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Golden Helix Debuts New Software Module to Meet Increasing Demand for CNV Analysis

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Of all the components of array-based studies, demand often seems highest for flexible and capable analysis tools. While array manufacturers continue to devise new ways to make novel content available, researchers often find themselves swamped by the tremendous amount of data coming off of chips like Illumina’s Human 1M Beadchip or Affymetrix’s GeneChip SNP 6.0 Array.
 
This week Bozeman, Mont.-based bioinformatics firm Golden Helix introduced a tool that seeks to address this gap in the market. The Copy Number Analysis Module is a plug-in for the firm’s Helix Tree 6.0 SNP & Variation Suite and is capable of scanning through high-resolution microarray intensity data to identify copy number deletions and amplifications, which can then be used to perform whole-genome association analysis. 
 
Andy Ferrin, vice president of sales and marketing at Golden Helix, said the company based its CNV algorithm on the same one it uses in the SNP & Variation Suite. While other software packages offer CNV-specific analysis capabilities, Golden Helix believes that it is the first to offer algorithms robust to handle data from whole-genome association studies.
 
The company believes that rising demand for this capability combined with the increasing investment in informatics tools by array vendors themselves makes it likely that the availability of such software tools will play an increasing role in the development of the market.
 
Golden Helix plans to capitalize on the availability of CNAM to sell the Helix Tree analysis tool and will likely add more CNV-related features in the future. “Copy number analysis is a hot area and holds great promise for uncovering the genetic foundations of disease, so quite a bit of research is going on in this area,” he wrote in an e-mail to BioArray News this week.
 
“There is still no consensus on how best to work with copy number data or how to analyze it. For nearly 10 years, we’ve specialized in genetic association studies, so this is the direction we’ve chosen for copy number analysis,” wrote Ferrin. “Association is a relatively new concept for this application as most others have focused their efforts on visualization tools that enable an investigator to visually determine how copy number variations differ between small numbers of samples.” 
 
Other companies offering software tools for copy number analysis include Partek, which sells a plug-in for use with its Genomics Suite, as well as the vendors themselves, such as Illumina, Affymetrix, and Agilent.
 
Ferrin noted that none of these tools have been explicitly designed for integration into large-sample association studies, so rather than viewing these packages as competing products, Golden Helix is looking to integrate its tools into these systems.
 
“CNAM is fairly open and should integrate fairly easily with other packages. The file that is created, though fairly large, can be saved in several file formats that most programs should be able to import,” he wrote.
 

“There is still no consensus on how best to work with copy number data or how to analyze it.”

According to Ferrin, Helix Tree also includes the ability to create “bookmarks” for viewing in Illumina’s BeadStudio product, and also supports the format used in the University of California, Santa Cruz, Genome Browser. In addition, the SNP & Variation Suite includes a Python scripting engine “that can be used for custom analyses and tight linkage with other tools,” he wrote.
 
CNAM also provides direct import and appropriate conversion measures for Illumina and Affymetrix probe intensity data. “There is also functionality for importing intensity data from other providers in a more generic process,” wrote Ferrin.
 
Segmentation
 
The opportunity that Golden Helix sees in copy number analysis is not surprising. Most array users involved in these studies have been quite vocal about the need for better analysis tools.
 
For example, Steve Scherer, a senior scientist at the Hospital for Sick Children in Toronto, and Joris Veltman, head of the microarray facility at the Radboud University Nijmegen Medical Center in the Netherlands, told BioArray News in July that the greatest challenge they face working with CNV content is in data analysis (see BAN 7/24/2007).
 
Ferrin wrote this week that the company faced two primary challenges in developing CNAM. “First, the microarray probe intensity data from major genotyping platform providers is much noisier than SNP data, making it difficult to make integer copy number calls,” he wrote. In response, the company opted to “work directly with copy number intensity data, circumventing the need to make an actual copy number call, and avoiding the increased error that doing so introduces into a study.”
 
The second challenge Golden Helix faced was how best to perform association analysis. To do so, “you need a set of independent variables or covariates that you can associate with the response,” he wrote. “One approach could be to make each intensity value a covariate,” but Ferrin wrote that this method is “too computationally intensive to perform analysis with conventional methods.”
 
Instead, Golden Helix decided to divide the copy number variants into groupings or segments, and to make the copy number segments themselves the covariates. “We’re able to do this without storing every intensity value in system memory, thereby overcoming the computational challenge,” he wrote. “CNAM makes it possible to not only find true copy number segments, but to do it with computational efficiency that makes it viable for whole-genome analysis.”  
 
While Golden Helix believes its new copy number analysis tool offers a market advantage over rival array analysis platforms, Ferrin acknowledged that other companies, including the array vendors, have made some significant investments in informatics recently. He said the company plans to see how well CNAM performs in the market before it plans follow-up modules.
 

“We’ll continue down the association path as we believe it is the most powerful approach for determine how CNVs truly relate to disease, but only time will tell whether it becomes an accepted method for copy number analysis,” Ferrin wrote. “If it does, then we can expect other firms to follow suit.”

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