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BioInform s Funding Update: NSF Bioinformatics Awards to April 24, 2004


Holistic Approach to the Study of Protein Mechanisms. Start date: April 1, 2004. Expires: March 31, 2009. Expected total amount: $500,000. Principal investigator: Cecilia Clementi. Sponsor: William Marsh Rice University.

Supports the development of coarse-grained models to study the mechanism and dynamics of the protein folding process. Both inverse Monte Carlo and dissipative particle dynamics methods will be used to seek effective potentials, and an algorithm developed for robotics applications will be used for motion-planning on the protein landscape.

Methods for Comparative Genomics. Start Date: March 15, 2004. Expires: March 31, 2009. Expected total amount: $557,162. Principal investigator: Serafim Batzoglou. Sponsor: Stanford University.

Funds the evaluation of emerging methods of genome comparison, to improve existing methods and to develop new ones when appropriate. The concentration will be on developing comprehensive alignments of whole genomes to locate biologically functional and evolutionarily constrained elements, and to trace their evolutionary history.

Development of New Digital Library Applications in the Context of a Basic Ontology for Biosystematics Information Using the Literature of Entomology (Ants). Start Date: April 1, 2004. Expires: February 28, 2007. Expected total amount: $559,245. Investigator: Thomas Moritz. Sponsor: American Museum of Natural History.

Project will design and test approaches to mark up and extract scientific data from a corpus drawn from the biosystematics literature of entomology and to develop a set of search and retrieval applications based on an ontology for this area of research. The project will build on digital library work underway at the American Museum of Natural History, on biological informatics at Ohio State University, and on computer science at Universitat Magdeburg in Germany.

Efficient Algorithms for Computational Problems in Bioinformatics Via Combinatorial and Geometric Techniques. Start date: April 15, 2004. Expires: March 31, 2009. Current-year amount: $72,239. Principal investigator: Bhaskar DasGupta. Sponsor: University of Illinois, Chicago.

Project will apply combinatorial and geometric optimization techniques to design efficient algorithms for three research areas in bioinformatics: substructure similarity identification; inverse protein folding; and test set problems.

Combinatorial Algorithms for Biological Data Clustering. Start date: Sept. 26, 2003. Expires: Aug. 31, 2007. Expected total amount: $550,000. Principal investigator: Ying Xu. Sponsor: University of Georgia Research Foundation.

Project will develop a general framework for biological data clustering, which will be applicable to a large class of biological data analysis problems. The clustering framework will be implemented as a set of library functions so that other researchers can build their own clustering capabilities more efficiently. The foundation of the framework is a minimum spanning tree representation of a data set and its relationships with clustering.

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The Scan

Genetic Tests Lead to Potential Prognostic Variants in Dutch Children With Dilated Cardiomyopathy

Researchers in Circulation: Genomic and Precision Medicine found that the presence of pathogenic or likely pathogenic variants was linked to increased risk of death and poorer outcomes in children with pediatric dilated cardiomyopathy.

Fragile X Syndrome Mutations Found With Comprehensive Testing Method

Researchers in Clinical Chemistry found fragile X syndrome expansions and other FMR1 mutations with ties to the intellectual disability condition using a long-range PCR and long-read sequencing approach.

Team Presents Strategy for Speedy Species Detection in Metagenomic Sequence Data

A computational approach presented in PLOS Computational Biology produced fewer false-positive species identifications in simulated and authentic metagenomic sequences.

Genetic Risk Factors for Hypertension Can Help Identify Those at Risk for Cardiovascular Disease

Genetically predicted high blood pressure risk is also associated with increased cardiovascular disease risk, a new JAMA Cardiology study says.