Computational Genomics and Data Science Program

Computational genomics has been an important area of focus for NHGRI since the beginning of the Human Genome Project. Today, however, advances in tools and techniques for data generation are rapidly increasing the amount of data available to researchers, particularly in genomics. This increase requires researchers to rely ever more heavily on computational and data science tools for the storage, management, analysis, and visualization of data. NHGRI’s commitment to computational genomics and data science is NHGRI’s commitment to computational genomics and data science is a key component of the NHGRI 2020 Strategic Vision and is in alignment with the NIH Strategic Plan for Data Science, which provides a roadmap for modernizing the NIH-funded biomedical data science ecosystem.

Read the Genomic Data Science Fact Sheet.

See the Draft 2023-2028 NIH Strategic Plan for Data Science

The NHGRI 2020 Strategic Vision highlights the importance of bioinformatics and computational biology by stating, “all major genomics breakthroughs to date have been accompanied by the development of groundbreaking statistical and computational methods.” See an extensive outline of the 2020 Strategic Vision.

Projects involving a substantial element of computational genomics or data science account for around 30% of NHGRI’s FY2023 budget; these areas are key components of many NHGRI grants and programs.

NHGRI’s support for computational genomics and data science follows the general principles and priorities identified in the FY2023 NHGRI Funding Policy. NHGRI prioritizes funding support on “the development of resources, approaches, and technologies that accelerate and support studies focused on the structure and biology of genomes; functional genomics; the genomics of disease; the implementation and effectiveness of genomic medicine, computational genomics and data science; training, developing, and expanding the diversity of the genomics workforce; and ethical, legal, and social issues related to genomic advances."

The Computational Genomics and Data Science Program (CGDS) supports the development of advanced computational approaches, innovative data analysis tools, and data resources that provide scientific utility across the extramural research programs and divisions. The CGDS program includes a number of managed grants and programs spanning many scientific topics. These grants can be categorized usefully, though neither exhaustively nor perfectly, into three categories: Computational Genomics and Data Science Methods Development, Genomic Data Resources and Informatics Platforms, and Computational Genomics Training and Workforce Development. 

The links below lead to NIH RePORTER, a database that provides information on NIH funded grants and research activities. Each link associated with a category will display the portfolio of FY2023 grants that received funding from the NHGRI Computational Genomics and Data Science Program.

Computational Genomics and Data Science Methods Development

• Computational Methods for Clinical Genomics: Development and implementation of genomic-based clinical informatics resources and tools that harmonize scalable, sharable and computable inferences of genomic knowledge with clinical practice guidelines. Includes frameworks and collaborative tools that allow researchers to share, analyze and secure genomic data and patient information.
• Computational Methods for Functional Genomics: Development of novel methods, software and tools to analyze gene regulation, gene expression, epigenetic modifications and methylation data. Includes methods to integrate and interpret across multiple data types.
• Computational Methods for Genomic Sequencing Data: Development of novel methods, software and tools to process, align, format and visualize genomic sequence reads; perform genome assembly; and extract sequence features. Includes graph-based and other novel approaches for pangenome analysis. Also includes general genomic analysis tools.
• Computational Methods for Variation and Association Analysis: Development of novel methods, software and tools for identifying and interpreting genetic variation, elucidating the genetic architecture of human traits and disease, and analyzing population and evolutionary level genomic data.
• Privacy and Security Technologies: Development of novel methods, software and tools to maximalize security in genomic data sharing and storage.
• General Computational Tools: Any development of novel methods, software, or tools not covered in the other categories. 

Genomic Data Resources and Informatics Platforms

• Genomic and Phenotypic Measures and Standards: Development of tools and standards to facilitate sharing and analysis of large-scale genomics data, phenotype data and associated metadata. Includes approaches to harmonize phenotypic information for use in genomic analysis, such as incorporating family history information, electronic phenotyping and ontology development.
• Genomic Community Resources: Development and maintenance of resources that collect, curate, integrate and distribute comprehensive sets of genomic information from humans or biomedically relevant species. Includes software environments to store, share, analyze and visualize genomics data.

Computational Genomics Training and Workforce Development

• Training and Workforce development: Development of resources, research training, career development, classroom courses, or events for expanding and diversifying the genome informatics workforce.
   

   

  Looking for a contact in your research area? Visit the NHGRI Extramural Scientific Areas off Emphasis and Program Contacts page for the Computational Genomics and Data Science Program. 
   

2023-2028: In December 2023, NIH released a Draft 2023-2028 Strategic Plan for Data Science to solicit public comments. This updated Strategic Plan for Data Science builds on accomplishments from the initial NIH Strategic Plan for Data Science and will prepare NIH to face the acceleration of sophisticated new technologies and address the rapid rise in the quantity and diversity of data. The updated Strategic Plan supports the NIH Policy for Data Management and Sharing and embraces data-driven discovery as a powerful tool to elucidate biological processes and better characterize the health and health consequences of all people. The plan also fosters ethical use of new methodologies arising from artificial intelligence (AI) and machine learning (ML).

More information to come when the final 2023-2028 Strategic Plan for Data Science is released. 

2018-2023: As a result of the rapid changes in biomedical research and information technology, several pressing issues related to the data-resource ecosystem confront NIH and other components of the biomedical research community. To address these challenges, NIH released its first Strategic Plan for Data Science on June 4, 2018, to provide a roadmap for modernizing the NIH-funded biomedical data science ecosystem. In establishing this plan, NIH addresses storing data efficiently and securely; making data usable to as many people as possible (including researchers, institutions, and the public); developing a research workforce poised to capitalize on advances in data science and information technology; and setting policies for productive, efficient, secure, and ethical data use.

• Future Directions of the NHGRI Analysis, Visualization, and Informatics Lab-space (AnVIL)  October 29, 2021 (Virtual)
  This workshop aimed to identify gaps, challenges and future opportunities related to NHGRI’s investment in the AnVIL’s cloud-based infrastructure, tools, and services.
   
• Genomic Medicine XIII: Developing a Clinical Genomic Informatics Research Agenda  February 9-10, 2021 (Virtual)
  The goal of this meeting was to develop a research strategy on the use of genomic-based clinical informatics resources to improve the detection, treatment, and reporting of genetic disorders in clinical settings.
   
• NHGRI Extramural Informatics & Data Science Workshop September 29-30, 2016; Bethesda, MD
  The goal of the workshop was to identify and prioritize opportunities of significance to the NHGRI Computational Genomics and Data Science Program over the next 3-5 years. A report was generated that outlined the opportunities identified through the course of this workshop. This was presented to the NHGRI council in May 2017.

Investigators interested in submitting applications to NHGRI are encouraged to contact NHGRI program staff before submission to discuss their specific aims and their choice of Funding Opportunity Announcement (FOA). Contact information for NHGRI program staff is at the bottom of this page. 

Investigator Initiated Research in Computational Genomics and Data Science (R01 and R21): PAR-21-254 and PAR-21-255, 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.

Genomic Resource Grants for Community Resource Projects (U24): PAR-23-124 is tightly focused on supporting major genomic resources, including those in informatics. Potential applicants are strongly encouraged to contact NHGRI Program Staff before developing an application.

Trans-NIH Enhancement and Management of Established Biomedical Data Repositories and Knowledgebases (U24): PAR-23-237 supports the enhancement and maintenance of established, widely used data resources.

Trans-NIH Early-stage Biomedical Data Repositories and Knowledgebases (U24): PAR-23-236 supports the initial development of a data resource or pilot significant modification of an existing resource.

Development and Implementation of Clinical Informatics Tools to Enhance Patients’ Use of Genomic Information (NOSI): NOT-HG-22-011 encourages applications to develop and implement patient-facing genomic-based clinical informatics tools that facilitate or enhance patient-provider electronic communication, patient tracking and registry functions, patient self-management and support, provider electronic prescribing, test tracking, referral tracking, and health care decision-making.

Parent NIH Solicitations: R01 (PA-20-185 and PA-20-183), Parent R21 (PA-20-195 and PA-20-194), and Parent K25 (PA-20-199) solicitations. These investigator-initiated grants allow researchers to target their specific area of science relevant to NHGRI’s mission (per the NHGRI Funding Policy). Other funding opportunities include PAR-21-075, which focuses on research experiences for students seeking a master’s degree. Additionally, NIH funding opportunities for Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) grants can be found at https://sbir.nih.gov/funding.

Broadening Opportunities for Computational Genomics and Data Science Education (UE5): RFA-HG-23-002 supports educational activities that encourage individuals from diverse backgrounds, including those from groups underrepresented in the biomedical and behavioral sciences, to pursue further studies or careers in research. This is a parallel effort with the (expired) RFA-HG-22-002 Educational Hub for Enhancing Diversity in Computational Genomics and Data Science.

Other Relevant NIH Funding Opportunities 

NHGRI's Funding Opportunities page links to various NHGRI funding opportunities and provides instructions for signing up for NHGRI's funding opportunities email list.

The webpage of the Office of Data Science Strategy (ODSS) provides resources and links to various informatics-related funding opportunities across the NIH and other Federal agencies.

Expired Funding Opportunities

RFA-HG-22-020: Limited Competition: The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) (U24 Clinical Trial Not Allowed)

RFA-HG-22-021: The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space Clinical Resource (ACR) (U24 Clinical Trial Not Allowed)

RFA-HG-17-011: The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) (U24)

RFA-HG-22-002: Educational Hub for Enhancing Diversity in Computational Genomics and Data Science (U24 Clinical Trials Not Allowed)

PAR-20-097: Trans-NIH Biomedical Knowledgebase (U24)

• See PAR-23-237 and PAR-23-236 

PAR-20-089: Trans-NIH Biomedical Data Repository (U24)

• See PAR-23-237 and PAR-23-236