GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes

Remond J.A. Fijneman, Evert van den Broek, Stef van Lieshout, Christian Rausch, Bauke Ylstra, Mark A. van de Wiel, Gerrit A. Meijer, Sanne Abeln

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).
Original languageEnglish
Article number2340
Pages (from-to)1-8
Number of pages8
JournalF1000Research
Volume5
Early online date19 Sep 2016
DOIs
Publication statusPublished - 6 Jul 2017

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Aberrations
Chromosome Aberrations
Genes
Genome
DNA
Chromosome Breakage
Neoplasms
Comparative Genomic Hybridization
Chromosome Mapping
Computational methods
Tumors
Statistics
Testing

Keywords

  • Cancer genome
  • Computational method
  • Copy number aberration profile
  • Molecular characterization
  • Recurrent breakpoint genes
  • Structural chromosomal aberrations

Cite this

Fijneman, Remond J.A. ; van den Broek, Evert ; van Lieshout, Stef ; Rausch, Christian ; Ylstra, Bauke ; van de Wiel, Mark A. ; Meijer, Gerrit A. ; Abeln, Sanne. / GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes. In: F1000Research. 2017 ; Vol. 5. pp. 1-8.
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abstract = "Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).",
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GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes. / Fijneman, Remond J.A.; van den Broek, Evert; van Lieshout, Stef; Rausch, Christian; Ylstra, Bauke; van de Wiel, Mark A.; Meijer, Gerrit A.; Abeln, Sanne.

In: F1000Research, Vol. 5, 2340, 06.07.2017, p. 1-8.

Research output: Contribution to JournalArticleAcademicpeer-review

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AU - van Lieshout, Stef

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AU - Ylstra, Bauke

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