NSF Bioinformatics Grants Sept. 17 — Oct. 21, 2006
Critical Assessment of Information Extraction in Biology. Start date: Oct. 1, 2006. Expires: Sept. 30, 2007. Expected total amount: $296,174. Principal investigator: Lynette Hirschman. Sponsor: Mitre.
Supports continued work in organizing BioCreAtIvE: Critical Assessment for Information Extraction in Biology. The long-term focus of this project is to improve text-mining tools to support curators of biological databases, as well as to improve access to biological information. Specific tasks proposed include running the gene normalization task for BioCreAtIvE II and analyzing and disseminating the data and results of BioCreAtIvE II.
Collaborative Research: Core Database Technologies to Enable the Integration of AToL Information. Start date: Oct. 1, 2006. Expires: Sept. 30, 2009. This grant is awarded to three investigative teams:
Supports data integration for the AToL (Assembling the Tree of Life) project, a large-scale collaborative research effort to reconstruct the evolutionary origins of all living things. The initiative currently includes 31 projects involving more than 150 principal investigators. Data includes specimens and their provenance; phenotypic descriptions and their provenance; genotypic descriptions and their provenance; interpretation of primary measurements including homology; estimates of phylogenies and methods employed; and post-tree analyses such as character evolution hypotheses. The project will develop new, compact, abstract data models for phylogenetics and an integration system that will be based on “novel mappings between different phylogenetic data domains, and allow individual projects to join a network of integrated databases in an incremental manner,” according to the grant abstract.
Enhancing the E. coli Plasmid Genome Database and the Tools for its Effective Use. Start date: Oct. 1, 2006. Expires: Sept. 30, 2008. Expected total amount: $374,421. Principal investigator: Lisa Nolan. Sponsor: Iowa State University.
Funds a project to provide high-quality sequences of the most common plasmids for avian pathogenic Escherichia coli (APEC) and to develop a new E. coli Plasmid Genome Database tool that will be available for widespread use.
The Berkeley-TIGR Phylogenomic Encyclopedia of Microbial Protein Families. Start date: Nov. 1, 2006. Expires: Oct. 31, 2007. Expected total amount: $239,326. Principal investigator: Kimmen Sjolander. Sponsor: University of California, Berkeley.
Supports a joint project between the Berkeley Phylogenomics Group and the Institute for Genomic Research to develop a biological database enabling phylogenomic analysis of microbial genomes. The database, called the PhyloFacts Microbial Encyclopedia, will contain pre-calculated evolutionary, structural, and functional analyses for hundreds of thousands of proteins, and will be designed to improve the quality of functional annotation of microbial genomes.
Efficient Techniques for Reconstructing Horizontal Gene Transfer in Bacteria. Start date: Oct. 1, 2006. Sept. 30, 2009. Expected total amount: $600,000. Principal investigator: Luay Nakhleh. Sponsor: William Marsh Rice University.
Supports development of a set of methodologies to enable “efficient and accurate” phylogeny-based reconstruction of horizontal gene transfer in bacterial genomes, according to the grant abstract. In the phylogeny-based approach, a tree is built for each gene in the genome of a group of bacteria, and the gene tree disagreements are quantified and analyzed to detect horizontal gene transfer. The grantees plan to develop a stochastic framework that “incorporates population genetics theories with evolutionary events that act among species to classify gene tree disagreements.” All the models and algorithms will be implemented in a software platform called Sequoia, which will be made publicly available as an open source package.
Collaborative Research: Development of Effective Gene Selection Algorithms for Microarray Data Analysis. Start date: Oct. 1, 2006. Expires: Aug. 31, 2008. This grant is awarded to two investigative teams:
Supports development of new gene-selection algorithms for microarray data analysis that will be built upon “a mathematically rigorous framework that models gene selection problems, with careful consideration of the significance of the biological characteristics of the problem,” according to the grant abstract.