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CNVDAT: A Copy Number Variation Detection and Analysis Tool for Next-generation Sequencing Data  

Kang, Inho (한림대학교 컴퓨터공학과)
Kong, Jinhwa (한림대학교 컴퓨터공학과)
Shin, JaeMoon (한림대학교 컴퓨터공학과)
Lee, UnJoo (한림대학교 전자공학과)
Yoon, Jeehee (한림대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Databases / v.41, no.4, 2014 , pp. 249-255 More about this Journal
Abstract
Copy number variations(CNVs) are a recently recognized class of human structural variations and are associated with a variety of human diseases, including cancer. To find important cancer genes, researchers identify novel CNVs in patients with a particular cancer and analyze large amounts of genomic and clinical data. We present a tool called CNVDAT which is able to detect CNVs from NGS data and systematically analyze the genomic and clinical data associated with variations. CNVDAT consists of two modules, CNV Detection Engine and Sequence Analyser. CNV Detection Engine extracts CNVs by using the multi-resolution system of scale-space filtering, enabling the detection of the types and the exact locations of CNVs of all sizes even when the coverage level of read data is low. Sequence Analyser is a user-friendly program to view and compare variation regions between tumor and matched normal samples. It also provides a complete analysis function of refGene and OMIM data and makes it possible to discover CNV-gene-phenotype relationships. CNVDAT source code is freely available from http://dblab.hallym.ac.kr/CNVDAT/.
Keywords
copy number variation; CNV detection; CNV analysis; sequence analyzer; NGS technology;
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Times Cited By KSCI : 1  (Citation Analysis)
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