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Agilent Adds Genetic Association and Copy Number Variation Analyses to Bioinformatics Software
Product News

Agilent Adds Genetic Association and Copy Number Variation Analyses to Bioinformatics Software

Agilent Adds Genetic Association and Copy Number Variation Analyses to Bioinformatics Software
Product News

Agilent Adds Genetic Association and Copy Number Variation Analyses to Bioinformatics Software


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Agilent Technologies, Inc. has introduced GeneSpring GX 11, the latest generation of its desktop software for visualizing and analyzing microarray data, and GeneSpring Workgroup 11, the enterprise version of the GeneSpring desktop software.

Designed to support multi-omics research, GeneSpring 11 adds capabilities for genetic association analysis using genotyping data, genomic copy number analysis, and other analytical and visualization tools to facilitate comparison of these heterogeneous data. Agilent’s bioinformatics portfolio also includes Mass Profiler Professional, built on the same platform as GeneSpring, supporting proteomics and metabolomics data analysis.

“The multi-omics approach to defining mechanism of disease in systems biology research has created a bioinformatics bottleneck, where the ability to extract biological knowledge from the data often lags behind the ability to generate the data,” said Chris Grimley, Agilent senior director of marketing, Genomics. “GeneSpring 11 was developed with this bottleneck in mind and addresses some of the challenges of integrative analysis.”

“GeneSpring 11 represents a significant advance in data analysis software for life sciences,” said Bruce Aronow, Ph.D, scientific director of the Centre for Computational Medicine at Cincinnati Children’s Hospital Medical Centre, and a longtime GeneSpring user. “Here we see for the first time an all-wheel driving machine for multi-omics technologies that shape a new roadmap for integrative systems biology.”

In GeneSpring 11, researchers can have multiple experiment types, such as microRNA, gene expression, genotyping, and copy number, open simultaneously within a single window. This allows users to move back and forth between the data as needed without having to load each experiment separately. This functionality also allows researchers to easily combine data within a logical unit and compare results from different experiment types.

GeneSpring 11 also allows researchers to find critical linkages and concordance between them.

While there are other software applications that support multi-omics data analysis, a major differentiator of GeneSpring 11 is the ease with which users can compare heterogeneous data and the depth with which they can perform biological contextualization. A key tool for data comparison is the new genome browser, where researchers can plot multiple data types as tracks in the same view, and merge tracks to overlay different data types. For example, researchers can overlay copy number and gene expression data to assess concordance, or overlay copy number data and microRNA tracks to see if critical microRNAs are found in amplified or deleted regions.

Automatic translation of probes across different microarray platforms and organisms allows researchers to compare results through simple drag-and-drop function into a Venn diagram. This seamless translation also allows researchers to quickly identify entity lists that share a statistically significant overlap in content. This ease of data exploration allows the quick discovery of drug treatments, disease states, or other experimental factors that share similar biological profiles and, therefore, may share similar underlying mechanisms.

GeneSpring 11 continues to build on its strength in biological contextualization by extending the functionality of its network analysis tool. A set of algorithms combined with provided organism-specific pathway interaction databases enable researchers to generate a range of network types and dynamically explore the biochemical network depicting the interaction of their entities of interest.

GeneSpring 11 adds the ability for researchers to use MeSH terms as input for generating networks. For example, researchers can use “heart failure” as the input MeSH term and a network containing interactions associated with heart failure is automatically generated. Researchers can then determine how their gene expression data intersects with this disease network.

The release of GeneSpring Workgroup 11 also adds two Web-enabled clients for its enterprise-level product, with central database for storage of multi-omics data. The data browser provides an interface where users can search for experiments and samples stored in Workgroup’s database using configurable search fields. Associated data files can then be downloaded, and published static images from GeneSpring can be viewed.

The Web client allows users to visualize and dynamically explore experiments and results stored in Workgroup using all views and plots available in GeneSpring through a Web-enabled interface. With multiple ways to access the stored data, Workgroup provides an efficient way for scientists to collaborate and share analysis results and knowledge.
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