PRICING & INQUIRIES

For pricing and inquiries, send an email to sales@omicsoft.com.

5001 Weston Parkway, Suite 201
Cary, NC 27513
US

888-259-6642

Overview

Omicsoft is the leading provider of Next Generation Sequencing, Cancer Genomics, Immunology, and Bioinformatics solutions for Next Generation Sequencing Data and Gene Expression Analysis.

Exciting Updates and Latest News

Keeping you up-to-date with the latest in NGS, Bioinformatics Analysis, and cancer genomics with blogs on Array Suite, OncoLand (TCGA and more), ImmunoLand, and more.

[Array Studio Tutorial] Getting Started With Copy Number Variation Analysis

Vivian Zhang

Copy number variation is large-scale change in many locations in the genome, including insertions, deletions, inversions and duplications. A CNV can be defined as a DNA segment that is 1 kb or larger and present at variable copy number in comparison with a reference genome (Redon, R., et al. 2006) . CNV has been linked to many human diseases and has been found in all human populations. It also plays an important role in evolution. Array Studio provides comprehensive functions to manage, visualize, analyze and integrate CNV data. In this article, we will introduce the basic functions of CNV analysis.

 

 

 

Array Studio can import SNP and Copy Number Variation (CNV) intensity data, to analyze your samples for chromosome-wide and local amplifications, deletions, and Loss-of-Heterozygosity (LOH) events. The users can easily view, sort and filter data.

Filtering Log2 ratio data imported from Affymetrix CEL files. Each column is a sample and each row is a probe set (SNP in this example). 

Filtering Log2 ratio data imported from Affymetrix CEL files. Each column is a sample and each row is a probe set (SNP in this example). 

 

Array Studio can merge probe-level SNP intensity data to genomic regions, or segments, with predicted copy numbers for each segment. The CNV Segmentation command will generate segmentation results for Log2Ratio CNV Data, using a variety of criteria, to identify copy number segments, and any Loss of Heterozygosity segments. 

The CNV Segmentation Command Window allows users to change segmentation parameters. 

The CNV Segmentation Command Window allows users to change segmentation parameters. 

 

 

After segmenting CNV data, Array Studio has multiple interactive Views to help quickly identify meaningful amplifications and deletions. As with all Array Studio Views, users can sort, filter, and customize the Views to maximize the ability to identify these changes. These data can also be viewed in the Omicsoft Genome Browser.

Genome View displays Probe-level signal and B-allele frequency

Genome View displays Probe-level signal and B-allele frequency

Segment View displays segmented signal intensity along each chromosome. For example, the TCGA tumor sample has a chromosome-wide loss of signal on chromosome 10 comparing to normal sample. 

Segment View displays segmented signal intensity along each chromosome. For example, the TCGA tumor sample has a chromosome-wide loss of signal on chromosome 10 comparing to normal sample. 

Segment Chromosome View displays copy number predictions along chromosome schematics. As we can see, again, the TCGA tumor sample has a loss of signal on chromosome 10. 

Segment Chromosome View displays copy number predictions along chromosome schematics. As we can see, again, the TCGA tumor sample has a loss of signal on chromosome 10. 

 

Omicsoft Genome Browse also allows user to integrate CNV data with DNA-seq data. 

Omicsoft Genome Browser can display multiple data types, including CNV chip and DNA-Seq data.

Omicsoft Genome Browser can display multiple data types, including CNV chip and DNA-Seq data.

For more details on how to use Genome Browser, please refer to blog: 

[Array Studio Analysis] Getting Started With Genome Browser: Basic Navigation, Visualization And Annotation 

[Array Studio Analysis] Genome Browser Advanced Analysis Of Variants, Fusion And Isoform Expression

 

Reference: 

Redon, R., et al. Global variation in copy number in the human genome. Nature 444, 444–454 (2006) doi:10.1038/nature05329