ArticleBioinformatics (Oxford, England)2011
DiNAMIC: a method to identify recurrent DNA copy number aberrations in tumors.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 34 citations in OpenAlex.
- A copy number variation detection method based on OCSVM algorithm using multi strategies integration.Scientific reports · 2025Article
- RUBIC identifies driver genes by detecting recurrent DNA copy number breaks.Nature communications · 2016Article
- Consistent testing for recurrent genomic aberrations.Biometrika · 2015Article
- Article
- Detecting independent and recurrent copy number aberrations using interval graphs.Bioinformatics (Oxford, England) · 2014Article
- Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine.Genome medicine · 2014Review
- Use of autocorrelation scanning in DNA copy number analysis.Bioinformatics (Oxford, England) · 2013Article
- Molecular subtypes in head and neck cancer exhibit distinct patterns of chromosomal gain and loss of canonical cancer genes.PloS one · 2013Article
- Genome-wide identification of significant aberrations in cancer genome.BMC genomics · 2012Article
- Article
- Comparative analysis of methods for identifying recurrent copy number alterations in cancer.PloS one · 2012Article
- TAGCNA: a method to identify significant consensus events of copy number alterations in cancer.PloS one · 2012Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationDNA copy number gains and losses are commonly found in tumor tissue, and some of these aberrations play a role in tumor genesis and development. Although high resolution DNA copy number data can be obtained using array-based techniques, no single method is widely used to distinguish between recurrent and sporadic copy number aberrations.
resultsHere we introduce Discovering Copy Number Aberrations Manifested In Cancer (DiNAMIC), a novel method for assessing the statistical significance of recurrent copy number aberrations. In contrast to competing procedures, the testing procedure underlying DiNAMIC is carefully motivated, and employs a novel cyclic permutation scheme. Extensive simulation studies show that DiNAMIC controls false positive discoveries in a variety of realistic scenarios. We use DiNAMIC to analyze two publicly available tumor datasets, and our results show that DiNAMIC detects multiple loci that have biological relevance. AVAILABILITY: Source code implemented in R, as well as text files containing examples and sample datasets are available at http://www.bios.unc.edu/research/genomic_software/DiNAMIC.
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