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NIH Grants in Bioinformatics Awarded September — October 2006

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An Empirical Study of Open Source Bioinformatics Software. Start date: Sept. 27, 2006. Expires: Aug. 31, 2008. Amount: $145,936. Principal investigator: Mark Lemely. Institution: Stanford University. NIH institute: NHGRI.
 
Supports a proposal to examine whether an open source licensing model fosters the use of genomic software tools and encourages innovation in the research community. Specific aims include developing a system for monitoring the success of bioinformatics software tools; investigating the adoption of a genomic analysis software package under a dual license approach; analyzing the effect of open source and commercial licenses on genomic analysis software innovation; and formulating a public policy recommendation for genomic analysis software licensing strategies.
 

 
Bioconductor: an open computing resource for genomics. Start date: Sept. 28, 2006. Expires: July 31, 2011. Amount: $796,807. Principal investigator: Robert Gentleman. Institution: Fred Hutchinson Cancer Research Center. NIH institute: NHGRI.
 
Funds continued development of the Bioconductor software project, a set of statistical programs for computational biology based on R. The grant also funds general logistical support for software distribution and quality assurance and a research component that will involve the development of analysis techniques, including optimization of the R statistical analyses, statistical processing of Affymetrix data, analysis of SNP data, improved standards, data storage, retrievals from NCBI, sequence management, machine learning, web services, and distributed computing.
 

 
Computational Tools for Cancer Proteomics. Start date: Sept. 28, 2006. Expires: Aug. 31, 2010. Amount: $418,077. Principal investigator: Kathryn Resing. Institution: University of Colorado at Boulder. NIH institute: NCI.
 
Funds development of new computational methods to profile protein expression and phosphorylation changes in response to signaling pathways and disease states. Specific aims include developing computational tools for quantifying changes in protein abundances from samples fractionated by multidimensional liquid chromatography; increasing the accuracy of peptide and protein identifications by improving algorithms for theoretical MS/MS spectral predictions; developing statistical and computational methods to improve phosphopeptide analyses in complex samples; and developing an image recognition neural network strategy for clustering peptide and phosphopeptide features within multidimensional datasets between many samples.
 

 
Comparative Toxicogenomics Database (CTD). Start date: Aug. 18, 2006. Expires: April 30, 2011. Amount: $800,000. Principal investigator: James Boyer. Institution: Mount Desert Island Biological Lab. NIH institute: NIEHS.
 
Proposal to establish a public database, the Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org), that will include information on environmental chemicals, significant genes, and their interactions in vertebrates and invertebrates.
 

 
Computational Models for Mechanisms of Global Transcription Regulation. Start date: Sept. 27, 2006. Expires: Aug. 31, 2011. Amount: $420,000. Principal investigator: Xiaole Shirley Liu. Institution: Dana-Farber Cancer Institute. NIH institute: NHGRI.
 
Funds development of algorithms to analyze chromatin immunoprecipitation-on-chip experiments. The investigators will develop an open-source algorithm to identify genomic regions enriched by transcription factor ChIP-chip on Affymetrix tiled arrays; analyze performance variability introduced in ChIP-chip procedures, tiled array platforms, and analysis methods; implement a public web server with integrated tools for sequence analysis of global ChIP-chip regions in mammalian genomes; and identify transcription factor binding partners and regulated genes.
 

 
Algorithm for drug discovery. Start date: Sept. 30, 2006. Expires: Aug. 31, 2008. Amount: $74,250. Principal investigator: Gregory Elmer. Institution: University of Maryland Baltimore Professional School. NIH institute: NIDA.
 
Funds development of an approach for discovering the psychopharmacological properties of a drug based on an algorithm called Pattern Arrays that is capable of screening more than 100,000 potential behavioral endpoints within a single session “and identifying those patterns of behavior that best characterize the particular drug,” according to the grant abstract. The grant supports a study to “explore the capacity of the Pattern Array to characterize psychoactive drugs with a high degree of specificity.” The investigators will first create a database of psychopharmacological profiles using drugs from different classes and drugs within a class that have different mechanisms of action. They will then determine the algorithmically derived pattern profile of potential cocaine therapeutics.
 

 
Enabling new discoveries in pharmacogenomics through a genomic-driven nosology. Start date: Sept. 30, 2006. Expires: Aug. 31, 2011. Amount: $391,250. Principal investigator: Atul Butte. Institution: Stanford University. NIH institute: NIGMS.
 
Funds an exploration of Carl Linnaeus’s concept of systematic nosology, or the classification of disease, on the genomic scale. “We hypothesize that genomic data, medical knowledge, and structured vocabularies have advanced to the point that we can begin to modernize the classification of disease, similar to how DNA sequencing has modernized taxonomy,” the investigators note in the grant abstract. A genomics-based nosology “would serve as the beginnings of a continuous scale for pathology, where we could quantitate how similar one disease is to another,” they add. The investigators propose a collaboration between the National Center for Biomedical Ontology and the Pharmacogenetic and Pharmacogenomic Knowledge Base that will bring together researchers in bioinformatics, medical informatics, ontologies, and pharmacogenomics “to develop a novel methodological approach to create the first genomic classification of medicine and apply this nosology to discover new relations between drugs and genes.”
 

 
Analysis and Statistical Validation of Proteomic Datasets. Start date: Sept. 27, 2006. Expires: Aug. 31, 2010. Amount: $322,599. Principal investigator: Alexey Nesvizhskii. Institution: University of Michigan at Ann Arbor. NIH institute: NCI.
 
Proposal to develop a set of statistical models and algorithms for analyzing large-scale quantitative tandem mass-spectrometry-based proteomic datasets from human clinical cancer specimens. The investigators will develop data analysis methods and algorithms for statistical validation of peptide assignments to MS/MS spectra; develop an integrated, probability-based informatics approach for assembling peptides into proteins and for inferring the identities and changes in the abundance of proteins between compared samples; introduce multivariate metrics for assessing the quality of MS/MS data and design iterative computational strategies for reanalysis of unassigned high-quality spectra; and develop statistical models for quantifying error rates in composite databases of peptide and protein identifications collected from different studies.
 

 
Training in Biomedical Discovery from Large Scale Data Sets. Start date: Sept. 30, 2006. Expires: July 31, 2010. Amount: $114,134. Principal investigator: Timothy Palzkill. Institution: Baylor College of Medicine. NIH institute: NIDA.
 
Supports a program to train students to become proficient in the use of large-scale data sets for biomedical research, including methods for data acquisition, computation, and data integration.
 

 
New Proteomic Algorithms to Identify Mutant or Modified Proteins. Start date: Sept. 27, 2006. Expires: Aug. 31, 2008. Amount: $306,250. Principal investigator: David Lee Roth. Institution: Vanderbilt University. NIH institute: NCI.
 
Supports an integrated set of algorithms designed to address shortcomings in proteomic data analysis. The investigators will first develop "sequence tagging" software to infer partial sequences from tandem mass spectra “by repurposing research in database search algorithms,” according to the grant abstract. They will also create algorithms to reconcile partial peptide matches to these spectra in order to identify peptides that vary from reference sequences by mutations and modifications, and will develop a modular framework for assembling these peptide identifications into proteins that will incorporate estimated false positive rates and multiple forms of peptides.
 

 
Development of ThermoBLAST: Improving the Specificity of Probes and Primers. Start date: Sept. 29, 2006. Expires: March 31, 2007. Amount: $115,025. Principal investigator: Donald Hicks. Institution: DNA Software. NIH institute: NHGRI.
 
SBIR funds development of a probe-design software tool that accounts for sequence-dependent thermodynamics as well as sequence similarity. “For several reasons, the Blast algorithm fails to identify many candidates for mis-hybridization, representing potential false positives. Thus there is a need to develop an algorithm that combines the efficiency of Blast with the thermodynamic rules for hybridization,” according to the grant abstract. The proposed software tool, ThermoBlast, will be integrated into DNA Software’s Oligonucleotide Modeling Platform, “resulting in automated design of more specific probes and primers that can be designed more quickly and more economically.”
 

 
Combinatorial Genomics in Cancer. Start date: Sept. 26, 2006. Expires: July 31, 2011. Amount: $274,238. Principal investigator: Steve Cole. Institution: University of California, Los Angeles. NIH institute: NCI.
 
Proposal to use a variant of the machine learning algorithm PRIM to identify “disjunctive sets of conjunctive genetic alterations that cause specific cancers or provide prognostic information about clinical course and treatment efficacy,” according to the grant abstract. Goals include the development of a graphical user interface to support combinatorial genomic analyses by biologists with limited computational background; optimized combinatorial prediction of disease progression and treatment response; and the development of PRIM-based statistical models to identify functional complementation groups of genetic alterations and transcriptional control signals.
 

 
A Platform for Pattern-Based Proteomic Biomarker Discovery. Start date: Sept. 27, 2006. Expires: Aug. 31, 2009. Amount: $330,000. Principal investigator: Denkanikota Mani. Institution: Massachusetts Institute of Technology. NIH institute: NCI.
 
Proposal to develop a high throughput analytical platform for biomarker discovery that combines identity-based methods and pattern-based methods. The approach uses sequence-identified peptides to guide the alignment of unidentified m/z peaks and to correct for chromatographic variation. The software “will employ mathematically and statistically sound algorithms to match unidentified peaks across multiple samples, integrate peptide intensities into associated protein abundance, and use advanced pattern recognition tools for differential marker selection and quantitation,” according to the grant abstract. The biomarker discovery platform will be deployed as a caBIG service.
 

 
PICquant — An integrated platform for biomarker discovery. Start date: Sept. 27, 2006. Expires: Aug. 31, 2010. Amount: $303,000. Principal investigator: Dennis Templeton. Institution: University of Virginia Charlottesville. NIH institute: NCI.
 
Supports development of an analytic platform for urine biomarker discovery using 13C phenylisocyanate labeling. The platform includes custom designed software called PICquant for automatically quantifying labeled peaks, a spectrum-comparison algorithm that groups spectra into a registry of spectra representing unique peptide families, and enhanced peptide sequencing by distinguishing b- and y-ion series in CID spectra.
 

 
Novel Statistical Methods for Human Gene Mapping. Start date: Sept. 27, 2006. Expires: Aug. 31, 2009. Amount: $306,000. Principal investigator: Michael Epstein. Institution: Emory University. NIH institute: NHGRI.
 

Funds development of statistical methods for human gene-mapping studies. The proposed methods address topics in linkage, linkage disequilibrium, and high-dimensional genetic analyses of complex diseases and disease-related quantitative traits, and include mixed-modeling procedures and case-control likelihood procedures.

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