Genomic Data Analysis Network: Visualization Genomic Data Center (U24)
Department of Health and Human Services
National Institutes of Health
Research Objectives and Main Requirements for This FOA
Expectations for GDACs in the Genomic Data Analysis Network. As a whole the GDACs must be able to:
- Implement bioinformatic pipelines using existing state-of-the-art tools for timely high-throughput processing and integrated analyses of genome-wide data;
- Develop and implement new bioinformatic and computational tools to capture key biological parameters such as pathway analysis, data integration with visualization, and integrated cancer biology;
- Develop pipeline and network-wide quality control methods for the system developed as a response to the goals stated above; and
- Process/integrate analytical data from other components of the network to generate disease level findings and interpretations as well as cross-disease analyses.
- These goals will necessitate continuous communication and interactions among the members of the network. Each member of the GDAC group will have distinct functions and capabilities and be responsible for individual analytical components.
Interactions of GDACs with other members of the network. All GDAC awardees, including the Visualization GDAC to be supported under this FOA, must be able to accommodate their activities in the general scheme of the coordinated interactions across the various resources as outlined below.
- Biospecimen Core Resource (BCR): The BCR serves as the tissue processing center and provides the molecular biomolecules for all CCG-approved projects. Standard operating procedures are used for clinical data collection, sample collection, pathological examination, biomolecule (e.g., DNA and RNA) extractions, quality control, laboratory data collection, and biomolecule distribution to the Cancer Genome Characterization Centers. The samples are required to have patient informed consent for the public release of data or an IRB waiver.
- Genome Characterization Centers (GCCs): GCCs conduct high-throughput comprehensive genome-wide analyses using validated technologies (e.g., gene expression profiling, detection of chromosomal segment copy numbers alteration) to reveal the spectrum of genomic changes that exist in human tumors and to identify genomic regions for further characterization. The data generated by the characterization centers will be the main (but not only) starting point for the analyses performed by the GDACs.
- Genomic Data Analysis Network (GDAN): The aggregated capabilities of the awardees from the present FOA and its companions (RFA-CA-15-018 and RFA-CA-15-020) will produce the bulk of the analysis required to interpret the data generated by the GCCs described above. This group will work closely and collaboratively with all other components of the pipeline and be responsive to the necessities of the analysis requests posed by the AWGs that will be formed for each CCG-approved project.
- Data Management, Storage and Public Access: The whole of the data generated by CCG-approved projects is presented to the scientific community though publically-available databases that contain not only the raw data and associated metadata, but all of the analysis files generated in the course of each project. At the time of this FOA, that task is performed by the DCC (through project-specific data portals) and CGHub. In the near future, these activities will be consolidated under a single entity, the GDC, where all members of the network will deposit their data.
Required Team Expertise. Each GDAC applicant team must have current expertise in computational data analysis and genomics-specific bioinformatics as well as clinical oncology and/or cancer biology. GDACs would benefit greatly by being jointly led by two investigators: (i) an expert in bioinformatics/computation and (ii) either a clinical oncologist or a cancer biologist who can help to guide the analyses and contribute to the interpretation of the results at the disease-level.
GDACs differ by the proportion of the effort devoted to the development of novel analyses, but each GDAC will be required to develop novel approaches to data analysis and data integration. The analytical pipelines will be available as a resource to the scientific community for data integration and for advanced translational evaluation of genomic data. The analytical pipelines may result in valid conclusions which are not 100% concordant due to the different analytical methods used. The GDACs will need to develop annotation which will be made available along with the pipeline to explain any differences in data interpretation to the research community.
Specific Objectives for Visualization GDAC. The proposed Visualization GDAC must have all the expertise, personnel, instrumentation and throughput capabilities that will be required for the objectives defined below (with additional specific details in Section IV, under “Research Strategy”.)
- Objective 1: Development of innovative bioinformatics and computational tools and methodologies (requires drawing clinical and biological correlations).
- Objective 2: Conducting integrative analysis of data sets generated by GCCs using the bioinformatics tools developed by each GDAC.
Objective 3: Implementing an integrative bioinformatics approach utilizing existing bioinformatic tools for timely high-throughput processing and analyses of genome-wide data.
- Data Sharing Requirements for GDACs:
Public availability of data/information generated by all genomics data resulting from NCI-supported initiatives will be critical to facilitate disease-relevant discoveries of clinical significance and a goal of this initiative. Therefore, sharing as a public resource all rigorously validated data resulting from GDACs is essential for this initiative. For details, see Section IV.2, under “Resource Sharing Plan”.
|Funding Opportunity Number:
|Funding Opportunity Title:
||Genomic Data Analysis Network: Visualization Genomic Data Center (U24)
|Funding Instrument Type:
|Category of Funding Activity:
|Expected Number of Awards:
||93.393 — Cancer Cause and Prevention Research
93.394 — Cancer Detection and Diagnosis Research
93.395 — Cancer Treatment Research
93.396 — Cancer Biology Research
|Cost Sharing or Matching Requirement:
||Oct 22, 2015
||Oct 22, 2015
|Original Closing Date for Applications:
||Jan 27, 2016
|Current Closing Date for Applications:
||Jan 27, 2016
||Feb 27, 2016
|Estimated Total Program Funding:
Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education
Public and State controlled institutions of higher education
Others (see text field entitled “Additional Information on Eligibility” for clarification)
For profit organizations other than small businesses
Private institutions of higher education
Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education
Native American tribal organizations (other than Federally recognized tribal governments)
Public housing authorities/Indian housing authorities
Independent school districts
Special district governments
Native American tribal governments (Federally recognized)
City or township governments
|Additional Information on Eligibility:
||Other Eligible Applicants include the following: Alaska Native and Native Hawaiian Serving Institutions; Asian American Native American Pacific Islander Serving Institutions (AANAPISISs); Eligible Agencies of the Federal Government; Faith-based or Community-based Organizations; Hispanic-serving Institutions; Historically Black Colleges and Universities (HBCUs); Indian/Native American Tribal Governments (Other than Federally Recognized); Non-domestic (non-U.S.) Entities (Foreign Organizations); Regional Organizations; Tribally Controlled Colleges and Universities (TCCUs) ; U.S. Territory or Possession.
||National Institutes of Health
||This funding opportunity announcement (FOA) is a part of cancer genomics programs supported by the National Cancer Institute (NCI) and managed by its Center for Cancer Genomics (CCG). The overall goal of all CCG programs is to help elucidate the mechanisms of cancer initiation and evolution, as well as resistance to therapy by means of genomic characterization of well-annotated, high quality tumor samples. The acquired knowledge could facilitate and accelerate the development of new diagnostic and prognostic markers, new targets for pharmaceutical interventions, and new cancer prevention and treatment strategies.
|Link to Additional Information:
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