Cross-species meta-analysis tool for high-throughput genomic research. Start date: Aug. 10, 2006. Expires: Feb. 9, 2007. Amount: $157,299. Principal investigator: Saeid Akhtari. Institution: Nextbio. NIH institute: NIGMS.
Proposal to create a meta-analysis application for mining multiple high-throughput study results across different model organisms.
Comparative systematic genetics for cardiovascular disease gene identification. Start date: Aug. 15, 2006. Expires: July 31, 2007. Amount: $182,732. Principal investigator: Joel Bader. Institution: Omicia. NIH institute: NHLBI.
Proposal to identify gene variants that increase the risk of developing cardiovascular disease. Aims include the creation of a database of phenotypes observed in systematic genetic screens conducted in yeast, worm, fly, zebrafish, and other model organisms; the development of data-mining algorithms to identify modules of genes whose deletion or silencing phenotypes show similar patterns across model organisms; and the identification of modules that are enriched for genes with known variants relevant to cardiovascular disease.
3D Probabilistic Profiles of Protein/Peptide Interactions. Start date: Sept. 1, 2006. Expires: May 31, 2007. Amount: $106,873. Principal investigator: Richard Fine. Institution: Biocomputing Group. NIH institute: NIGMS.
Supports development of a new approach to describe and predict peptide-protein interactions for structurally solved proteins using Markov random fields. “Free energy minimization of the MRF yields a probability distribution called a 3D probabilistic peptide profile or 3D profile. The 3D profile probabilistically specifies types, locations, orientations, and conformations of amino acids within active sites that can be connected to form energetically favorable, preferably long, polypeptide chains,” according to the grant abstract. 3D profiles can then be used to either recognize peptides that will bind or to generate combinatorial libraries of peptides for testing.
Support Vector Machine modeling software for improving RNAi efficacy prediction. Start date: Sept. 1, 2006. Expires: Feb. 28, 2007. Amount: $97,773. Principal investigator: Andrew Peek. Institution: Integrated DNA Technologies. NIH institute: NIGMS.
Supports continued development of a feature-mapping method called binary base mapping that improves the ability of a support vector machine to predict RNAi activities when compared to two unit vector mapping and N-gram mapping. According to the grant abstract, the binary base SVM method has “greater predictive accuracy than a recently published neural network machine learning method, on the same training and testing data.”
VmatchNL — a user-friendly graphical interface for large-scale genome analysis. Start date: Sept. 1, 2006. Expires: Feb. 28, 2007. Amount: $99,722. Principal investigator: Charles Link. Institution: NewLink Genetics. NIH institute: NHGRI.
SBIR supports development of VmatchNL, a GUI-driven application of the Vmatch sequence-matching software developed at the University of Hamburg, Germany. NewLink Genetics has acquired the exclusive distribution and development rights to Vmatch and is now licensing this program to for-profit customers. An academic version of the program is freely available to non-profit researchers.
Machine Learning Prediction of Cancer Susceptibility. Start date: Sept. 1, 2006. Expires: Aug. 31, 2010. Amount: $319,800. Principal investigator: Jason Moore.
Institution: Dartmouth College. NIH institute: NLM.
Funds development of a research strategy for detecting, characterizing, and interpreting gene-gene and gene-environment interactions in a genome-wide association study of bladder cancer susceptibility. The project includes developing extensions to the ReliefF algorithm for selecting or filtering subsets of SNPs for multifactor dimensionality reduction.
A Unified System for Genome-Wide Association Analysis. Start date: Sept. 1, 2006. Expires: Aug. 31, 2009. Amount: $350,000. Principal investigator: Mark Daly. Institution: Massachusetts General Hospital. NIH institute: NHGRI.
Proposal to develop methods that combine and analyze genome-wide association data, human HapMap reference data, and external genomics resources. According to the grant abstract, “genome-wide association studies are being undertaken but without optimal methods for analyzing this data and adequate software tools for interpreting the results, these studies will not realize their potential of opening up new avenues for disease research.”
DataCoordinationCenter for the Knockout Mouse Project (KOMP). Start date: Sept. 7, 2006. Expires: Aug. 31, 2011. Amount: $1,008,000. Principal investigator: Martin Ringwald. Institution: Jackson Laboratory. NIH institute: NHGRI.
Supports the creation of a Data Coordination Center (DCC) for the NIH Knockout Mouse Project. The DCC will collect information from the KOMP research network, track progress of the knockout mutant production pipelines, and make the data available to the members of the KOMP research network and the scientific community.
Coordinating and Bioinformatics Unit for the AMDCC/MMPC. Start date: Sept. 15, 2006. Expires: June 30, 2011. Amount: $ 2,945,010. Principal investigator: Richard McIndoe. Institution: Medical College of Georgia. NIH institute: NIDDK.
The Animal Models of Diabetic Complications Consortium and the Mouse Metabolic Phenotyping Centers are two multi-center initiatives funded by the NIH. As part of the renewal of these two consortia, the NIH has decided to integrate and coordinate their activities through a Coordinating and Bioinformatics Unit.
Challenge Corpora for Biomedical Text Mining. Start date: Sept. 15, 2006. Expires: Sept. 14, 2009. Amount: $369,593. Principal investigator: Lawrence Hunter. Institution: University of Colorado, Denver. NIH institute: NLM.
Proposal to test the hypothesis that the creation of large, high-quality, biomedical corpora from multiple genres “will lead to significant improvements in the performance of biomedical text mining systems and the creation of new approaches to text mining tasks,” according to the grant abstract. Aims include constructing several large corpora, identifying factors that affect differential performance on full text versus abstracts, and developing new methods for language processing, especially of full text.
Integration and analysis tools for protein interaction networks. Start date: Sept. 17, 2006. Expires: Aug. 31, 2007. Amount: $199,950. Principal investigator: Ambuj Kumar Singh. Institution: Biomatics. NIH institute: NCRR.
Proposal to develop software for integrating and understanding protein-protein interactions. A set of tools will be developed for constructing large-scale probabilistic networks of protein interactions using data sources such as microarrays, bioimages, GO annotations, genomic data, literature, and experimental data. The techniques will be based on Bayesian networks and support vector machines, and will be scalable to large datasets. A second goal of the project is to develop tools for analyzing interaction networks for pathway discovery, motif finding, and function identification.
Cis-analysis of expression and ChIP data on Shh pathway. Start date: Sept. 19, 2006. Expires: July 31, 2009. Amount: $700,000. Principal investigator: Wing Wong. Institution: Stanford University. NIH institute: NHGRI.
Funds development of algorithms and software for identifying cis-regulatory sequence elements based on the experimental data, and the application of this approach to the study of Sonic Hedgehog (Shh) responsive gene regulation in mouse embryonic development.
A Comprehensive Test Suite for SBML. Start date: Sept. 20, 2006. Expires: Aug. 31, 2008. Amount: $302,685. Principal investigator: Michael Hucka. Institution: California Institute of Technology. NIH institute: NIGMS.
Supports development of mechanisms for validating Systems Biology Markup Language implementations. In preliminary work over the past year, the investigators have developed an SBML Semantic Test Suite that allows software developers and users to test the behavior of simulation tools against SBML benchmark models. This funding will enable the grantees to extend and refine this preliminary effort into “a comprehensive SBML Test Suite,” and to develop a web-based system that will allow anyone to upload test results, assess them independently, and optionally compare them to the results of other software tools, according to the grant abstract.
Analytical Tools for Whole Genome Association Studies. Start date: Sept. 22, 2006. Expires: June 30, 2010. Amount: $416,575. Principal investigator: David Cutler. Institution: Johns Hopkins University. NIH institute: NHGRI.
Supports development of analytical and computational techniques to perform whole-genome association studies on hundreds of thousands of SNPs simultaneously, using all available haplotypic data, “in ways that are both computationally tractable, and highly powered to find association when it does exist,” according to the grant abstract
Systems Biology of Angiogenesis: from Molecules to Therapy. Start date: Sept. 27, 2006. Expires: Aug. 31, 2009. NIH institute: NHLBI. This grant is awarded to two investigative teams:
- Johns Hopkins University. Principal investigator: Aleksander Popel. Amount: $447,670.
- Duke University. Principal investigator: Brian Annex. Amount: $388,959.
Supports development of anatomically, biophysically, and physiologically detailed integrative multi-scale computational models of angiogenesis in normal and diseased skeletal muscle. Experimentally validated computational models will be used to design and optimize therapies for human peripheral arterial obstructive disease. Experimental data will be obtained from tissues of the normal and diabetic mouse and human with and without PAOD and the results will be integrated into multi-scale computational models, spanning from the molecule, to the tissue, to the organism level.
Text Mining as a Translational Tool in Biomedicine. Start date: Sept. 30, 2006. Expires: Sept. 29, 2009. Amount: $150,219. Principal investigator: Michael Krauthammer. Institution: Yale University. NIH institute: NLM.
Funds development of a text mining-based translation informatics tool that helps geneticists in identifying likely disease candidate genes from whole-genome linkage scans.