Opportunity Information: Apply for F19AS00210
The Department of the Interior, U.S. Fish and Wildlife Service is offering a discretionary cooperative agreement opportunity focused on improving how quantitative PCR (qPCR) genetic data are managed and analyzed. The central goal is to strengthen the reproducibility of qPCR data analysis workflows by funding the development of automated tools that can cope with the volume and complexity of qPCR outputs. In practical terms, the work is meant to reduce manual handling of data, standardize analysis steps, and make it easier for analysts to repeat or verify results across studies, projects, and time.
A key emphasis of the opportunity is building automation that can process both validation data and field data. qPCR projects often generate large datasets during assay validation (for example, establishing sensitivity, specificity, limits of detection, and performance across conditions) and then produce additional streams of field-generated results once the assay is deployed operationally. The Service is looking for tooling that can ingest, clean, organize, and reduce these datasets in a consistent way, which helps minimize human error and supports transparent, repeatable analytical pipelines.
In addition to automated analysis and data reduction tools, the opportunity specifically calls for queryable relational databases designed to house the different types of qPCR data generated. This points to a structured data management solution where qPCR results, metadata, validation metrics, sample identifiers, run information, controls, and other relevant records can be stored in an organized schema and retrieved through queries. The intent is not only storage, but also usability: users should be able to search, filter, join, and extract data efficiently for quality assurance, reporting, and downstream analysis, all while preserving data provenance and making the workflow easier to reproduce.
This funding opportunity (number F19AS00210) falls under environment, natural resources, and science and technology research and development, and is associated with CFDA number 15.678. The eligible applicants are public and state-controlled institutions of higher education, indicating the program is aimed at universities and similar public academic entities with the technical capacity to build bioinformatics or data engineering solutions in partnership with the Service. Because the award instrument is a cooperative agreement, it implies substantial involvement or collaboration by the Fish and Wildlife Service during the project, rather than a hands-off grant relationship, which is typical when an agency expects to participate in technical direction, coordination, or iterative development.
The award ceiling is $200,000, with one expected award, so the agency is likely seeking a single project team to deliver a cohesive set of tools and database capabilities rather than funding multiple parallel efforts. The opportunity was created on May 14, 2019, and originally closed on May 21, 2019, reflecting a short application window at the time. Overall, the opportunity is aimed at modernizing and standardizing qPCR data workflows through automation and well-designed relational databases so that results are easier to manage, analyze, audit, and reproduce across conservation, environmental monitoring, and related genetic applications.Apply for F19AS00210
- The Department of the Interior, Fish and Wildlife Service in the environment, natural resources, science and technology and other research and development sector is offering a public funding opportunity titled "Development of automated tools and databases useful for genetic data reduction and analysis" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.678.
- This funding opportunity was created on May 14, 2019.
- Applicants must submit their applications by May 21, 2019. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $200,000.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Public and State controlled institutions of higher education.
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Frequently Asked Questions (FAQs)
What is this funding opportunity about?
This is a discretionary cooperative agreement opportunity from the Department of the Interior, U.S. Fish and Wildlife Service focused on improving how quantitative PCR (qPCR) genetic data are managed and analyzed. The main aim is to make qPCR data analysis workflows more reproducible by supporting the development of automated tools that can handle large, complex qPCR outputs with less manual intervention.
What is the central goal of the project described in the opportunity?
The central goal is to strengthen the reproducibility of qPCR data analysis workflows. This includes reducing manual handling of data, standardizing analysis steps, and making it easier for analysts to repeat or verify results across different studies, projects, and time periods.
What kinds of solutions is the Fish and Wildlife Service looking to fund?
The opportunity emphasizes two major deliverables: (1) automated tools for analysis and data reduction of qPCR outputs, and (2) queryable relational databases designed to store and retrieve the different types of qPCR data generated during validation and field operations.
Why is automation a key emphasis in this opportunity?
qPCR work can generate large volumes of data, and manual handling can introduce errors and inconsistencies. Automation is intended to help ingest, clean, organize, and reduce datasets in consistent ways that support transparent and repeatable analytical pipelines.
What types of qPCR datasets should the automated tools be able to process?
The tools are expected to handle both validation data and field data. Validation datasets may include information used to establish assay sensitivity, specificity, limits of detection, and performance under different conditions. Field datasets include results produced once an assay is deployed operationally.
What does the opportunity mean by "data reduction" in the qPCR context?
Based on the description provided, data reduction refers to processing raw or high-volume qPCR outputs into organized, consistent, and usable datasets for quality assurance, reporting, and downstream analysis, while minimizing manual steps and supporting reproducibility.
What is meant by a "queryable relational database" for qPCR data?
A queryable relational database implies a structured database (with an organized schema) that can store multiple related qPCR data types and allow users to retrieve information through queries. The database should support searching, filtering, joining, and extracting data efficiently.
What kinds of information does the relational database need to store?
The opportunity indicates the database should be able to house different types of qPCR data and related records, such as qPCR results, metadata, validation metrics, sample identifiers, run information, controls, and other relevant records needed for repeatable analysis and recordkeeping.
Is the database intended only for storage?
No. The description emphasizes usability in addition to storage. Users should be able to retrieve and work with the data efficiently for quality assurance, reporting, and downstream analysis, while preserving data provenance and supporting reproducible workflows.
What is the importance of data provenance in this opportunity?
The opportunity highlights preserving data provenance so that analytical steps and outputs can be traced and verified. This supports transparency and makes it easier to reproduce results across studies and over time.
What is the funding mechanism (award instrument) for this opportunity?
The award instrument is a cooperative agreement.
What does it imply that this award is a cooperative agreement?
A cooperative agreement implies substantial involvement by the Fish and Wildlife Service during the project. Rather than a hands-off grant, the agency is expected to collaborate in areas such as coordination, technical direction, or iterative development.
Who is eligible to apply?
Eligible applicants are public and state-controlled institutions of higher education.
What kinds of organizations does "public and state-controlled institutions of higher education" include?
Based on the eligibility statement provided, the opportunity is aimed at universities and similar public academic institutions that are state-controlled and have the capacity to build relevant technical solutions in partnership with the Service.
How many awards are expected?
One award is expected.
What is the maximum award amount (award ceiling)?
The award ceiling is $200,000.
What is the opportunity number?
The opportunity number is F19AS00210.
What is the CFDA number associated with this opportunity?
The opportunity is associated with CFDA number 15.678.
What topic areas does this opportunity fall under?
The opportunity falls under environment, natural resources, and science and technology research and development.
What problem is this opportunity trying to address in qPCR workflows?
It aims to address challenges caused by the volume and complexity of qPCR outputs, including the risk of human error from manual data handling and the difficulty of standardizing analysis steps across projects. The goal is to improve consistency, auditability, and reproducibility.
How does this opportunity relate to field operations?
It explicitly calls for solutions that can handle field-generated qPCR results after assays are deployed operationally, in addition to handling validation datasets created during assay development.
When was the opportunity created?
The opportunity was created on May 14, 2019.
When did the opportunity close?
The opportunity originally closed on May 21, 2019.
How long was the original application window?
Based on the dates provided (May 14, 2019 to May 21, 2019), the original window was one week.
What is the overall intended outcome of funding this work?
The intended outcome is to modernize and standardize qPCR data workflows through automation and well-designed relational databases so that results are easier to manage, analyze, audit, and reproduce across conservation, environmental monitoring, and related genetic applications.
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