Analysis of Gene Relationships using a Graph Data Model
DESCRIPTION (Applicant's abstract): Genomic technology has become a major force
facilitating biomedical research. Enormous data sets of great complexity are
accumulating more rapidly than scientists can create specialized databases and
analytical tools. The ability to synthesize and integrate disparate sources of
genomics data into biologically meaningful information is a fundamental need.
Commercially available products are not adequate in managing the data, mining
the information for novel biological relationships, or elucidating components
of biological pathways.
Agilix Corporation will develop a novel algorithmic analysis method based on
graph-theoretic tools, that will organize, catalogue, and data-mine genomic and
proteomic relationships. The goal of this research is to develop a novel
genomics analysis framework to unify heterogeneous genomics information into a
common data structure and analyze gene relationships. In Phase I, we will:
develop a comprehensive gene-graph model to organize and store gene
relationship information as graphs; build methods to analyze and compare
graphs; and create the software to visualize the gene-graph relationships. In
Phase II, we will expand the number of graph structures and graph operators,
and we will develop a local database and an analysis workflow engine. Phase III
commercialization will include a low cost software application for the general
PROPOSED COMMERCIAL APPLICATION:
Our new gene-graph technology will enable scientists to perform operations in several
areas of bioinformatics previously inaccessible to those not trained extensively in
bioinformatics. These areas include statistical analysis of gene expression data, data
management, analysis workflow, data visualization, and the performance of "consensus"
operations and simple mathematical operations such as addition, subtraction, and intersection
on complex data sets. This new analysis capability will reduce the need for a team of
bioinformaticians, and empower the biologist to synthesize and explore disparate genomic
information at a level of sophistication not available in any other commercial package.
Small Business Information at Submission:
Principal Investigator:Shan Jiang
BOX 8175, 2 CHURCH ST S, STE 401 NEW HAVEN, CT 06519
Number of Employees: