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

By Andrew D. Rouillard, Gregory W. Gundersen, Nicolas F. Fernandez, Zichen Wang, Caroline D. Monteiro, Michael G. McDermott, Avi Ma’ayan. Database J. Biol. Databases Curation, 2016

1,389 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6c79b202a39804d819ccd44e7edbd24f3e33d497

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Citations in the corpus

Open Targets: a platform for therapeutic target identification and validation (2016, methodology): “Two discovery platforms are developed as part of this program: the Harmonizome (40), a comprehensive resource of knowledge about genes and proteins and PHAROS (https://pharos.nih.gov/) which follows a similar approach to Open Targets by integrating multiple sources of biomedical data, albeit concent”
Massive mining of publicly available RNA-seq data from human and mouse (2017, background): “Such data can complement tissue and cell line expression resources such as BioGPS 35 and GTEx 14 as well as resources that provide accumulated knowledge about genes and proteins such as GeneCards 36 , the Harmonizome 37 , and the NCBI gene data-base 38 .”
The Library of Integrated Network-based Cellular Signatures (LINCS) NIH Program: System-level Cataloging of Human Cells Response to Perturbations (2017, background): “…Enrichr (http://amp. pharm.mssm.edu/Enrichr), which is a search engine for gene sets (Chen et al., 2013; Kuleshov et al., 2016); and Harmonizome (http://amp.pharm.mssm.edu/Harmonizome/), a database that serves integrative omics data from 66 resources, including LINCS (Rouillard et al., 2016).”
In silico methods and tools for drug discovery (2021, background): “Several web servers such as Harmonizome [40] and the Open Targets Platform [41] provide lists of potential drug targets predicted using various databases.”
Deep Learning in Mining Biological Data (2020, background): “Harmonizome Collection of genes and proteins datasets [231]”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, positive): “It includesbiomedicalliterature,expressiondata,diseaseandphe-notype association data,bioactivitydata,DTI data and databases from Harmonizome [295].”
Gene Set Knowledge Discovery with Enrichr (2021, neutral): “This approach was demonstrated for several applications (Rouillard et al., 2016).”