DNA copy number alterations (CNAs) are the main genomic events that occur during the initiation and development of cancer. Distinguishing driver aberrant regions from passenger regions, which might contain candidate target genes for cancer therapies, is an important issue. Several methods for identifying cancer-driver genes from multiple cancer patients have been developed for single nucleotide polymorphism (SNP) arrays. However, for NGS data, methods for the SNP array cannot be directly applied because of different characteristics of NGS such as higher resolutions of data without predefined probes and incorrectly mapped reads to reference genomes. In this study, we developed a wavelet-based method for identification of focal genomic alterations for sequencing data (WIFA-Seq). We applied WIFA-Seq to whole genome sequencing data from glioblastoma multiforme, ovarian serous cystadenocarcinoma and lung adenocarcinoma, and identified focal genomic alterations, which contain candidate cancer-related genes as well as previously known cancer-driver genes.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2058053), and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. R-20150826-002098, Developing tools to detect cancer driver mutations using single-cell sequencing data.) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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