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Program Snapshot
The Common Fund Data Ecosystem (CFDE) aims to enable broad use of Common Fund data to accelerate discovery. CFDE is organized around five Centers that integrate data, resources, and knowledge from many Common Fund Programs. The resources created by the Centers will empower the research community to use Common Fund data sets for novel scientific research that was impossible before.
The Common Fund Data Ecosystem (CFDE) aims to enable broad use of Common Fund data to accelerate discovery. Common Fund programs generate a wide range of diverse and valuable data sets and knowledge designed to be used by the research community. However, these data and knowledge reside in different locations, and it is challenging to work with multiple digital resources in an accessible and user-friendly way. To help remedy this problem, the CFDE has created an online discovery portal ( CFDE Portal ) to help make Common Fund data sets FAIR (Findable, Accessible, Interoperable, and Reusable) and to enable researchers to search across Common Fund data sets to ask scientific and clinical questions from a single access point.
The CFDE Centers integrate data, resources, and knowledge across Common Fund Programs. Together, these resources will empower the research community to use Common Fund data sets for novel scientific research that was impossible before. This may include hypothesis generation, discovery, or validation that leads to new insights in health and disease. The CFDE is also supporting pilot projects that engage a broad community of end-users, collect feedback on the utility of Common Fund data resources, and enable novel cross-cutting biological questions to be formulated and addressed.
More information can be found in the materials from past NIH Council of Councils presentations, including the Council of Councils Working Group report on CFDE and the presentation and background materials for the concept clearance of CFDE.
Program Updates
Meet the winners of the 7th Annual Bio‑IT FAIR Data Hackathon!
Texas Advanced Computing Center (TACC) launches CFDE Cloud Workspace
Check out the CFDE Training Center’s podcast series Decoding the Data Ecosystem!
Visit the CFDE Information Center
Your gateway to the CFDE portal, technical documentation, training, and more.
Health Relevance
Watch the Overview of the Common Fund Data Ecosystem video below to learn how CFDE is helping create a brighter healthier future through scientific discovery and innovation.
Funded Research
| PI Name | Institution Name | Title |
|---|---|---|
| BAXTER, SALLY LIU (contact) PATEL, BHAVESH | UNIVERSITY OF CALIFORNIA, SAN DIEGO | FREEMIND: Focused Research Education and Experience using Multimodal and Interdisciplinary NIH Datasets |
| CHURCHILL, GARY A (contact) KUMAR, VIVEK | JACKSON LABORATORY | Short Course on Methods for MultiOmics Data Analysis |
| GICHOYA, JUDY (contact) PURKAYASTHA, SAPTARSHI TRIVEDI, HARI | EMORY UNIVERSITY | Developing a Hive Learning and Datathon Supported Course on Imaging and Multimodal Data for Resource-Limited Institutions (CIMDAR-HIVE) |
| KARSTENS, LISA ANNE (contact) ) HERSH, WILLIAM R | OREGON HEALTH & SCIENCE UNIVERSITY | Human Microbiome Data Discovery: A short course on unveiling microbial insights to human health through Common Fund Data |
| PI Name | Institution Name | Title |
|---|---|---|
| GIBSON, LAFRANCIS (contact) BURNETTE, JENNIFER DILLMAN, ALLISSA AMANDA | OAK RIDGE ASSOCIATED UNIVERSITIES | Fostering Meaningful Use of Common Fund Data |
| PI Name | Institution Name | Title |
|---|---|---|
| FONNER, JOHN | UNIVERSITY OF TEXAS AT AUSTIN | The CFDE Cloud Workspace |
| PI Name | Institution Name | Title |
|---|---|---|
| GEHLENBORG, NILS (contact) WENG, ZHIPING | HARVARD MEDICAL SCHOOL | Community Data Hub for Integrative Visualization |
| MAZUMDER, RAJA | GEORGE WASHINGTON UNIVERSITY | Enhancing and making Biomarker Knowledge FAIR using contextual CFDE data |
| SUBRAMANIAM, SHANKAR | UNIVERSITY OF CALIFORNIA, SAN DIEGO | Defining the Human Metabotype [Human metabolic status defined through genetic, diet, environmental exposure and exercise perturbations] |
| TAYLOR, DEANNE MARIE (contact) DISKIN, SHARON GOLDMUNTZ, ELIZABETH SILVERSTEIN, JONATHAN C | CHILDREN'S HOSP OF PHILADELPHIA | U24-Uncovering the Shared Genetic Origins of Childhood Cancer and Structural Birth Defects Through Enhanced Data Integration and Analysis with the CFDE Data Distillery Knowledge Graph. |
| WU, CATHY H (contact) BATEMAN, ALEX | UNIVERSITY OF DELAWARE | UniProt Partnerships with Common Fund Data Ecosystem Resources for Protein-Centric Functional Genomics |
| PI Name | Institution Name | Title |
|---|---|---|
| FLANNICK, JASON (contact) BURTT, NOEL P GAULTON, KYLE JEFFRIE | BROAD INSTITUTE, INC. | The Common Fund Knowledge Center (CFKC): providing scientifically valid knowledge from the Common Fund Data Ecosystem to a diverse biomedical research community. |
| MA'AYAN, AVI (contact) SUBRAMANIAM, SHANKAR | ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI | The CFDE Workbench |
| PI Name | Institution Name | Title |
|---|---|---|
| CHEN, JAKE YUE (contact) DAVIS, SEAN GREENE, CASEY S PING, PEIPEI WANG, WEI | UNIVERSITY OF ALABAMA AT BIRMINGHAM | CONNECT: Collaborative Network for Nurturing Ecosystems of Common Fund Team Science |
| PI Name | Institution Name | Title |
|---|---|---|
| GEORGOPOULOS, KATIA (contact) MORGAN, BRUCE A | MASSACHUSETTS GENERAL HOSPITAL | Epigenetic regulation of epidermal proinflammatory responses |
These partnerships are meant to enhance DCC-DCC interactions. In addition, these partnerships aim to demonstrate the utility of their data integration tools and approaches for CF datasets to the broader scientific community.
| Project Name and Goals | Participating DCCs |
|---|---|
| Gene Centric Prototype Dashboard This project will develop methods to harmonize gene, protein, and RNA identifiers and generate a cloud workspace that pools gene information from DCCs for use cases. | exRNA; GlyGen; GTEx; HuBMAP; IDG; Kids First; LINCS; Metabolomics |
| Clinical Observations and Vocabularies (CLOVoc I & II) The goal of the CLOVoc project is to improve the ability to query and integrate across CF datasets for a given disease/phenotype or a clinical profile; allowing secondary analyses that drive insights about health and disease. | Kids First; Metabolomics; SPARC |
| Aggregation and Sharing of Variant-centric Information This project aims to make CFDE variant data FAIR by establishing a framework to derive information about specific variants and regulatory elements from the high-volume -omics profiling datasets to interpret such non-coding variants. | exRNA; GTEx; Kids First |
| Toxicology Screening Pipeline This project will develop a pipeline infrastructure that will tag CFDE Portal records for genes, gene products, and small molecules with labels of toxicity potential for reproductive and developmental processes. | IDG; Kids First; LINCS; SPARC |
| Workflow Playbook This project will develop an interactive workflow engine that will draw knowledge from across CF DCCs. | exRNA; GlyGen; Kids First; LINCS; Metabolomics |
| RNA Seq This project will produce common harmonized RNAseq data resources for the CFDE, and harmonized processing pipeline(s) for further use, to increase the fairness and interoperability of the RNA datasets in the CFDE. | GTEx; HuBMAP; Kids First; SPARC |
| Data Distillery This partnership will produce the largest yet research knowledge graph database of integrated NIH project data, with hundreds of millions of experimental and ontological data points and relationships mapped. | 4DN; exRNA; GlyGen; GTEx; HuBMAP; IDG; Kids First; LINCS; Metabolomics; SPARC |
| Making Gene Regulatory Knowledge FAIR The project will focus on gene regulatory element knowledge as the key “stepping stone” connecting genes and pathways and regulators in tissue-specific, developmental, and disease contexts. | exRNA; GTEx; Kids First |
| PI Name | Institution Name | Title |
|---|---|---|
| WHITE, OWEN | UNIVERSITY OF MARYLAND BALTIMORE | University of Maryland NIH Data Commons Facilitation Center |
- Hugo Bellen (Baylor College of Medicine, 3U54NS093793-05S1)- This supplement will build upon Dr. Bellen’s current work on the Model Organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) by adding data from the Knockout Mouse Phenotyping (KOMP2) and Illuminating the Druggable Genome (IDG) programs. MARRVEL is a web-based tool that provides information from both humans and various animal models on genetic variations that occur in different diseases. By adding additional Common Fund datasets to the MARRVEL tool, Dr. Bellen will create a resource that not only expands our ability to search for potential drivers of disease, but also allows for research into potential drugs that may help treat these illnesses.
- Stephen Burley (Rutgers University, 3R01GM133198-01S1) – This supplement proposes to integrate the Protein Data Bank, a large, open-access resource for information on protein structures, with five Common Fund data sets (4D Nucleome, Genotype-Tissue Expression (GTEx), PHAROS (Illuminating the Druggable Genome), Metabolomics, and Knockout Mouse Phenotyping Program (KOMP2)). The proposed integration will enhance the utility of the Common Fund data sets by providing users access to protein structure information that had not previously been connected to the Common Fund data. Ultimately, this integration is expected to enable investigation of novel biological questions, and promote a more complete understanding of human health and disease.
- Robert Cornell (University of Iowa, 3R01AR062547-04S1) – This supplement will leverage data from several Common Fund data sets to explore how genes are regulated during melanocyte stem cell generation and maintenance. Melanocyte stem cells play a role in skin and hair pigmentation and are involved in several different skin disorders, including melanoma. This project proposes to integrate Knockout Mouse Phenotyping Program (KOMP2) data on mice with pigmentation defects, 4D Nucleome data from melanoma cell lines, and Genotype-Tissue Expression (GTEx) data on the relationship between gene variants and gene expression levels of melanocyte-related genes. Working across these data sets will lead to a better understanding of the complex regulation of melanocytes and melanoma.
- Trey Ideker and Nevan Krogan (University of California San Diego, 3U54CA209891-03S1) – This supplement aims to use data sets from the Library of Integrated Network-based Cellular Signatures and PHAROS (Illuminating the Druggable Genome) to develop artificial intelligence techniques to design novel molecules predicted to inhibit cancer protein targets. Molecules identified through this supplement would then be generated and tested in future research in cancer cell lines with genetic changes that are similar to those seen in patients. This research could be a first step towards developing a new approach to designing potent cancer treatments using artificial intelligence.
- Jeffrey O’Connell (University of Maryland Baltimore, 3U01HL137181-03S1) - This supplement will use data from three Common Fund Datasets including the Genotype-Tissue Expression (GTEx), the Knockout Mouse Phenotyping (KOMP2), and the Library of Integrated Network-based Cellular Signatures (LINCS) programs. These datasets will be integrated into the web-based “Omics Analysis, Search, and Information System” (OASIS) to provide automated integration of Common Fund datasets with end-user generated association results. This new capability will automatically search and highlight connections between a multitude of datasets, all with very different types of biomedical information such as genomics, metabolomics, and proteomics. Researchers with a variety of specialties (e.g. Biologists, Epidemiologists, Physicians, Clinicians) will be exposed to the power of existing Common Fund data sets and will benefit from the automated integration provided by OASIS.
- Douglas Phanstiel (University of North Carolina at Chapel Hill, 3R35GM128645-02S1) – This supplement aims to use data from the 4D Nucleome program to develop computational tools to predict pairs of genes and enhancers, which are regulatory segments of DNA that help control when genes are turned on or off. Enhancers may be located far away from the genes they regulate, making it challenging to identify which genes are the targets of a given enhancer. However, 4D Nucleome time-course data on the temporal patterns of enhancer strength, structural conformation of genetic material and associated proteins, and gene expression will be used to develop new computational approaches to predict these gene-enhancer pairs, leading to a better understanding of how genes are regulated over time.
- Pinaki Sarder (State University of New York at Buffalo, 3R01DK114485-02S1) – This supplement will use kidney tissue samples collected as part of the Genotype-Tissue Expression (GTEx) program to help develop a computational image analysis method for improved diagnosis of diabetic nephropathy. The large number of healthy kidney tissue samples available through GTEx will add to the samples already collected by Dr. Sarder, improving the computational method and leading to better diagnosis and projection of disease trajectory in patients with diabetic neuropathy.
- Edwin Silverman (Brigham and Women’s Hospital, 3U01HL089856-13S1) - This supplement will combine information from the Genotype-Tissue Expression (GTEx) and Illuminating the Druggable Genome (IDG) programs to deepen our understanding of chronic obstructive pulmonary disease (COPD), which is the third leading cause of death in the developed world. By using the genetic information present in the GTEX dataset, Dr. Silverman aims to identify new genetic changes that may be linked to COPD. Using this information, the study will then look for new potential drugs to help treat COPD by searching the IDG database Pharos.
- Ansley Stanfill (University of Tennessee Health Science Center, 3R01NR017407-02S1) – This supplement will build upon Dr. Stanfill’s current study of aneurysmal subarachnoid hemorrhage (aSAH) in Caucasian and African American cohorts. Genotype-Tissue Expression (GTEx) data will be used to examine the effects of identified genetic variants on brain tissue gene expression in neurotransmitter pathways that are predictive of disability following aSAH. These data may provide insight into the observed disparities in outcomes after aSAH between Caucasians and African Americans. Additionally, GTEx data will be used to explore whether similar gene expression changes are present in the blood, potentially identifying a surrogate marker for brain gene expression that could inform personalized treatment interventions.