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Cited by 138Computational Drug Discovery MethodsBioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

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Pharos 2023: an integrated resource for the understudied human proteome

Open access
Kelleher, Keith J, Sheils, Timothy K, Mathias, Stephen L, Yang, Jeremy J, Metzger, Vincent T, Siramshetty, Vishal B, Nguyen, Dac-Trung, Jensen, Lars Juhl, Vidović, Dušica, Schürer, Stephan C, Holmes, Jayme, Sharma, Karlie R, Pillai, Ajay, Bologa, Cristian G, Edwards, Jeremy S, Mathé, Ewy A, Oprea, Tudor I·2023·Nucleic Acids Research
DOI 10.1093/nar/gkac1033PMC PMC9825581
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Abstract

The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for users to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.

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Crossref · 10.1093/nar/gkac1033

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Figures

Figure 1. TDL changes over time • Chart of the TDL changes between an early version (TCRD v3.0), a version at the time of the last update publication (TCRD v6.7), and the current version (TCRD 6.13). The decrease of Tdark and subsequent increase of other development levels shows an overall increase in target illumination.
Figure 1. TDL changes over time • Chart of the TDL changes between an early version (TCRD v3.0), a version at the time of the last update publication (TCRD v6.7), and the current version (TCRD 6.13). The decrease of Tdark and subsequent increase of other development levels shows an overall increase in target illumination.CC BY-NC · PubMed Central
Figure 2 Target details updates. (A) Expression data from five sources is color coded according to expression type (red: aggregate score, blue: RNA expression, yellow: protein expression) and displayed as a heat map on the left panel. Cells in the heat map display details about the expression data for each tissue when clicked. The right panel contains tabs to show either a circular treemap, where tissues are grouped according to the UBERON hierarchy, and an anatomogram, where tissues on a human form are shaded according to their expression level. The circular treemap is interactive and can be used to filter the heat map. (B) Text-mined target-disease associations from TIN-X are shown in an interactive scatter plot of importance vs novelty adjacent to a circular treemap. The circular treemap groups the associations based on the hierarchy defined by the Mondo Disease Ontology. Selecting a circle in the right panel highlights corresponding points on the scatter plot for those diseases. This dynamic visualization helps users find classes of diseases which tend to be both high in the importance and novelty metrics. (C) GWAS traits associated with a single target, scored and ranked according to the TIGA data processing pipeline. More reliable associations tend to have a higher Mean Rank Score and a higher Beta Count.
Figure 2 Target details updates. (A) Expression data from five sources is color coded according to expression type (red: aggregate score, blue: RNA expression, yellow: protein expression) and displayed as a heat map on the left panel. Cells in the heat map display details about the expression data for each tissue when clicked. The right panel contains tabs to show either a circular treemap, where tissues are grouped according to the UBERON hierarchy, and an anatomogram, where tissues on a human form are shaded according to their expression level. The circular treemap is interactive and can be used to filter the heat map. (B) Text-mined target-disease associations from TIN-X are shown in an interactive scatter plot of importance vs novelty adjacent to a circular treemap. The circular treemap groups the associations based on the hierarchy defined by the Mondo Disease Ontology. Selecting a circle in the right panel highlights corresponding points on the scatter plot for those diseases. This dynamic visualization helps users find classes of diseases which tend to be both high in the importance and novelty metrics. (C) GWAS traits associated with a single target, scored and ranked according to the TIGA data processing pipeline. More reliable associations tend to have a higher Mean Rank Score and a higher Beta Count.CC BY-NC · PubMed Central
Figure 3. Structure search. The component contains a Marvin JS widget showing an editable query structure. Structures can also be edited via the Query SMILES input or resolved using an external tool (not pictured). This serves as a starting point for performing a similarity, or substructure, search of compounds in TCRD, or a search for predicted targets, using the QSAR models in NCATS Predictor.
Figure 3. Structure search. The component contains a Marvin JS widget showing an editable query structure. Structures can also be edited via the Query SMILES input or resolved using an external tool (not pictured). This serves as a starting point for performing a similarity, or substructure, search of compounds in TCRD, or a search for predicted targets, using the QSAR models in NCATS Predictor.CC BY-NC · PubMed Central
Figure 4. Enrichment analysis. (A) Left shows the number of targets associated with different diseases in a full target list. Right shows the number of targets associated with different diseases in a target list consisting of targets with a documented protein-protein interaction with DRD2. Note how there is a lot of overlap between the entries in this list, when the list is sorted by the naive counts. (B) Enrichment score results after performing Fisher's Exact Test on the filtered list. Note how the top entries in the list comprises a different set of diseases, including a lot of neurological disorders, and substance dependence disorders, diseases which are more commonly known to be related to DRD2.
Figure 4. Enrichment analysis. (A) Left shows the number of targets associated with different diseases in a full target list. Right shows the number of targets associated with different diseases in a target list consisting of targets with a documented protein-protein interaction with DRD2. Note how there is a lot of overlap between the entries in this list, when the list is sorted by the naive counts. (B) Enrichment score results after performing Fisher's Exact Test on the filtered list. Note how the top entries in the list comprises a different set of diseases, including a lot of neurological disorders, and substance dependence disorders, diseases which are more commonly known to be related to DRD2.CC BY-NC · PubMed Central
Figure 5. UpSet chart examples. (A) For a list of ligands, this plot shows different combinations of targets against which each ligand has been shown to be active. (B) For a target list, this plot shows how many targets in the list were on each combination of NIH lists. Gold highlighting shows filters that are currently applied to the list, so this list would consist of 12 targets that were on the IDG list from 2017 to 2020, and removed in 2022. (C) In this case, the corresponding target list would show 385 targets that have data from ProKinO, but not from Dark Kinase Knowledgebase. (D) In this case, the corresponding target list would show 21 targets annotated by PANTHER to be a RNA binding protein, but not a DNA binding protein.
Figure 5. UpSet chart examples. (A) For a list of ligands, this plot shows different combinations of targets against which each ligand has been shown to be active. (B) For a target list, this plot shows how many targets in the list were on each combination of NIH lists. Gold highlighting shows filters that are currently applied to the list, so this list would consist of 12 targets that were on the IDG list from 2017 to 2020, and removed in 2022. (C) In this case, the corresponding target list would show 385 targets that have data from ProKinO, but not from Dark Kinase Knowledgebase. (D) In this case, the corresponding target list would show 21 targets annotated by PANTHER to be a RNA binding protein, but not a DNA binding protein.CC BY-NC · PubMed Central

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  1. Fisher, 1934, Statistical Methods for Research Workers … Fifth Edition - Revised and Enlarged Edinburgh

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Metadata

Authors
Kelleher, K. J., Sheils, T. K., Mathias, S. L., Yang, J. J., Metzger, V. T., Siramshetty, V. B., Nguyen, D.-T., Jensen, L. J., Vidović, D., Schürer, S. C., Holmes, J., Sharma, K. R., Pillai, A., Bologa, C. G., Edwards, J. S., Mathé, E. A., & Oprea, T. I.
Affiliations
  • National Center for Advancing Translational Sciences (US)
  • University of New Mexico (US)
  • National Center for Advancing Translational Science , 9800 Medical Center Drive , Rockville , MD  20850, USA
  • University of Copenhagen (DK)
  • University of Miami (US)
Corresponding author
  • Edwards, Jeremy S
  • Mathé, Ewy A
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2022-11-29
Volume
51
Issue
D1
Pages
D1405-D1416
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkac1033
PMC
PMC9825581
OpenAlex
W4310240482
Cited by
137
References
51
Citations by year
2026: 39 · 2025: 42 · 2024: 46 · 2023: 10
FWCI
13.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
Funders
  • National Institutes of Health (U24 CA224370, U24 TR002278)
  • Novo Nordisk Foundation (NNF14CC0001)
  • Intramural Research Program, NCATS
  • Intramural/Extramural research program of the NCATS, NIH: Knowledge Management Center for Illuminating the Druggable Genome (ZIA TR000057-08)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
Keywords
Pharo, human proteome, disease biology, drug discovery, small molecule perturbations
MeSH terms
Biological Products, Humans, Databases, Factual, Internet, Proteome, Drug Discovery, Molecular Targeted Therapy

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