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 in alignment with the NIH Strategic Plan for Data Science, which provides a roadmap for modernizing the NIH-funded biomedical data science ecosystem.

The NHGRI 2011 strategic plan identifies bioinformatics and computational biology as a cross-cutting area “broadly relevant and fundamental across the entire spectrum of genomics and genomic medicine.” Projects involving a substantial element of computational genomics or data science account for almost a quarter of NHGRI’s FY2018 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 NHGRI Funding Policy. Particular priority is placed on “approaches generalizable across diseases and biological systems of higher order organisms.” Projects focusing on a single disease are less likely to be relevant to NHGRI than those generalizable across multiple diseases.

Grants supported under this program span many scientific topics. These grants can be categorized usefully, though neither exhaustively nor perfectly, into "Genome Analysis Tools and Software Resources" and "Data Management Resources." This structure is further explained in the text below and illustrated in Figure 1. The program structure described below should be considered as a general and not exclusive framework for organizing grants into broad scientific categories of interest to NHGRI.

Figure 1: CGDS Program Breadth.  See text-only version.

The links below lead to NIH RePORTER, a database containing information concerning NIH funded grants. Each link associated with a category, will display the relevant portfolio of grants that receive funding from the NHGRI Computational Genomics and Data Science Program.  

Genetic variation, clinical and phenotype analysis

• Genetic variation, clinical and phenotype analyses.
  • Variation and association analyses: Methods for interpreting genetic variation, associating variation with phenotypes, and analyzing population data.
     
  • Clinical and phenotype analyses: Methods for management and analysis of clinical phenotype and electronic health record (EHR) data.
     

• Genomic data processing and analysis tools
   
  • Sequencing informatics: Methods for processing, aligning, and formatting sequence reads, performing genome assembly, and extracting sequence features.
     
  • Function analyses: Methods and tools that facilitate the use of gene regulation, gene expression, epigenetic modifications, and methylation data.
     
  • Machine learning for genomics: Methods development and applications using ML to explore genomics data.
     
  • General genome data analysis tools: Development of other tools not covered above.
    
     
• Software environments for management, analysis, and visualization of genomic data: Web-hosted environments (PaaS or Saas) for genomics data storage, analysis, visualization and sharing
   

Genome Informatics training and workforce development

• Online resources for workforce development: Development of online resources (e.g. MOOCs), classroom courses or events for expanding and diversifying the genome informatics workforce.
   
• Educational resources and community engagement for transitioning genome informatics to the cloud: Non-research funding for programs designed to facilitate usage of cloud computing in genomics.
   
• Cloud computing resources for genome informatics: Infrastructure-building funding for genomic data science in cloud computing environments.
   

Data Management Resources

• Human and Model Organism Databases (HMODs): Highly-curated and broadly-used databases that provide genomic data on humans and a range of significant model organisms.
   
• Data analysis and coordination centers: Highly-curated and broadly-used databases that provide genomic data on humans and a range of significant model organisms.
   
• Informatics solutions for security and privacy of genomic data: Resources for maximizing security in genomic data storage.
   
• Genomics and Phenomics data standards: Development of standards for easier annotation and sharing of large-scale genomics data and associated metadata.

Storing, managing, standardizing and publishing the vast amounts of data produced by biomedical research is a critical mission for the National Institutes of Health. In support of this effort, on June 4 2018 NIH released its first Strategic Plan for Data Science that provides a roadmap for modernizing the NIH-funded biomedical data science ecosystem.  Over the course of the next year, NIH will begin implementing its strategy, with some elements of the plan already underway. NIH will continue to seek community input during the implementation phase.

Further information on this NIH strategic plan for data science can be found at: https://datascience.nih.gov/strategicplan.

NHGRI held an Informatics and Data Science-focused workshop on Sept 29-30, 2016, in 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 outlined the opportunities that were identified through the course of this workshop and this was presented to the NHGRI council in May 2017.

The NHGRI Genomic Data Science Working Group is a subcommittee of the National Advisory Council for Human Genome Research (NACHGR). The working group was created in 2017 to facilitate a deeper engagement of the NACHGR in the numerous and increasingly complex issues at the interface between genomics and d2ata science.

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, R21, and R43/R44): PAR-18-844, PAR-18-843, and PAR-19-061, 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-17-273 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.

Parent NIH Solicitations: Many applications are received through the NIH Parent R01 (PA-18-484 and PA-18-345) and Parent R21 (PA-18-489 and PA-18-344) solicitations. These investigator-initiated grants allow researchers to target their specific area of science relevant to NHGRI's mission (per the NHGRI Funding Policy). 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.

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 Biomedical Information Science and Technology Initiative (BISTI) provides links to various informatics-related funding opportunities across NIH and other Federal agencies.