Advancing Bioinformatics, Translational Bioinformatics and Computational Biology Research (R01 Clinical Trial Optional)

The National Library of Medicine (NLM) wishes to advance groundbreaking and innovative research in bioinformatics, translational bioinformatics and computational biology, which are related areas of biomedical informatics that aim to understand biological data using storage, analytic and interpretive methods. This initiative will support NLM's mission to accelerate discovery by enhancing human health through data-driven research.

This Notice of Funding Opportunity (NOFO) aims to address the growing need to leverage transformative technologies—such as artificial intelligence (AI), machine learning, and large-scale computational platforms—to extract actionable knowledge from vast, diverse, and complex biological datasets. By enabling more effective interpretation and integration of multi-dimensional biological and biomedical data, this research will ultimately contribute to improving individual and population health outcomes.

Applications that promote interdisciplinary collaboration and focus on scalable, autonomous innovations will be encouraged. Additionally, emphasis will be placed on the development of cutting-edge methodologies that advance the field beyond predictive modeling to yielding functional insights that can explain biological phenomena and mechanisms underlying human diseases, including chronic diseases. Projects may span a wide range of topics, including but not limited to omics-based bioinformatics at the molecular level, as well as computational biology studies at the cellular, tissue, organismal, and population levels. Advancements may draw from innovations in multiple disciplines, including biology, chemistry, physics, biodata imaging, computer science, information engineering, mathematics and statistics. 

For this NOFO, areas of bioinformatics and computational biology for which methodological advances are of interest include, but are not limited to:

• Various types of omics and multi-omics studies, including genomics, transcriptomics, proteomics, epigenomics, structural or spatial omics, comparative omics, and multimodal integrations of those.
• Microbiome studies, including studies on host-microbiome interactions.
• Metagenomic and/or metaproteomic studies.
• Studies that use artificial intelligence (AI) and machine learning (ML) applications for predictive and analytical bioinformatics research.
• Computational biology method development for analysis of genomic or non-genomic biological data and related phenomena.
• Genetic variation and disease association studies, including methodological advances for determining combinatorial variant associations, and for accelerating discovery beyond rare diseases, such as for chronic diseases.
• Phenotypic studies, including methodological advances for complex trait analyses.
• Translational bioinformatic studies that aim to provide a pathway for effective treatment strategies or have a tangible effect on health outcomes.
• Translational bioinformatic studies applicable to personalized or genomic medicine.
• Pathway and/or regulatory network studies.
• Systems biology studies, including analysis and visualization of large datasets.
• Cell-cell interaction informatics, and other types of cellular/tissue/organism networking studies.
• Single cell informatics.
• Informatics related to biomarker discovery.
• Analysis of drug targets.
• Various emerging or other informatics studies on biological data, including but not limited to multimodal studies that encompass multiple data types and/or research domains, or studies relevant to bioinformatic data harmonization, integration and reuse.

Example topics of interest include, but are not limited to:

• Interdisciplinary approaches that are informed by functional biological and/or biomedical data.
• Approaches that result in whole usable pipelines, workflows or resources, rather than software products that perform segmented functions in a larger workflow.
• Tools and methods that can be developed, applied, evaluated and disseminated within the scope of the proposed project, and which are durable and autonomously updatable.
• Tools and workflows that are FAIR (Findable, Accessible, Interoperable, Reusable) and easily findable by the wide research community in well-documented, user-friendly, publicly available resources, and which encompass ethical considerations for data representation and downstream use.
• Translational bioinformatics approaches that are broadly adaptable and generalizable.
• Non-translational bioinformatics advances that contribute tangible functional knowledge for downstream use.
• Computational models that are adaptable, credible, explainable and testable by real-world data.

Research Objectives

Research approaches should foster data-driven discovery to accomplish NLM mission-related goals. Additionally, the following expectations should be addressed in all applications:

• Research should exemplify scientific rigor, feasibility, technological innovation, and the potential for significant impact across biomedical and clinical domains.
• Outputs should be transformative and generalizable across diseases and biological systems; use of specific biological systems may be permissible for proof-of-concept and initial methodology development, but applicants must demonstrate broad applicability beyond that.
• Products should be durable with the potential to inform and advance further research.
• Intended use cases must be clearly stated and evaluated, with approaches expected to be wide-reaching.
• Detailed descriptions of approaches are expected, with metrics provided for the scope and scale of the study.
• Improvements over comparable existing approaches must be documented, and any limitations, trade-offs or risks resulting from the proposed innovative approach must be described and addressed.
• Applicants must describe a plan to account for accuracy of the product(s), to maintain the product(s), and to capture and communicate considerations for downstream use.
• All awardees are expected to widely disseminate the results of their research, including software products, datasets, resources and platforms.
• For applications that incorporate the aggregation of public or non-controlled-access data, applicants are expected to address methods for mitigating privacy concerns that may arise from such aggregated data. Privacy protections for human subjects are expected to be long-term and must extend beyond the funding period.

Applications Not Responsive to this NOFO

The following types of projects will be considered non-responsive to the scope of NLM’s Advancing Bioinformatics, Translational Bioinformatics and Computational Biology Research program:

• Projects that do not align with current NIH or Department of Health and Human Services priorities, including projects that partially incorporate non-aligning activities.
• Projects not centrally focused on, or not clearly pertaining to, bioinformatics, translational bioinformatics or computational biology.
• Projects focused on social determinants of health (SDOH), or ethical, legal and social issues (ELSI).
• Projects that duplicate NLM products and databases.
• Projects that do not produce open-access datasets, methods, resources or tools.
• Projects focused on mere incremental improvements of, or on the maintenance of, existing bioinformatic tools, resources or platforms; significant innovation and advancement is expected from this initiative.
• Projects with most of the effort devoted to experimental research; only a small portion of the budget may be permitted for experimental validation of computational approaches.
• Projects narrowly focused on specific diseases and with limited potential to yield broadly applicable tools.

Non-responsive applications will not be reviewed. Potential applicants are strongly encouraged to discuss their proposed project with the Scientific/Research Contacts listed in Section VII for guidance about the application process and suitability of the project for support by NLM.

Investigators proposing NIH-defined clinical trials may refer to the Research Methods Resources website for information about developing statistical methods and study designs.