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NSF Bioinformatics Grants Awarded May 29 — June 26, 2008

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The Horton Project: Developing Bioinformatics Recipes for Microbial Ecology in R. Start date: June 1, 2008. Expires: May 31, 2011. Awarded amount to date: $297,834. Principal investigator: Patrick Schloss. Sponsor: University of Massachusetts Amherst.
 
Supports development of an open-source computational workbench called the “Horton project” that will allow microbial ecologists to statistically describe and compare microbial communities using DNA sequence information within the R programming environment. The project will pursue three specific aims: developing a “collection of recipes” for describing and comparing microbial communities; developing new methods for describing and comparing collections of PCR-generated sequence collections; and validating additional tools for use with pyrosequencing and metagenomic sequence collections.
 

 
Statistical Approaches to Integration of Mass Spectral and Genomic Data of Yeast Histone Modifications. Start date: June 15, 2008. Expires: May 31, 2009. Awarded amount to date: $156,221. Principal investigator: Ping Ma. Sponsor: University of Illinois at Urbana-Champaign.
 
Funds the development of new statistical and analytical methods to study the regulatory role of histone modifications in Saccharomyces cerevisiae. The investigators plan to develop statistical methods for identifying target genes of histone modifications and associated DNA sequence features of histone modifications, as well as computational and statistical methods for predicting histone modifications and their interactions.
 

 
Collaborative Research: Enhancing the Reliability of Bioinformatics Software by Correlating User Feedback and Execution Data. Start date: June 15, 2008. Expires: May 31, 2011. This grant was awarded to two investigative teams:
  • American University of Beirut. Principal investigator: Wassim Masri. Awarded amount to date: $139,236.
  • Case Western Reserve University. Principal investigator: H. Andy Podgurski. Awarded amount to date: $687,147.
Supports a project to “develop, evaluate, and refine automated techniques to help developers discover emergent reliability problems with deployed software, understand their nature and significance, and diagnose their causes.” According to the grant abstract, the project aims to address “a neglected consequence of the proliferation of scientific data sets and computational services on the Internet,” which is that the scientific community is “becoming increasingly dependent on the quality of shared data sets and the reliability of the software used to analyze them.” The approach that the investigators will follow is based on eliciting “structured feedback” from users about the problem symptoms and then automatically correlating this feedback with more detailed information about internal program dynamics and input-output mappings. “The proposed work has the potential to significantly improve the reliability of bioinformatics applications used by thousands of scientists,” according to the investigators.
 

 
Collaborative Research: An Arabidopsis Polymorphism Database. Start date: June 15, 2008. Expires: May 31, 2011. This grant was awarded to three investigative teams:
  • Cold Spring Harbor Laboratory. Principal investigator: Lincoln Stein. Awarded amount to date: $770,222.
  • University of California-Irvine. Principal investigator: Brandon Gaut. Awarded amount to date: $486,327.
  • University of Southern California. Principal investigator: Magnus Nordborg. Awarded amount to date: $635,143.
Funds development of a community resource for Arabidopsis genetics by integrating several new data sets with the existing A. thaliana genome sequence and those of two closely related species, A. lyrata and Capsella rubella. The project is “analogous to similar efforts to understand human variation and evolution by integrating vast amounts of human polymorphism data with genome sequences from closely related primates like chimpanzee and orangutan,” according to the grant abstract. The project will also provide training opportunities in bioinformatics and computational biology for biology students.
 

 
Public Service System for Automated Protein Structure Predictions. Start date: July 1, 2008. Expires: June 30, 2009. Awarded amount to date: $183,538. Principal investigator: Yang Zhang. Sponsor: University of Kansas Center for Research.
 
Funds development of a public protein structure prediction server based on the I-TASSER method. The investigators plan to combine the I-TASSER structure assembly method with a locally installed meta-threading server, or LOMETS, that “will be developed for the generation of multiple threading alignments and spatial restraints,” according to the grant abstract.
 

 
Arabidopsis 2010: Visualization Software and Data Server for Arabidopsis. Start date: July 1, 2008. Expires: June 30, 2011. Awarded amount to date: $600,000. Principal investigator: Ann Loraine. Sponsor: University of North Carolina at Charlotte.
 
Supports development of a community resource for collating large-scale genomic datasets within an interactive desktop environment. The resource will include a “Quickload” repository for large-scale data distribution, a distributed annotation server for incremental data access, and an integrated genome browser for visualization, according to the grant abstract. It will also include programming tools and tutorials to allow users to build new applications on top of the desktop platform.
 

 
Novel Optimization-Based Biclustering Algorithms for Biomedical Data Analysis. Start date: Sept. 1, 2008. Expires: Aug. 31, 2011. Awarded amount to date: $212,789. Principal investigator: Oleg Prokopyev. Sponsor: University of Pittsburgh.
 
Funds the development of optimization-based methods for handling the problem of biclustering in biomedical data analysis. Biclustering — simultaneously partitioning a set of data samples and a set of data attributes into subsets — “has great significance for data analysis in a variety of biomedical applications,” according to the grant abstract. The investigators plan to develop a family of “new mathematical programming models to tackle the biclustering problem in biomedical data analysis.”
 

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