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

The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.

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
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkac1033

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 51 references

Research works printed but not linked: 1

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

Cited by

Sorted by citations

See all 138

Related

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review