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BioInform s Funding Update: Some Recent NIH Awards in Bioinformatics: Jan 19, 2004

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AutoDock Software Development and Maintenance. Start date: January 2004. Expires: December 2007. Amount: NA. Principal investigator: Arthur Olson. Institution: Scripps Research Institute. NIH institute: NIGMS.

A proposal to extend the usefulness, usability, and maintainability of the AutoDock biomolecular interaction prediction software package. Goals include restructuring the AutoDock suite into a component-based framework; expanding AutoDock’s capabilities by extending the treatment of molecular representations, search methods, and scoring functions; enhancing the graphical user interface, analysis and visualization tools; and developing support mechanisms for AutoDock users and developers.


Semi-Automated Method for Annotating Repeated Sequences. Start date: February 2004. Expires: January 2006. Amount: NA. Principal investigator: Mitrick Johns. Institution: Northern Illinois University. NIH institute: NIGMS.

Funds development of a graphical method for identifying and annotating repeated sequences found on chromosome-length DNA sequence assemblies. The basic method uses a Blast search of segments of the chromosome against the entire chromosome sequence. The results are then displayed on a “Blast dotplot,” where both the x and y axes represent sections of the chromosome. Repeated sequences are seen as a series of stacked horizontal lines whose position and color shows the chromosomal location and similarity of each repeat.


Defining the Topography of Gene Expression. Start date: June 2004. Expires: NA. Amount: NA. Principal investigator: Katherine Pollard. Institution: University of California, Santa Cruz. NIH institute: NIGMS.

Supports development of several new clustering methods, including the hierarchical ordered partitioning and collapsing hybrid (HOPACH), which combines the strengths of both partitioning and agglomerative clustering methods, for gene expression data analysis. The automated HOPACH algorithm will be written in the R language and will be a contributed package to the R open source statistical software projects.

 

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