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The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins

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Rouillard, Andrew D., Gundersen, Gregory W., Fernandez, Nicolas F., Wang, Zichen, Monteiro, Caroline D., McDermott, Michael G., Ma’ayan, Avi·2016·Database
DOI 10.1093/database/baw100PMC PMC4930834
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Abstract

Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly generate a plethora of data on the activity and levels of biomolecules within mammalian cells. At the same time, curation projects that organize knowledge from the biomedical literature into online databases are expanding. Hence, there is a wealth of information about genes, proteins and their associations, with an urgent need for data integration to achieve better knowledge extraction and data reuse. For this purpose, we developed the Harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins from over 70 major online resources. We extracted, abstracted and organized data into ∼72 million functional associations between genes/proteins and their attributes. Such attributes could be physical relationships with other biomolecules, expression in cell lines and tissues, genetic associations with knockout mouse or human phenotypes, or changes in expression after drug treatment. We stored these associations in a relational database along with rich metadata for the genes/proteins, their attributes and the original resources. The freely available Harmonizome web portal provides a graphical user interface, a web service and a mobile app for querying, browsing and downloading all of the collected data. To demonstrate the utility of the Harmonizome, we computed and visualized gene-gene and attribute-attribute similarity networks, and through unsupervised clustering, identified many unexpected relationships by combining pairs of datasets such as the association between kinase perturbations and disease signatures. We also applied supervised machine learning methods to predict novel substrates for kinases, endogenous ligands for G-protein coupled receptors, mouse phenotypes for knockout genes, and classified unannotated transmembrane proteins for likelihood of being ion channels. The Harmonizome is a comprehensive resource of knowledge about genes and proteins, and as such, it enables researchers to discover novel relationships between biological entities, as well as form novel data-driven hypotheses for experimental validation.Database URL: http://amp.pharm.mssm.edu/Harmonizome.

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  • The majority (14/16) of nearby genes or 3D interaction genes has been associated with abnormal ear/craniofacial phenotypes in various databases.

    “The majority (14/16) of nearby genes or 3D interaction genes has been associated with abnormal ear/craniofacial phenotypes in various databases [35].”

    From Combined genome-wide association study of 136 quantitative ear morphology traits in multiple populations reveal 8 novel loci (2023) · cites this paper as [35]

    Table 3 in Results > Datasets and data resources lists multiple datasets providing evidence for gene–phenotype associations, supporting the reference to databases. The paper does not report that 14 of 16 nearby or 3D-interaction genes are associated with abnormal ear or craniofacial phenotypes; searches for those terms did not find that result. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., & Ma’ayan, A.
Affiliations
Icahn School of Medicine at Mount Sinai (US)
Corresponding author
Ma’ayan, Avi
ORCID
Venue
Database
Publisher
Oxford University Press (OUP)
Published
2016
Volume
2016
Pages
baw100
ISSN
1758-0463 (ISSN-L)
DOI
10.1093/database/baw100
PMC
PMC4930834
OpenAlex
W2462053388
Cited by
1,515
References
17
Citations by year
2026: 86 · 2025: 187 · 2024: 219 · 2023: 265 · 2022: 229 · 2021: 184 · 2020: 138 · 2019: 90 · 2018: 72 · 2017: 36 · 2016: 4 · 2014: 2
FWCI
35.01
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Database (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • National Heart, Lung, and Blood Institute (U54HL127624, T32HL007824)
  • National Cancer Institute (U54CA189201)
  • National Institute of General Medical Sciences (R01GM098316)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
data integration, biomedical knowledge extraction, relational databases, drug-induced gene expression changes, kinase substrate prediction, G protein-coupled receptor ligands, ion channels, similarity networks
MeSH terms
Machine Learning, Animals, Humans, Databases, Nucleic Acid, Databases, Protein, Mice, Data Mining

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