Opportunity Information: Apply for RFA RM 15 016
The Limited Competition: Knockout Mouse Phenotyping Project Database (UM1) opportunity (RFA-RM-15-016) is a National Institutes of Health (NIH) Common Fund-funded cooperative agreement aimed at building and operating the informatics backbone for the Knockout Mouse Phenotyping Project (KOMP2). The Common Fund is designed to support cross-cutting, high-impact efforts, and this program fits that theme by tackling a foundational challenge in biology: understanding the function of every protein-coding gene. KOMP2 itself is focused on producing a comprehensive collection of null-mutant (knockout) mouse lines and systematically collecting phenotype data so researchers can link genes to biological functions and, ultimately, to disease processes.
The core purpose of this funding opportunity is to support a Data Coordination Center and Database (DCCDB) that can handle the full lifecycle of high-throughput mouse phenotype data generated by NIH-funded phenotyping projects. Those phenotyping efforts are running broad, standardized test pipelines across many knockout lines, producing large volumes of complex data that need to be quality-checked, harmonized, analyzed, and made accessible quickly. This FOA specifically calls for informatics support that enables real-time or near-real-time dissemination and makes the data genuinely usable by the broader biomedical community rather than remaining siloed within individual projects.
A major emphasis is coordination and integration, both within NIH-supported activities and with international partners under the International Mouse Phenotyping Consortium (IMPC). The aim is not just to host datasets, but to integrate them into a common database so phenotype results from different sources can be compared consistently. That requires rigorous curation and alignment with external data sources, including standardized vocabularies and links to other biological and biomedical resources, so that users can move from a gene to its associated phenotypes and related annotations without friction.
The DCCDB responsibilities described in the announcement span four main functions: curation, analysis, visualization, and dissemination. Curation involves ingesting phenotype data from many knockout lines, validating it, standardizing formats, and integrating it with relevant external resources. Analysis goes beyond routine summaries; the FOA highlights the need to further develop and validate statistical methods appropriate for large-scale, high-throughput phenotyping, where issues like batch effects, sex differences, background strain effects, variable penetrance, and multiple-testing concerns can strongly influence interpretation. Visualization and query capabilities are expected to be innovative and practical, allowing researchers to explore phenotypic patterns, compare across genes and systems, and identify potentially meaningful signals efficiently in a data-rich environment.
The intended impact is concrete: by making knockout mouse phenotype data well-curated, statistically sound, and easy to search and visualize, the program is meant to accelerate discovery of new disease models, improve recognition of phenotype patterns that may point to underlying mechanisms, and advance a more complete functional map of mammalian genes. In other words, the database is positioned as a community resource that turns a massive experimental effort into something researchers can directly use to generate hypotheses, validate targets, and connect gene disruptions to organism-level outcomes.
Administratively, this is a discretionary funding opportunity using a cooperative agreement (UM1), which typically means substantial NIH programmatic involvement during the project to coordinate activities and ensure alignment with broader program goals. Eligibility is limited to public and state-controlled institutions of higher education and private institutions of higher education, and the competition was expected to result in a single award. The original closing date listed is December 9, 2015, and the FOA is tied to the NIH Common Fund KOMP2 ecosystem, explicitly referencing the associated high-throughput phenotyping effort (RFA-RM-15-017) whose data the DCCDB would support and integrate.Apply for RFA RM 15 016
- The National Institutes of Health in the education, environment, food and nutrition, health, income security and social services sector is offering a public funding opportunity titled "Limited Competition: Knockout Mouse Phenotyping Project Database (UM1)" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.113, 93.121, 93.172, 93.173, 93.233, 93.273, 93.279, 93.310, 93.313, 93.351, 93.396, 93.837, 93.838, 93.839, 93.846, 93.847, 93.853, 93.855, 93.856, 93.859, 93.865, 93.866, 93.867.
- This funding opportunity was created on 2015-10-07.
- Applicants must submit their applications by 2015-12-09. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Public and State controlled institutions of higher education, Private institutions of higher education.
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Frequently Asked Questions (FAQs)
1. What is this funding opportunity?
This is the NIH funding opportunity titled "Limited Competition: Knockout Mouse Phenotyping Project Database (UM1)" (RFA-RM-15-016). It is a Common Fund-supported cooperative agreement focused on building and operating the informatics backbone for the Knockout Mouse Phenotyping Project (KOMP2).
2. What does KOMP2 aim to do, and why does it matter?
KOMP2 aims to produce a comprehensive collection of null-mutant (knockout) mouse lines and collect standardized phenotype data across those lines. The broader goal is to help researchers understand the function of every protein-coding gene by linking gene knockouts to organism-level phenotypes, ultimately improving connections between genes and disease processes.
3. What is the main purpose of the award described in this FOA?
The FOA supports a Data Coordination Center and Database (DCCDB) that can manage the full lifecycle of high-throughput mouse phenotype data generated by NIH-funded phenotyping projects. The intent is to ensure data are quality-checked, harmonized, analyzed, and shared rapidly so the broader biomedical community can use them effectively.
4. What is the DCCDB expected to build or operate?
The DCCDB is expected to build and operate a database and informatics infrastructure that serves as the coordinating and data-access backbone for high-throughput knockout mouse phenotyping. The focus is on turning large volumes of complex phenotype data into a consistent, usable, and community-facing resource.
5. What kinds of data will the DCCDB handle?
The DCCDB is designed to handle high-throughput phenotype data generated from broad, standardized test pipelines applied across many knockout mouse lines. These data are described as large-volume and complex, requiring systematic validation, standardization, and integration.
6. What does "full lifecycle" data management mean in this announcement?
Based on the description provided, "full lifecycle" includes data ingestion, validation and quality control, harmonization and standardization, integration with external resources, statistical analysis and method development/validation, and then dissemination through query and visualization tools for broad community use.
7. How quickly is the database expected to share data?
The FOA emphasizes real-time or near-real-time dissemination, meaning the informatics backbone should support rapid release and access rather than slow, delayed, or siloed sharing.
8. What are the four main functional responsibilities expected of the DCCDB?
The responsibilities are described as: (1) curation, (2) analysis, (3) visualization, and (4) dissemination.
9. What does "curation" include for this project?
Curation includes ingesting phenotype data from many knockout lines, validating and quality-checking it, standardizing formats, integrating it into a common database, and aligning it with relevant external resources so users can navigate from genes to phenotypes and related annotations smoothly.
10. What does the FOA expect regarding data integration and standardization?
A major emphasis is integrating phenotype results from different sources into a common database so they can be compared consistently. This includes alignment with standardized vocabularies and integration with external biological and biomedical resources to support consistent interpretation and cross-resource linking.
11. Does the FOA emphasize coordination with other groups beyond NIH projects?
Yes. The FOA highlights coordination and integration not only within NIH-supported activities but also with international partners under the International Mouse Phenotyping Consortium (IMPC).
12. What kinds of statistical or analytical challenges does the FOA call out?
The FOA specifically notes the need to further develop and validate statistical methods suitable for large-scale, high-throughput phenotyping, where issues such as batch effects, sex differences, background strain effects, variable penetrance, and multiple-testing concerns can significantly affect interpretation.
13. What are the expectations for visualization and user access?
The FOA expects innovative and practical visualization and query capabilities that help researchers explore phenotypic patterns, compare across genes and biological systems, and identify meaningful signals efficiently in a data-rich environment.
14. What is meant by making the data "usable" for the broader community?
The announcement contrasts community usability with siloed datasets. Usability here includes rapid access, rigorous curation, consistent integration across sources, statistically sound analysis, and practical tools for searching and visualizing phenotypes so researchers can readily generate and test hypotheses.
15. What is the intended scientific impact of funding this DCCDB?
The intended impact is to accelerate discovery of new disease models, improve recognition of phenotype patterns that suggest underlying mechanisms, and advance a more complete functional map of mammalian genes by making knockout mouse phenotype data well-curated, statistically sound, and easy to search and visualize.
16. What does the UM1 mechanism imply for project oversight?
The award uses a cooperative agreement (UM1). The description indicates this typically involves substantial NIH programmatic involvement during the project to coordinate activities and ensure alignment with broader program goals.
17. Is this a limited competition, and how many awards were expected?
Yes, it is described as a limited competition, and it was expected to result in a single award.
18. Who is eligible to apply based on the information provided?
Eligibility is limited to public and state-controlled institutions of higher education and private institutions of higher education.
19. What is the key relationship between this FOA and other KOMP2 activities?
This FOA supports the database and coordination function for high-throughput phenotyping efforts. It explicitly references the associated phenotyping effort (RFA-RM-15-017) whose data the DCCDB would support and integrate.
20. What was the original closing date listed for this opportunity?
The original closing date listed is December 9, 2015.
21. What is the NIH Common Fund role in this opportunity?
The FOA is Common Fund-funded. The Common Fund is described as supporting cross-cutting, high-impact efforts, and this program aligns with that by supporting an informatics foundation intended to advance broad gene-function understanding.
22. What is the main problem this database effort is trying to solve?
The effort targets a foundational challenge in biology: understanding the function of every protein-coding gene. Practically, it tackles the informatics problem of turning large-scale, standardized knockout mouse phenotyping outputs into integrated, curated, analyzable, and accessible data that researchers can compare across sources.
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