Opportunity Information: Apply for RFA HG 16 004
Apply for RFA HG 16 004
- The HHS-NIH11 in the health sector is offering a public funding opportunity titled "Computational Analysis of the Encyclopedia of DNA Elements (ENCODE) Data (U01)" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.172,.
- This funding opportunity was created on Jan 15, 2016 and posted on Jan 15, 2016.
- Applicants must submit their applications by Mar 21, 2016. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Eligible applicants include: State governments, County governments, City or township governments, Special district governments, Independent school districts, Public and State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities/Indian housing authorities, Native American tribal organizations (other than Federally recognized tribal governments), Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education, Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education, Private institutions of higher education, For profit organizations other than small businesses, Small businesses, Others (see text field entitled Additional Information on Eligibility for clarification).
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Opportunity Summary:
The grant opportunity titled "Computational Analysis of the Encyclopedia of DNA Elements (ENCODE) Data (U01)" (Funding Opportunity Number RFA HG 16 004) is a National Institutes of Health (NIH) cooperative agreement designed to fund research groups that can create and apply advanced computational methods to ENCODE Project data. ENCODE is a major functional genomics effort that has generated large-scale datasets and catalogs of candidate functional DNA elements across the human and mouse genomes. This opportunity focuses on improving the usefulness of those catalogs by developing better ways to analyze, integrate, interpret, and translate ENCODE data into clearer biological conclusions and more actionable genomic annotations.
A central aim of the FOA is method development and methodological innovation. Applicants are expected to propose computational approaches that push beyond standard pipelines, such as new algorithms for identifying, refining, or prioritizing candidate regulatory elements; improved statistical frameworks for handling complex genomic signals; and more accurate models for interpreting functional genomics assays across different tissues, cell types, and experimental conditions. Because ENCODE datasets are large, heterogeneous, and often high dimensional, proposals that address issues like noise, batch effects, reproducibility, uncertainty quantification, and cross-assay integration fit well within the intent of the program.
Another major theme is integrative analysis across projects and data types. The FOA explicitly encourages combining ENCODE resources with related functional genomics datasets from other large initiatives to extract new biological insights. In practice, this can include meta-analytic strategies that align ENCODE maps with epigenomic, transcriptomic, chromatin conformation, or single-cell resources, as well as approaches that harmonize annotations across platforms and consortia. The goal is not just data aggregation, but creating coherent interpretations of genome function that improve confidence in which genomic regions are likely to be functional, in what contexts they act, and how they influence gene regulation.
The opportunity also highlights translationally relevant computational work, particularly around human disease genetics. One example called out is using ENCODE data to improve the interpretation of disease mapping studies, including genome-wide association studies (GWAS) and other variant discovery efforts, to help identify likely causal variants and mechanisms. This can involve fine-mapping frameworks that incorporate functional priors, models that connect noncoding variants to target genes and cell types, and strategies for prioritizing variants for experimental follow-up. By leveraging ENCODE functional element catalogs, applicants can propose methods that better connect statistical association signals to underlying regulatory biology.
In addition, the FOA supports development of tools aimed at functional annotation and interpretation of individual genomes, aligning with longer-term goals in personalized genomic medicine. This includes computational tools, frameworks, or user-facing software that can take personal genomic variation and place it into functional context using ENCODE-based annotations, thereby helping researchers or clinicians interpret potential regulatory impacts. While the FOA does not restrict proposals to software development alone, tools that are broadly usable, well-documented, and that meaningfully improve interpretation of genome function are consistent with the program’s intent.
From an administrative and eligibility standpoint, this is a discretionary NIH funding opportunity issued by HHS-NIH (listed as HHS-NIH11) using the U01 cooperative agreement mechanism, which typically implies substantial NIH programmatic involvement during the project period compared with a standard research project grant. The funding activity category is health, and the CFDA numbers listed are 93.172 (and an additional listed code in the source data). The applicant pool is broad and includes various levels of government entities, public and private institutions of higher education, nonprofit organizations with or without 501(c)(3) status, for-profit organizations (other than small businesses), small businesses, tribal governments and organizations, and other eligible applicants as described in the FOA’s eligibility clarification text. The opportunity was posted and created on January 15, 2016, with an original and current closing date of March 21, 2016. The source text does not provide an award ceiling or expected number of awards, indicating those details were either unspecified in the excerpt or provided elsewhere in the full announcement.
Overall, this FOA is aimed at teams that can substantially improve how the research community extracts knowledge from ENCODE’s functional genomics resources. Strong applications under this program would typically center on rigorous computational innovation, careful validation and benchmarking, and clear plans for generating interpretable outputs that enhance the functional element catalogs for human and mouse genomes, while also enabling downstream applications in disease variant interpretation and genome annotation.
FAQs: Computational Analysis of the Encyclopedia of DNA Elements (ENCODE) Data (U01) - RFA HG 16 004
What is the title and funding opportunity number for this grant?
The opportunity is titled "Computational Analysis of the Encyclopedia of DNA Elements (ENCODE) Data (U01)" and the Funding Opportunity Number is RFA HG 16 004.
Which agency is offering this opportunity?
This is a National Institutes of Health (NIH) funding opportunity issued by HHS-NIH (listed as HHS-NIH11).
What type of funding mechanism is used?
The award mechanism is a U01 cooperative agreement. This mechanism generally involves substantial NIH programmatic involvement during the project period compared with a standard research project grant.
What is the main purpose of the program?
The program is designed to fund research groups that can create and apply advanced computational methods to ENCODE Project data, with the goal of improving how ENCODE functional element catalogs are analyzed, integrated, interpreted, and translated into clearer biological conclusions and more actionable genomic annotations.
What is ENCODE and why is it central to this FOA?
ENCODE is a major functional genomics effort that has generated large-scale datasets and catalogs of candidate functional DNA elements across the human and mouse genomes. This FOA focuses on computational work that improves the usefulness and interpretability of those ENCODE-generated catalogs.
Is this FOA focused more on method development or on routine analysis?
A central aim is method development and methodological innovation. Applicants are expected to propose computational approaches that go beyond standard pipelines, rather than simply applying routine or established workflows.
What kinds of computational innovations are encouraged?
Examples described in the opportunity include new algorithms for identifying, refining, or prioritizing candidate regulatory elements; improved statistical frameworks for complex genomic signals; and more accurate models for interpreting functional genomics assays across tissues, cell types, and experimental conditions.
Does the FOA encourage work on common large-scale genomics analysis challenges?
Yes. Because ENCODE datasets are large, heterogeneous, and often high dimensional, proposals that address noise, batch effects, reproducibility, uncertainty quantification, and cross-assay integration align well with the stated intent.
Is integrative analysis across data types and projects part of the scope?
Yes. A major theme is integrative analysis across projects and data types, including combining ENCODE resources with related functional genomics datasets from other large initiatives to extract new biological insights.
What does "integrative analysis" mean in the context of this FOA?
It can include meta-analytic strategies that align ENCODE maps with epigenomic, transcriptomic, chromatin conformation, or single-cell resources, as well as approaches that harmonize annotations across platforms and consortia. The emphasis is on producing coherent interpretations of genome function, not just aggregating data.
What outcomes is the FOA aiming for from integration and interpretation?
The goal is to improve confidence in which genomic regions are likely to be functional, in what contexts they act, and how they influence gene regulation, leading to clearer biological conclusions and more actionable annotations.
Does the FOA include disease genetics or translational applications?
Yes. The opportunity highlights translationally relevant computational work, particularly around human disease genetics, including using ENCODE data to improve interpretation of disease mapping studies such as GWAS and other variant discovery efforts.
What kinds of disease-variant interpretation methods are within scope?
Examples mentioned include fine-mapping frameworks that incorporate functional priors, models that connect noncoding variants to target genes and cell types, and strategies for prioritizing variants for experimental follow-up using ENCODE functional element catalogs.
Is software/tool development supported?
Yes. The FOA supports development of computational tools, frameworks, or user-facing software aimed at functional annotation and interpretation of individual genomes using ENCODE-based annotations.
Does a proposal have to be software-only to fit this FOA?
No. The FOA does not restrict proposals to software development alone, but broadly usable, well-documented tools that meaningfully improve interpretation of genome function are consistent with the program's intent.
Is the FOA limited to human genomics, mouse genomics, or both?
ENCODE catalogs described in the opportunity span both the human and mouse genomes, and the FOA is framed around improving these catalogs and their interpretation.
Who is eligible to apply based on the information provided?
The applicant pool is broad and includes various levels of government entities; public and private institutions of higher education; nonprofit organizations with or without 501(c)(3) status; for-profit organizations (other than small businesses); small businesses; tribal governments and organizations; and other eligible applicants as described in the FOA's eligibility clarification text.
Are small businesses eligible?
Yes. Small businesses are listed among eligible applicants.
Are for-profit organizations eligible?
Yes. For-profit organizations are listed as eligible, with the note that eligibility includes for-profit organizations other than small businesses, and small businesses are also listed separately as eligible.
Are nonprofit organizations eligible even if they do not have 501(c)(3) status?
Yes. Nonprofit organizations with or without 501(c)(3) status are listed among eligible applicants.
Are tribal governments and organizations eligible?
Yes. Tribal governments and organizations are included in the eligible applicant pool.
What is the funding activity category?
The funding activity category is health.
What CFDA numbers are associated with this opportunity?
The CFDA number listed is 93.172, and the source also notes an additional listed code in the source data.
When was the opportunity posted?
The opportunity was posted and created on January 15, 2016.
What was the closing date?
The original and current closing date listed is March 21, 2016.
Is an award ceiling provided in the information shown?
No. The source text provided does not include an award ceiling, suggesting it was unspecified in the excerpt or provided elsewhere in the full announcement.
Is the expected number of awards provided?
No. The information provided does not specify the expected number of awards, indicating that detail was not included in the excerpt or was available elsewhere in the full announcement.
What characteristics would a strong application typically include, based on the description?
The description suggests strong applications would center on rigorous computational innovation, careful validation and benchmarking, and clear plans for generating interpretable outputs that enhance ENCODE functional element catalogs for human and mouse genomes and enable downstream applications such as disease variant interpretation and genome annotation.
Does the FOA emphasize interpretability and actionable outputs?
Yes. A repeated theme is translating ENCODE data into clearer biological conclusions and more actionable genomic annotations, including outputs that improve confidence about functional regions and their contexts.
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