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UMICH Center for Computational Medicine and Bioinformatics installs Genomatix Genome Analyzer
"We are very pleased to add Genomatix advanced Next Generation Sequencing downstream analysis capabilities to our ongoing effort of providing our university-wide user base with quality tools for their data analysis", said Dr. Jim Cavalcoli, Director of the CCDU Bioinformatics Core of the Center for Computational Medicine and Biology at the University of Michigan. "We are constantly looking for tools that have both in-depth data analysis capabilities coupled to an intuitive user interface, which we found in the Genomatix Genome Analyzer. The fact that it provides command line access for our IT people was simply an added bonus."
Peter Grant, Chief Executive Officer at Genomatix, USA commented: "Placing our Genomatix Genome Analyzer at the University of Michigan in an environment providing campus-wide accessibility to our tools provides further validation of the quality and depth of the Genomatix' technology for the analysis of NGS data. That this represents our second installation at a university of such stature is a terrific vote of confidence not only for our existing technologies, but is also a strong endorsement of our abilities to keep up with the analysis demands of this very dynamic and rapidly growing technology. We have more than a decade of research and development behind many of the tools implemented in this sweeping array of analysis applications, and are prepared today for the data avalanche that will inevitably come from the 'next'-next generation of sequencers."
The Genomatix Genome Analyzer (GGA) delivers downstream software tools and databases for the deep biological analysis of mapped NGS data. It allows for easy integration and visualization in the extensive background annotation of our ElDorado genome database. The GGA extensively annotates genomic coordinates and surrounding areas derived from any mapping procedure. Clustering and peak finding, analysis for phylogenetic conservation, large scale correlation analysis with annotated genomic elements, meta-analysis of data correlation between different experiments, pathway mining for groups of identified genes, transcription factor binding site (TFBS) analysis ( identification, over-representation, binding partner analysis, framework identification, phylogenetic conservation, regulatory SNP effects) and much more are all processes carried out on the GGA.
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