ArticleBMC bioinformatics2011
Wavelet-based identification of DNA focal genomic aberrations from single nucleotide polymorphism arrays.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Identification of recurrent focal copy number variations and their putative targeted driver genes in ovarian cancer.BMC bioinformatics · 2016Article
- Identification of cancer-driver genes in focal genomic alterations from whole genome sequencing data.Scientific reports · 2016Article
- Application of discrete wavelet transform for analysis of genomic sequences of Mycobacterium tuberculosis.SpringerPlus · 2016Article
- FocalCall: An R Package for the Annotation of Focal Copy Number Aberrations.Cancer informatics · 2014Article
- Voting-based cancer module identification by combining topological and data-driven properties.PloS one · 2013Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCopy number aberrations (CNAs) are an important molecular signature in cancer initiation, development, and progression. However, these aberrations span a wide range of chromosomes, making it hard to distinguish cancer related genes from other genes that are not closely related to cancer but are located in broadly aberrant regions. With the current availability of high-resolution data sets such as single nucleotide polymorphism (SNP) microarrays, it has become an important issue to develop a computational method to detect driving genes related to cancer development located in the focal regions of CNAs.
resultsIn this study, we introduce a novel method referred to as the wavelet-based identification of focal genomic aberrations (WIFA). The use of the wavelet analysis, because it is a multi-resolution approach, makes it possible to effectively identify focal genomic aberrations in broadly aberrant regions. The proposed method integrates multiple cancer samples so that it enables the detection of the consistent aberrations across multiple samples. We then apply this method to glioblastoma multiforme and lung cancer data sets from the SNP microarray platform. Through this process, we confirm the ability to detect previously known cancer related genes from both cancer types with high accuracy. Also, the application of this approach to a lung cancer data set identifies focal amplification regions that contain known oncogenes, though these regions are not reported using a recent CNAs detecting algorithm GISTIC: SMAD7 (chr18q21.1) and FGF10 (chr5p12).
conclusionsOur results suggest that WIFA can be used to reveal cancer related genes in various cancer data sets.
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