The IGVF catalog—from genetic variation to function

Genomic variation between individuals is essential for understanding how differences in the genome sequence affect molecular and cellular processes. The Impact of Genomic Variation on Function (IGVF) Consortium aims to uncover the relationships among genomic variation, genome function, and phenotypes by combining experimental techniques, such as single-cell mapping and genomic perturbation assays, with computational approaches such as machine learning-based predictive modeling. The IGVF Data and Administrative Coordinating Centers collect, analyze, and disseminate data and results from across the consortium through an open-source platform called the IGVF Catalog. This resource includes, but is not limited to, data on the effects of coding variants on protein abundance and function, noncoding variants on enhancer activity (measured by MPRA or predicted computationally), and associations between variants and quantitative traits. All data are organized within a graph database comprising over 50 types of data collections with nearly 3 billion nodes and over 7.5 billion edges. The Catalog offers public API endpoints (https://api.catalogkg.igvf.org/) and a user-friendly interface for exploring, querying, and visualizing the data at https://catalog.igvf.org. We expect that this open-access platform will support the broader scientific community to advance our understanding of how genomic variation influences biology and disease.

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Publication Details

Journal
Nucleic Acids Research
Published
2025-12-08
DOI
https://doi.org/10.1093/nar/gkaf1341
Citations
3
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
2.42
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article

The IGVF catalog—from genetic variation to function

Lucinda Antonacci-Fulton, Otto Jolanki, Douglas M. Fowler, Sarah Cody et al.
3 citations
Nucleic Acids Research
Single-cell and spatial transcriptomics
2.42
article

The IGVF catalog—from genetic variation to function

Lucinda Antonacci-Fulton, Otto Jolanki, Douglas M. Fowler, Sarah Cody, Rintsen N. Sherpa, Meenakshi S. Kagda, Timothy E. Reddy, Alan P. Boyle, Ian Whaling, Joel Rozowsky, Yuriy Sverchkov, Juan F Macias-Velasco, Yuanhao Huang, Shane Liu, Kushal K. Dey, Michael A. Calderwood, Shengcheng Dong, Pedro Assis, Mark Gerstein, Michael T. Montgomery, Feng Yue, Michael I. Love, Predrag Radivojac, Daniel Zeiberg, J. Michael Cherry, Lea M. Starita, Mark Craven, Benjamin C. Hitz, Charles A. Gersbach, Daofeng Li, Vikas Pejaver, Yile Chen, Nadav Ahituv, William H. Majoros, Christopher P. Castro, Timothy Bergquist, Tianjie Liu, Mingjie Li, Ting Wang, Jie Liu, Jian Ma, Shantanu Jain, J Engreitz, Mpathi Nzima, Xiangmeng Cai, Riya Sinha, Wenjin Zhang
article en
3 citations

Abstract

Genomic variation between individuals is essential for understanding how differences in the genome sequence affect molecular and cellular processes. The Impact of Genomic Variation on Function (IGVF) Consortium aims to uncover the relationships among genomic variation, genome function, and phenotypes by combining experimental techniques, such as single-cell mapping and genomic perturbation assays, with computational approaches such as machine learning-based predictive modeling. The IGVF Data and Administrative Coordinating Centers collect, analyze, and disseminate data and results from across the consortium through an open-source platform called the IGVF Catalog. This resource includes, but is not limited to, data on the effects of coding variants on protein abundance and function, noncoding variants on enhancer activity (measured by MPRA or predicted computationally), and associations between variants and quantitative traits. All data are organized within a graph database comprising over 50 types of data collections with nearly 3 billion nodes and over 7.5 billion edges. The Catalog offers public API endpoints (https://api.catalogkg.igvf.org/) and a user-friendly interface for exploring, querying, and visualizing the data at https://catalog.igvf.org. We expect that this open-access platform will support the broader scientific community to advance our understanding of how genomic variation influences biology and disease.

Nucleic Acids ResearchVol. 54(D1)
QB3 (US), Northwestern University (US), University of North Carolina at Chapel Hill (US), James S. McDonnell Foundation (US), Northeastern University (US), Mount Sinai Health System (US), Memorial Sloan Kettering Cancer Center (US), University of Wisconsin–Madison (US), Duke University (US), University of California, San Francisco (US), University of Washington (US), Washington University in St. Louis (US), Cardiovascular Institute of the South (US), University of Michigan (US), Novo Nordisk Foundation (DK), University of Washington Medical Center (US), Yale University (US), Stanford Health Care (US), Dana-Farber Cancer Institute (US), Duke Medical Center (US), Duke University Hospital (US), Genetic Technologies (Australia) (AU), Carnegie Mellon University (US), The University of Texas Southwestern Medical Center (US), Stanford University (US), Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 10%
Single-cell and spatial transcriptomics
2.42
DOIOpen Access PDFOpenAlex
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