Genovar: a Detection and Visualization Tool for Genomic Variants

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2012 May 19

PMID 22594998

Citations 1

Authors

Kwang Su Jung

Sanghoon Moon

Young Jin Kim

Bong-Jo Kim

Kiejung Park

Affiliations

Soon will be listed here.

Abstract

Background: Along with single nucleotide polymorphisms (SNPs), copy number variation (CNV) is considered an important source of genetic variation associated with disease susceptibility. Despite the importance of CNV, the tools currently available for its analysis often produce false positive results due to limitations such as low resolution of array platforms, platform specificity, and the type of CNV. To resolve this problem, spurious signals must be separated from true signals by visual inspection. None of the previously reported CNV analysis tools support this function and the simultaneous visualization of comparative genomic hybridization arrays (aCGH) and sequence alignment. The purpose of the present study was to develop a useful program for the efficient detection and visualization of CNV regions that enables the manual exclusion of erroneous signals.

Results: A JAVA-based stand-alone program called Genovar was developed. To ascertain whether a detected CNV region is a novel variant, Genovar compares the detected CNV regions with previously reported CNV regions using the Database of Genomic Variants (DGV, http://projects.tcag.ca/variation) and the Single Nucleotide Polymorphism Database (dbSNP). The current version of Genovar is capable of visualizing genomic data from sources such as the aCGH data file and sequence alignment format files.

Conclusions: Genovar is freely accessible and provides a user-friendly graphic user interface (GUI) to facilitate the detection of CNV regions. The program also provides comprehensive information to help in the elimination of spurious signals by visual inspection, making Genovar a valuable tool for reducing false positive CNV results.

Availability: http://genovar.sourceforge.net/.

Citing Articles

Introduction: Advanced intelligent computing theories and their applications in bioinformatics.

Gromiha M, Huang D BMC Bioinformatics. 2012; 13 Suppl 7:I1.

PMID: 22594994 PMC: 3348015. DOI: 10.1186/1471-2105-13-S7-I1.

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