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Funded Research

Pilot Projects Enhancing Utility and Usage of Common Fund Data Sets (R03 Clinical Trial Not Allowed) RFA-RM-23-003
PI NameInstitution NameTitle
CHIU, YU-CHIAO (contact) 
GONG, YI-NAN 
UNIVERSITY OF PITTSBURGH AT PITTSBURGHIn silico screening for immune surveillance adaptation in cancer using Common Fund data resources
EVANS, CHARLES ROBERT (contact) 
KARNOVSKY, ALLA 
UNIVERSITY OF MICHIGAN AT ANN ARBORMeta-Analysis of Metabolic Determinants of Exercise Response in Common Funds Data
FURMAN, DAVID  BUCK INSTITUTE FOR RESEARCH ON AGINGIdentification of blood biomarkers predictive of organ aging
GOODS, BRITTANY ANNE DARTMOUTH COLLEGELeveraging multiple Common Fund datasets to rank cell-cell interactions for faster hypothesis generation
GURSOY, GAMZE  COLUMBIA UNIVERSITY HEALTH SCIENCESDelineating the functional impact of recurrent repeat expansions in ALS using integrative multiomic analysis
HASSOUN, SOHA TUFTS UNIVERSITY MEDFORDUsing Common Fund Datasets to Illuminate Drug-Microbial Interactions
LIANG, JIE  UNIVERSITY OF ILLINOIS AT CHICAGOPredicting 3D physical gene-enhancer interactions through integration of GTEx and 4DN data
MOORE, JILL ELIZABETH UNIV OF MASSACHUSETTS MED SCH WORCESTEREvaluating the utility of cis-regulatory element graphs for modeling gene regulation
ROKITA, JO LYNNE  CHILDREN'S HOSP OF PHILADELPHIADiscovery of neoepitope immunotherapeutic targets in diffuse pediatric high-grade gliomas
XIA, BO  BROAD INSTITUTE, INC.High-throughput Discovery of Novel Genome Organization Regulators
ZHANG, YUN  J. CRAIG VENTER INSTITUTE, INC.Cell type harmonization of single cell data in HuBMAP and GTEx

 

NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed) PA20-185
PI NameInstitution NameTitle
GEORGOPOULOS, KATIA (contact) 
MORGAN, BRUCE A 
MASSACHUSETTS GENERAL HOSPITALEpigenetic regulation of epidermal proinflammatory responses

 

NIH Common Fund Data Ecosystem (CFDE) Data Resource Center and Knowledge Center (OT2) OTA-23-004
PI NameInstitution NameTitle
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 SINAIThe CFDE Workbench

 

Pilot Projects Enhancing Utility and Usage of Common Fund Data Sets (R03 Clinical Trial Not Allowed) RFA-RM-22-007
PI NameInstitution NameTitle
AHMED, TAMER AHMED MANSOUR (contact)
BROWN, C TITUS
UNIVERSITY OF CALIFORNIA AT DAVISData-driven search of Common Fund data sets for better discoverability and novel meta-analysis
AY, FERHATLA JOLLA INSTITUTE FOR IMMUNOLOGYUsing Common Fund datasets for prioritization of disease-associated genetic variants
DAHMANE, NADIA (contact)
POLLARD, KATHERINE S
RESNICK, ADAM CAIN
WEILL MEDICAL COLL OF CORNELL UNIVDeciphering the 3D genome of pediatric brain tumors
DORRESTEIN, PIETER C (contact)
WANG, MINGXUN
UNIVERSITY OF CALIFORNIA, SAN DIEGOCross Repository Metabolomics Data and Workflow Integration
EDWARDS, NATHAN JGEORGETOWN UNIVERSITYFunctional Annotation of Glycan Motifs using Common-Fund Data Resources
FIEHN, OLIVERUNIVERSITY OF CALIFORNIA AT DAVISIntegrating metagenomics data into accurate mass stool metabolite identifications
LI, HUMAYO CLINIC ROCHESTERUncovering therapeutic-associated biomarkers via machine learning and feature engineering approaches
LIU, JIEUNIVERSITY OF MICHIGAN AT ANN ARBORAllele-specific analysis of human epigenome, transcriptome and high-resolution chromatin organization
WANG, JIEBIAO (contact)
YAN, QI
UNIVERSITY OF PITTSBURGH AT PITTSBURGHIntegration of GTEx and HuBMAP data to gain population-level cell-type-specific insights
ZHOU, CHANUNIV OF MASSACHUSETTS MED SCH WORCESTERIntegrative analysis of multi-omics data to identify and characterize long noncoding RNA-derived fusions in pediatric cancer

 

Engaging Common Fund Data Coordinating Centers to Establish the Common Fund Data Ecosystem (CFDE) (OT2) OTA-20-005
PI NameInstitution NameTitle
ARDLIE, KRISTINBROAD INSTITUTE, INC.GTEx engagement with the CFDE-CC and other DCCs towards building a data ecosystem spanning the Common Fund projects
BLOOD, PHILIP D (contact)
BORNER, KATY
SILVERSTEIN, JONATHAN C.
CARNEGIE-MELLON UNIVERSITYAmplifying the Value of HuBMAP Data Through Data Interoperability and Collaboration
HUNTER, PETER JOHN (contact)
DE BONO, BERNARD
UNIVERSITY OF AUCKLANDCross-CFDE semantic and spatial interoperability for anatomy
MA'AYAN, AVIICAHN SCHOOL OF MEDICINE AT MOUNT SINAIThe LINCS DCIC Engagement Plan with the CFDE
MARTONE, MARYANN E (contact) 
DE BONO, BERNARD    
UNIVERSITY OF CALIFORNIA, SAN DIEGOSPARC Engagement Plan with the Common Fund Data Ecosystem
MAZUMDER, RAJAGEORGE WASHINGTON UNIVERSITYHarmonization of GlyGen glycoconjugate and glycan array data for integration into CFDE
MILOSAVLJEVIC, ALEKSANDARBAYLOR COLLEGE OF MEDICINEGENOMIC INDEXING OF COMMON FUND DATASETS
OPREA, TUDOR I (contact)
MA'AYAN, AVI
UNIVERSITY OF NEW MEXICO HEALTH SCIS CTRIlluminating the Druggable Genome Data Coordinating Center - Engagement Plan with the CFDE
PARK, PETER JHARVARD MEDICAL SCHOOLInteroperability and Collaboration with the Common Fund Data Ecosystem to Improve Utility of 4DN Data
RESNICK, ADAM CAIN (contact)
DIGIOVANNA, JACK
FERRETTI, VINCENT
HEATH, ALLISON
CHILDREN'S HOSP OF PHILADELPHIAKids First Data Resource Center (KFDRC): Harnessing Data-Driven Opportunities in the Present on behalf of the Future of Common Fund Data Ecosystem (CFDE)
SUBRAMANIAM, SHANKARUNIVERSITY OF CALIFORNIA, SAN DIEGOBiomedical Data Commons Workbench (BDCW)

 

The CFDE has funded collaborative DCC partnership projects that will develop approaches and tools to harmonize data and workflows from multiple Common Fund programs enabling cross-dataset analysis. 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 GoalsParticipating 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

 

Common Fund Data Ecosystem - Coordinating Center (RFA-RM-17-026)
PI NameInstitution NameTitle
WHITE, OWENUNIVERSITY OF MARYLAND BALTIMOREUniversity of Maryland NIH Data Commons Facilitation Center

 

Pilot Projects Enhancing Utility and Usage of Common Fund Data Sets (R03 Clinical Trial Not Allowed) RFA-RM-21-007
PI NameInstitution NameTitle
CAREY, VINCENT JAMES  BRIGHAM AND WOMEN'S HOSPITALDurable Common Fund Data Interfaces and Tutorials with Bioconductor
CIESLIK, MARCIN PIOTRUNIVERSITY OF MICHIGAN AT ANN ARBORUnraveling the topological architecture and phenotypic contexture of structural variation
KRUG, KARSTEN (contact)
MANI, DENKANIKOTA R
BROAD INSTITUTE, INCUsing phosphorylation signatures of drug perturbagens to identify exercise-mimetic compounds
LAU, EDWARDUNIVERSITY OF COLORADO DENVERInvestigating systems physiology with multi-omics data
LIANG, JIEUNIVERSITY OF ILLINOIS AT CHICAGOConstructing High-Resolution Ensemble Models of 3D Single-Cell Chromatin Conformations of eQTL Loci from Integrated Analysis of 4DN-GTEx Data towards Structural Basis of Differential Gene Expression
LIU, DAJIANGPENNSYLVANIA STATE UNIV HERSHEY MED CTRMethods to maximize the utility of common fund functional genomic data in multi-ethnic genetic studies
MAGA, ALI MURATSEATTLE CHILDREN'S HOSPITALDeep Phenotyping of 3D Data for Candidate Gene Selection from Kids First Studies
MOSHIRI, ALAUNIVERSITY OF CALIFORNIA AT DAVISInterrogation and Interpretation of Common Fund Data Sets to Identify Novel Ocular Disease Genes
SIRIMULLA, SUMANUNIVERSITY OF TEXAS EL PASOUsing Common Fund datasets for xenobiotic localization
XU, JINRUIYALE UNIVERSITYUsing three-dimensional genome structure to refine eQTL detection

 

Pilot Projects Enhancing Utility and Usage of Common Fund Data Sets (R03 Clinical Trial Not Allowed) RFA-RM-19-012
PI NameInstitution NameTitle
BROWN, C TITUS UNIVERSITY OF CALIFORNIA AT DAVISLarge-scale annotation-free disease correlation analysis of the iHMP
HASSOUN, SOHATUFTS UNIVERSITY MEDFORDUsing machine learning techniques to characterize the Metabolomics Workbench Dataset
KIDD, JEFFREY MUNIVERSITY OF MICHIGAN AT ANN ARBORIncorporating Analysis of Gene Paralog Variation Into Existing Genomics Datasets
LASSEIGNE, BRITTANY NICOLEUNIVERSITY OF ALABAMA AT BIRMINGHAMUsing Common Fund data to inform rare disease preclinical models and prioritize drug repurposing
LIU, JIEUNIVERSITY OF MICHIGAN AT ANN ARBORA database for high-resolution chromatin contact maps and human genetic variants
MOSELEY, HUNTER NATHANIELUNIVERSITY OF KENTUCKYImproving Deposition Quality and FAIRness of Metabolomics Workbench
SOEMEDI, RACHEL (contact)
SOFER, TAMAR
BROWN UNIVERSITYUsing GTEx to assess the functionality of sex-biased variants
TAYLOR, DEANNE MARIECHILDREN'S HOSP OF PHILADELPHIALeveraging Common Fund data for feature selection in Kids First studies.
WENG, ZHIPINGUNIV OF MASSACHUSETTS MED SCH WORCESTERConstructing multi-omics regulatory networks for functional variant annotation
WORLEY, KIM CBAYLOR COLLEGE OF MEDICINEExpanding the List of Human Disease Genes Using the Knockout Mouse Phenotyping Program (KOMP2) Data to Reassess Human Clinical Data
ZHAO, HONGYUYALE UNIVERSITYComputational Methods to Integrate Common Fund Data for Drug Repurposing
 ZHU, JINGCHUNUNIVERSITY OF CALIFORNIA SANTA CRUZUse UCSC Xena to promote integration and usage of the KidsFirst, GTEx, and LINCS L1000 data sets

 

Notice of Special Interest (NOSI): Availability of Administrative Supplements for Enhancing Utility and Usage of Common Fund Data Sets (NOT-RM-19-009)

  • 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.

This page last reviewed on March 14, 2024