Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources

By processing and abstracting diverse omics datasets into associations between genes and their attributes, the Harmonizome database enables researchers to explore and integrate knowledge about human genes from many central omics resources. Here, we introduce Harmonizome 3.0, a significant upgrade to the original Harmonizome database. The upgrade adds 26 datasets that contribute nearly 12 million associations between genes and various attribute types such as cells and tissues, diseases, and pathways. The upgrade has a dataset crossing feature to identify gene modules that are shared across datasets. To further explain significantly high gene set overlap between dataset pairs, a large language model (LLM) composes a paragraph that speculates about the reasons behind the high overlap. The upgrade also adds more data formats and visualization options. Datasets are downloadable as knowledge graph (KG) assertions and visualized with Uniform Manifold Approximation and Projection (UMAP) plots. The KG assertions can be explored via a user interface that visualizes gene-attribute associations as ball-and-stick diagrams. Overall, Harmonizome 3.0 is a rich resource of processed omics datasets that are provided in several AI-ready formats. Harmonizome 3.0 is available at https://maayanlab.cloud/Harmonizome/.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2024-11-20
DOI
https://doi.org/10.1093/nar/gkae1080
Citations
92
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
21.51

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources

John Erol Evangelista, Avi Ma’ayan, Daniel Clarke, Ido Diamant et al.
92 citations
Nucleic Acids Research
Bioinformatics and Genomic Networks
21.51
article

Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources

John Erol Evangelista, Avi Ma’ayan, Daniel Clarke, Ido Diamant, Nathania Lingam
article en
92 citations

Abstract

By processing and abstracting diverse omics datasets into associations between genes and their attributes, the Harmonizome database enables researchers to explore and integrate knowledge about human genes from many central omics resources. Here, we introduce Harmonizome 3.0, a significant upgrade to the original Harmonizome database. The upgrade adds 26 datasets that contribute nearly 12 million associations between genes and various attribute types such as cells and tissues, diseases, and pathways. The upgrade has a dataset crossing feature to identify gene modules that are shared across datasets. To further explain significantly high gene set overlap between dataset pairs, a large language model (LLM) composes a paragraph that speculates about the reasons behind the high overlap. The upgrade also adds more data formats and visualization options. Datasets are downloadable as knowledge graph (KG) assertions and visualized with Uniform Manifold Approximation and Projection (UMAP) plots. The KG assertions can be explored via a user interface that visualizes gene-attribute associations as ball-and-stick diagrams. Overall, Harmonizome 3.0 is a rich resource of processed omics datasets that are provided in several AI-ready formats. Harmonizome 3.0 is available at https://maayanlab.cloud/Harmonizome/.

Nucleic Acids ResearchVol. 53(D1)
Icahn School of Medicine at Mount Sinai (US)
National Institutes of Health
Industry, innovation and infrastructure
Openalex Percentile: Top 0%
Bioinformatics and Genomic Networks
21.51
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.