. Holmes, John Broxholme

^*Corresponding author for this work

Cancer and Genomic Sciences

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

10 Citations (Scopus)

28 Downloads (Pure)

Abstract

SUMMARY: GREVE has been developed to assist with the identification of recurrent genomic aberrations across cancer samples. The exact characterization of such aberrations remains a challenge despite the availability of increasing amount of data, from SNParray to next-generation sequencing. Furthermore, genomic aberrations in cancer are especially difficult to handle because they are, by nature, unique to the patients. However, their recurrence in specific regions of the genome has been shown to reflect their relevance in the development of tumors. GREVE makes use of previously characterized events to identify such regions and focus any further analysis.

AVAILABILITY: GREVE is available through a web interface and open-source application (http://www.well.ox.ac.uk/GREVE).

Original language	English
Pages (from-to)	2981-2982
Number of pages	2
Journal	Bioinformatics
Volume	28
Issue number	22
Early online date	6 Sept 2012
DOIs	https://doi.org/10.1093/bioinformatics/bts547
Publication status	Published - Nov 2012

Keywords

Chromosome Aberrations
Chromosome Breakpoints
Genome, Human
Humans
Neoplasms
Software

UN SDGs

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

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10.1093/bioinformatics/bts547Licence: Creative Commons: Attribution (CC BY)

CazierJ2012GREVEFinal published version, 186 KBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples",

abstract = "SUMMARY: GREVE has been developed to assist with the identification of recurrent genomic aberrations across cancer samples. The exact characterization of such aberrations remains a challenge despite the availability of increasing amount of data, from SNParray to next-generation sequencing. Furthermore, genomic aberrations in cancer are especially difficult to handle because they are, by nature, unique to the patients. However, their recurrence in specific regions of the genome has been shown to reflect their relevance in the development of tumors. GREVE makes use of previously characterized events to identify such regions and focus any further analysis.AVAILABILITY: GREVE is available through a web interface and open-source application (http://www.well.ox.ac.uk/GREVE).",

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author = "Jean-Baptiste Cazier and Holmes, {Chris C.} and John Broxholme",

year = "2012",

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doi = "10.1093/bioinformatics/bts547",

language = "English",

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pages = "2981--2982",

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T2 - Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples

AU - Cazier, Jean-Baptiste

AU - Holmes, Chris C.

AU - Broxholme, John

PY - 2012/11

Y1 - 2012/11

N2 - SUMMARY: GREVE has been developed to assist with the identification of recurrent genomic aberrations across cancer samples. The exact characterization of such aberrations remains a challenge despite the availability of increasing amount of data, from SNParray to next-generation sequencing. Furthermore, genomic aberrations in cancer are especially difficult to handle because they are, by nature, unique to the patients. However, their recurrence in specific regions of the genome has been shown to reflect their relevance in the development of tumors. GREVE makes use of previously characterized events to identify such regions and focus any further analysis.AVAILABILITY: GREVE is available through a web interface and open-source application (http://www.well.ox.ac.uk/GREVE).

AB - SUMMARY: GREVE has been developed to assist with the identification of recurrent genomic aberrations across cancer samples. The exact characterization of such aberrations remains a challenge despite the availability of increasing amount of data, from SNParray to next-generation sequencing. Furthermore, genomic aberrations in cancer are especially difficult to handle because they are, by nature, unique to the patients. However, their recurrence in specific regions of the genome has been shown to reflect their relevance in the development of tumors. GREVE makes use of previously characterized events to identify such regions and focus any further analysis.AVAILABILITY: GREVE is available through a web interface and open-source application (http://www.well.ox.ac.uk/GREVE).

KW - Chromosome Aberrations

KW - Chromosome Breakpoints

KW - Genome, Human

KW - Humans

KW - Neoplasms

KW - Software

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U2 - 10.1093/bioinformatics/bts547

DO - 10.1093/bioinformatics/bts547

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SN - 1367-4803

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SP - 2981

EP - 2982

JO - Bioinformatics

JF - Bioinformatics

IS - 22

ER -

GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples

Abstract

Keywords

UN SDGs

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