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Investigator Initiated Innovation in Computational Genomics and Data Science (R01 Clinical Trial Not Allowed)

National Institutes of Health · Opportunity PAR-25-228

Who can apply

• State governments
• County governments
• City or township governments
• Special district governments
• Independent school districts
• Public and State institutions of higher education
• Native American tribal governments (Federally recognized)
• Public housing authorities
• Native American tribal organizations (other)
• Nonprofits with 501(c)(3), other than higher education
• Nonprofits without 501(c)(3)
• Private institutions of higher education
• For-profit organizations other than small businesses
• Small businesses
• Others

Other Eligible Applicants include the following: Alaska Native and Native Hawaiian Serving Institutions; Asian American Native American Pacific Islander Serving Institutions (AANAPISISs); Eligible Agencies of the Federal Government; Faith-based or Community-based Organizations; Hispanic-serving Institutions; Historically Black Colleges and Universities (HBCUs); Indian/Native American Tribal Governments (Other than Federally Recognized); Non-domestic (non-U.S.) Entities (Foreign Organizations); Regional Organizations; Tribally Controlled Colleges and Universities (TCCUs) ; U.S. Territory or Possession.

About this opportunity

The purpose of this funding opportunity announcement (FOA) is to invite applications for a broad range of research efforts in computational genomics, data science, statistics, and bioinformatics relevant to one or both of basic or clinical genomic science, and broadly applicable to human health and disease. This FOA supports fundamental genomics research developing innovative analytical methodologies and approaches, early-stage development of tools and software, and refinement or hardening of software and tools of high value to the biomedical genomics community. Work supported under this FOA should be enabling for genomics and be generalizable or broadly applicable across diseases and biological systems. All applications should address how the methods would scale to address increasingly larger data sets.

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