Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 181.86K | Year: 2010
DESCRIPTION (provided by applicant): High-throughput Epistasis Screening using Genetical Genomics A fast software tool is proposed for identifying potential sets of interacting genes involved in human disease pathways. A meta-analysis of marker and expression-trait studies is performed using penalized regression software running in parallel on commodity graphics cards. The research team includes experts from genomics, statistics and software acceleration. Data will come from published studies. Initial results suggest promise for our approach. Epistasis is a key area of investigation in the elucidation of human- disease pathways. eQTL experiments have shown promise in identifying epistasis for given expression traits. We will leverage the success of eQTLs by employing the results of GWAS experiments to suggest specific expression traits to study. In this way we will exploit the findings of multiple, disparate studies in an overall meta-analysis of a disease trait. Various forms of regression analysis are currently used to screen eQTL data for epistasis, especially stepwise linear regression. We will employ penalized regression techniques, because of their speed advantage, their ability to identify multiple candidates simultaneously and their relative novelty. We will apply several distinct types of penalized regression, each with its own predictor-selection characteristics. We have strong in-house expertise in penalized regression. As more and larger genomic data sets become available, effective means for combining and mining them become essential. The sheer mass of the data, moreover, will require high-performance software in order to provide analysis in reasonable time. Parallel computation is one promising area for improving software performance. We will employ the new generation of inexpensive, widely-available graphics coprocessors to run our software in parallel. Successful application will demonstrate that relevant, large- data bioinformatics solutions can be implemented on modestly-priced desktop hardware. PUBLIC HEALTH RELEVANCE: Personalized medicine is based on the observation that susceptibility to disease has a strong genetic component. This genetic component consists of groups of highly interacting genes. We will develop high- speed software able to process the huge amounts of data needed to identify these interactions and the role they play in disease susceptibility.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 179.50K | Year: 2010
DESCRIPTION (provided by applicant): A enhancement to the popular peptide search engine X Tandem is proposed, to allow it to run on new super-scalable MapReduce computer clusters. This will allow much faster and less-expensive operation, allowing proteomics researchers to routinely search for post-translational modifications (PTMs). Proteomics has led to many important advances in biological understanding. Yet, many valuable data sets are not searched for PTMs, simply because the computer power necessary to conduct the searches is not available. With this project, we plan to substantially reduce the computational cost of proteomics experiments, via a peptide search engine operating on highly-scalable computer clusters. The research team is well-qualified to undertake this research, having extensive, direct experience in all the scientific disciplines and specific software elements necessary. The research team includes experts in proteomics, mass spectrometry, peptide search, cloud computing, and MapReduce. PUBLIC HEALTH RELEVANCE: High-throughput analysis of post-translational modifications is increasingly pivotal for understanding the molecular function and dynamics of living cells. This proposal thus addresses key opportunities for applying proteomics to human health research.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.20M | Year: 2010
DESCRIPTION (provided by applicant): Heart disease is the leading cause of death in the western societies. The plasma level of high density lipoprotein cholesterol (HDL-C), the good form of cholesterol, is negatively correlated with the risk of heart disease, myocardial infarction, and coronary death. However, current composite risk score tests such as the Framingham tests identify only 1/3rd of individuals at risk, and HDL-C contributes very little to these composite risk scores. Phase I studies demonstrated that the protein composition of HDL measured by MALDI-MS differs markedly between patients with established CAD and age- and sex-matched healthy subjects. Phase I also established that CAD subjects could be distinguished from age- and sex- matched healthy subjects with a high sensitivity and specificity using HDL protein signals. The goal of the proposed work is to demonstrate that the HDL protein signals can be used to improve the accuracy of composite MI risk scores. This goal will be met through experiments that measure banked samples and new samples collected from 400 subjects who will be monitored for adverse events during the period of the project. PUBLIC HEALTH RELEVANCE: Relevance to public health: Our overall hypothesis is that pattern recognition MS analysis of HDL will be a powerful tool for detecting people at risk for myocardial infarctions (Ml). Each year, more than a million Americans have an Ml, of whom half die. However, our ability to identify subjects at increased risk for these events is severely limited. Therefore, the availability of diagnostics that could accurately predict risk in time to ward off an MI would have an enormous impact on health care costs and public health.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 203.60K | Year: 2011
DESCRIPTION (provided by applicant): The use of Graphics Processing Unit (GPU) devices is proposed to increase the speed of peptide search by as much as an order of magnitude. Peptide search engines perform the most computation-intensive task in shotgun proteomics. This proposal is to speed up peptide search using the increasingly popular approach of general-purpose computation on highly-parallel GPU devices. Computation using GPUs to gain desktop supercomputer performance at low cost is an active and fruitful area of research. This project will obtain these benefits for peptide search. As a result, researchers will be able to take experiments that today must be analyzed on a computer cluster, and instead analyze them on their desktop computer. PUBLIC HEALTH RELEVANCE: A enhancement to the popular peptide search engine X Tandem is proposed, to allow it to run in parallel on graphics processing unit (GPU) cards with dramatically faster performance. This will allow much faster and less-expensive operation, allowing proteomics researchers to analyze large proteomics experiments or search for post- translational modifications (PTMs) using their existing desktop computer. Proteomics has led to many important advances in biological understanding. Yet, manyvaluable data sets are not searched for PTMs, simply because the computer power necessary to conduct the searches is not available. With this project, we plan to substantially reduce the computational cost of proteomics experiments, via a peptide search engine making use of inexpensive, highly parallel GPU hardware. Thus, this project, if successful, will allow proteomics to be more successfully exploited for public health. The research team is well-qualified to undertake this research, having extensive, direct experience in all the scientific disciplines and specific software elements necessary. The research team includes experts in proteomics, mass spectrometry, peptide search, and GPU computing.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 222.75K | Year: 2012
DESCRIPTION (provided by applicant): This Small Business Innovation Research project addresses the problem of biomarker detection in clinical and high-throughput data. The objective is to investigate new approaches for deter- mining, from data consisting of many possibly irrelevant or redundant measurements, a highly predictive and interpretable model that involves only a small number of measurements. These new methods will be studied for modeling subjects' time-to-event (such as stroke, heart attack, or metastasis in cancer). The proposed approaches will be compared with existing methods that attempt to use relatively few mea- surements in modeling survival (time-to-event) data. The data to be analyzed will include ion-mobility and clinical data from a large cardiovascular disease cohort, as well as high-throughput genomic data from cancer research with many more measurements than samples. Relevance. Although today's advanced technologies offer the possibility of revolutionizing clinical practice, the analytical tools available for extracting information from this amount of daa are not yet sufficiently developed for targeted exploration of the underlying biology. This project directly addresses the need to make what the FDA terms IVDMIA (In-Vitro DiagnosticMultivariate Index Assays) transparent and interpretable, and is thus an opportunity to improve analysis services or products provided to companies that identify, characterize, and validate biomarkers for clinical diagnostics and drug development decisionpoints. The proposed project will produce robust methods for parsimonious biomarker detection that will speed the development of cheaper and more effective diagnostic tests for disease diagnosis, treatment monitoring, and therapeutic drug development.PUBLIC HEALTH RELEVANCE: There is a great need in medical research for prognostic models that can accurately predict time to an event, such as a heart attack, from a few observed features. These models can be used in establishing new diagnostic and screening tests, and in advancing new therapies. New methods for time-to-event modeling are proposed that will speed the development of cheaper and more effective clinical support systems, and have a far-reaching impact on public health.