Westborough, MA (Vocus) September 16, 2010
GenomeQuest today announced that the session "State-of-the-Art in Whole- and Multi-Genome Analysis: a Discussion and Demonstration of Critical Requirements" at the Next-Generation Sequencing Data Management conference will feature Dr. Don Gregory, head of field application science at GenomeQuest.
The luncheon session starts at 12:40p.m. on Wednesday, September 29, 2010 at the Rhode Island Convention Center in Providence, RI. The dates for the full conference, run by Cambridge Healthtech Institute, are September 27-29.
In his presentation, Dr. Gregory will outline use cases for multi-genome analysis (MGA), including Family Genetics, Disease/Normal Study, Population Genetics, Pharmacogenomics, and Propensity/Diagnostics.
He will also explore powerful science questions enabled by MGA, including:
- How prevalent is this variation?
- What is the predicted effect of this variation?
- How can I prioritize the observed varations?
- What are the variations common to my triad?
- Is this haplotype generalizable to the overall population?
- Is the variation homozygous/heterozygous?
Lastly, he will review and demonstrate critical requirements of MGA solutions, including:
- Scalability to whole-genome reads
- Interactive querying of sequence comparison results
- Incorporation of custom annotated reference genomes
- Comparison against multiple, very large datasets
- Integrated access to aggregated, public datasets
- Consolidation of multiple comparison results into one virtual,
- Statistical analysis of clinical attributes
- Visualization of MGA variations
- Detection of structural variation
- Web-enabled, high-performance, openness
More information is available at http://www.genomequest.com/109/sept-2010-mga-at-chi, where you can also register for the iPad drawing at the conclusion of the session. More information on the conference is available at http://www.healthtech.com/sda/overview.aspx.
In related news last quarter, GenomeQuest and SGI (NASDAQ: SGI) announced the immediate availability of the world’s first whole- and multi-genome analysis services for researchers. As a result, pharmaceutical companies, core labs, biotechs, government agencies, and clinics now have direct access to processing at a scale previously found only inside genome centers combined with comprehensive, self-serve analysis.
Don Gregory Bio
Dr. Gregory is Director of the Field Application Scientist group at GenomeQuest. He holds a PhD in Computational Biochemistry and has been academically and professionally involved in the field of computational-biochemistry and bioinformatics for nearly two decades.
Gregory’s early research was focused on the molecular basis of neurological functioning. His expertise in related software modeling led to fellowship in applying this technology to research groups and their studies. He left academia for a position with Polygen Inc. (Accelrys), where for the next 14 years he helped researchers answer scientific and biological questions. Dr. Gregory earned his MBA, gaining him a greater appreciation of the differences and connections between business and science.
The combination of Dr. Gregory’s extensive scientific research experience, IT skills, and business perspective allow him to effectively communicate and apply GenomeQuest capabilities to scientists, bioinformaticists, and their work.
More on GenomeQuest
GenomeQuest, the global leader in sequence data management (SDM), helps genomic researchers and their organizations make great discoveries far faster. Over 160 leading life science companies use GenomeQuest for mission-critical work, including nine of the top ten pharmaceuticals.
The GenomeQuest SDM platform enables researchers to analyze and manage sequence data from their web browser. Bioinformatics managers can customize applications and unify their sequence data environment. IT and business managers can efficiently scale genomics across discovery operations. The core technology of the platform is the GQ-Engine -- a database engine that is purpose-built for storing, analyzing, and managing sequence data at whole- and multi-genome scale.