| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | February 9, 2022 |
| Latest Amendment Date: | February 9, 2022 |
| Award Number: | 2126918 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Erik Pierstorff
epiersto@nsf.gov (703)292-0000 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | February 15, 2022 |
| End Date: | September 30, 2022 (Estimated) |
| Total Intended Award Amount: | $256,000.00 |
| Total Awarded Amount to Date: | $256,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2917 GEORGIA AVE NW WASHINGTON DC US 20001-3805 (202)709-5383 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2917 GEORGIA AVE NW WASHINGTON DC US 20001-3805 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SBIR Phase I |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041, 47.084 |
ABSTRACT
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is enhancing the role of metabolite (substances used by cells for growth, reproduction and health) analysis in the growing area of systems biology research. This is expected to lead to faster and less expensive biomarker and drug discovery to allow for more accurate, reproducible, and faster clinical trials, and to accelerate basic scientific research into many areas of cellular and system-wide organismal studies. This will have a significant impact on the bottom line for drug companies and for improving health and reducing health care cost. The innovation will provide customers with a platform and expertise that enable them to increase their ability to develop biomarkers and drugs faster by: (1) allowing more metabolites to be involved in the discovery of new relationships between diseases and metabolites, potentially opening up new areas of basic research; (2) selecting disease-associated metabolites on the basis of not only statistically significant changes in metabolite levels but also correlations of interactions among metabolites in diseased vs. healthy cells; and (3) evaluating the relationships between metabolites and diseases through integration of metabolite analysis with other system-wide analytical methods (i.e. gene expression, protein levels, etc.).
The proposed project seeks to develop an innovative cloud-based platform with an interactive modular interface that allows users to easily build customized pipelines for analysis of untargeted metabolomics data. The platform will empower the opportunity to increase the number of annotated analytes and to integrate metabolomics with other omics data, thereby enhancing the involvement of metabolomics in systems biology-based biomarker and drug discovery studies. Despite a large accumulation of metabolomics data acquired over the past several years, effective use of these data for biomarker and drug discovery has been very limited. These challenges are in part due to the lack of effective tools that: (1) accurately determine the identity of disease-associated analytes; (2) help investigate the rewiring and conserved interactions among metabolites in the progression of disease; and (3) integrate multi-omics data to evaluate the relationship between metabolites and diseases at the systems level. This project will advance scientific knowledge by investigating and evaluating innovative computational methods for metabolite annotation, differential analysis of metabolite profiles, and multi-omics data integration. Furthermore, the project will lead to a cloud-based platform that enables users to build their desired data analysis workflow or pipeline by choosing from several innovative modules to analyze metabolomics data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The major goal of this Phase I SBIR project is to develop a prototype of MetCraft - a cloud-based platform for untargeted metabolomics data analysis. The platform enables users to build their desired metabolomics data analysis workflow or pipeline by choosing from a dozen innovative modules implemented into four categories: (1) data import; (2) metabolomics data pre-processing; (3) metabolite annotation; and (4) marker selection.
The pipeline can be run from a remote machine through a web browser to analyze untargeted metabolomic data acquired by liquid chromatography coupled with mass spectrometry (LC-MS). Thus, the key outcome of the project is the development and implementation of a prototype cloud-based platform that consists of a pipeline builder and more than a dozen modules for untargeted analysis of metabolomics data. Availability of such a one-stop shop is highly desired by the metabolomics community.
One graduate student was given the opportunity to participate in this project as a summer intern. In addition, several high school and undergraduate students participated in testing the developed modules during the summer of 2022. The students gained experience in evaluation of software tools developed for biomedical applications.
Although our algorithm implementations in the prototype version of MetCraft are functional, they need to be optimized to improve performance in terms of data size they handle and their execution time. The cloud provides effective alternatives that can resolve most of the performance and infrastructure limitation issues. The cloud?s dynamic resource provisioning features are ideal for applications like ours where virtually any amount of memory and computing power can be available if the data processing function demands that. By developing a highly scalable and robust version of MetCraft, we can harness this potential of the cloud and address the shortcomings identified in the current version.
Last Modified: 12/30/2022
Modified by: Dawit Mengistu
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