Genovar

A Detection and Visualization Tool
for Genomic Variants

Kwang Su Jung¹, Kiejung Park¹, Sanghoon Moon², Young Jin Kim², Bong-jo Kim²

¹ Division of Bio-Medical informatics, Cente for Genome Science,Korea National Institute of Health

²Division of Structural and Functional Genomics, Center for Genome Science, Korea National Institute of Health

Availability:
freely distributed to public users and private institutions at http://genovar.sourceforge.net/

Contact: Kwang Su Jung (ksjung76.at.gmail.com)

Distribution: Download Genovar

Quick User Guide: Download Guide

Test files for detecting Copy Number Variation

Array CGH file
Probe and gene mapping file

Example Sequence Alignment Results (*.bam, *bai) on 1000 genomes :BAM file and its index ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data/NA19403/alignment/NA19403.chrom1.LS454.ssaha2.LWK.exon_targetted.20100311.bam
ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data/NA19403/alignment/NA19403.chrom1.LS454.ssaha2.LWK.exon_targetted.20100311.bam.bai
ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data/NA19397/alignment/NA19397.chrom1.LS454.ssaha2.LWK.exon_targetted.20100311.bam
ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data/NA19397/alignment/NA19397.chrom1.LS454.ssaha2.LWK.exon_targetted.20100311.bam.bai

UCSC reference sequence file hg 19(GRCh 37) :FastA format

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz
Indices of reference sequence files

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, which utilizes the Smith-Waterman Array (SW-ARRAY) algorithm to detect CNV regions, 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. Genovar is available for download from theGenovar website (http://genovar.sourceforge.net/).

Screen shots

Figure 1. Chromosomal view and statistical summary .

Figure 1a. A whole-chromosomal view.

Whole-chromosome scale view of the first sample in the file. Hyper- and hypo-expressed regions are represented by green and redcolor, respectively.

Figure 1b. A single-chromosomal view.

A series of log ratio values for a specific chromosome are given in table form, and a plot of each log ratio value is also offered, along with a cytoband. Zoom in and out functions are also included, via a pop-up menu.

Figure 1c. Statistical summary operation.

Statistical summary including mean, median, max, min, sum, and variance, as well as an outlier filter, for a particular region, in single-chromosome view.

Figure 2. Copy number variation (CNV) detection.

Figure 2a. Copy number variation (CNV) detection in whole-chromosome view.

Genovar performs CNV detection by SW-Array and Threshold Based methods.

Figure 2b. Database of Genomic Variants (DGV) entries related to detected copy number variation (CNV) regions.

CNV regions reported in DGV are indicated. Gained and lost areas are marked as red and green rectangles, respectively. CNV regions are derived from Figure 2a.

Figure 3. Comparison with multiple samples.

Figure 3a. Heat map of copy number variation (CNV) regions.

Detailed view of specific CNV regions, with loci. This view is obtained by setting starting and ending position using a pop-up menu. Heat map is offered for each chromosome.

Figure 3b. Comparative genomic hybridization (CGH) values; comparison of samples.

The coloredsamples correspond directly to the same-colored spot on the cytoband view.

Figure 4. Sequence alignment view of a single binary sequence alignment/map (BAM) file.

A single alignment view contains cytoband information, locus range, coverage of each locus, zoom level, and frequency of nucleotides for the current viewing window.

Figure 5. Sequence alignment comparison of two binary sequence alignment/map (BAM) files.

Comparison of sequence alignment results from one sample with those from another sample. Results from dbSNP in a user-specified locus range are a useful means of detecting unknown SNPs.