The druggable genome corresponds to the set of protein targets that are amenable to small molecule perturbation. While this set of targets has enormous potential in terms of understanding and treating many disease conditions, the bulk of them are understudied or not studied at all. To address this the NIH initiated the "Illuminating the Druggable Genome" program to characterize the dark regions of the druggable genome. As part of this program, a Knowledge Management Center (KMC) was created to aggregate and integrate heterogeneous data sources and data types creating a centralized location for information about all protein targets indentified as part of the druggable genome. In this presentation we describe the design and deployment of Pharos, the user interface for the KMC. Based on modern web design principles the interface provides facile access to all data types collected by the KMC. We provide an overview of the data sources and types made available via Pharos and then describe the architecture of the system and its integration with KMC & external resources. Given the complexity of the data surrounding any target, efficient and intuitive visualization has been a high priority, to enable users to quickly navigate and summarize search results and rapidly identify patterns. We highlight the approaches we have taken to address this requirement. A critical feature of the interface is the ability to perform flexible search and subsequent drill down of search results. We describe the design of a faceted search interface coupled to the Drug-Target Ontology (DTO) that supports these activites. Underlying the interface is a RESTful API that provides programmatic access to all KMC data, allowing for easy consumption in user applications. We conclude by highlighting some workflows on targets of interest to the IDG program.

1. Pharos
Shining Light on the Druggable Genome
Dac Trung Nguyen, Timothy Sheils, Geetha Mandava,
Ajit Jadhav, Noel Southall, Rajarshi Guha
NCATS, NIH
2016 ACS Fall Meeting, Philadelphia

2. The interface to the KMC
Entity browsing (filterable & linked)Search (full text, auto-suggest)
Detailed view of entities Built on top of a robust REST API

3. Target Audience
Biologists &
Clinical Researcher
• Characterize &
validate novel
targets
• Identify key small
molecules or
biologics
Informatics
Scientists
• Data mining
• Support target
validation
projects
Program Staff
• Explore the
research
landscape
• New directions
for research &
funding

4. Infrastructure
• Built using industry standard tools
• Open Source, straightforward to run locally
• Sources at https://spotlite.nih.gov/ncats/pharos

5. What’s Included?
• Pharos presents data from a variety of
sources, integrated by U. New Mexico
• Primary focus is the protein target
• Target related data include
– Identifiers, ontology terms, sequence, expression
data, publications (curated & text mined)
• Wherever possible, targets are linked to other
entities
– Small molecules, Diseases, Publications

6. The Data Sources
Antibodypedia.com, BioPlex, Druggable
Epigenome Domains, DrugCentral, Ensembl
Cross References, GO Consortium, GTEx,
GWAS Catalog, HGNC, HPA, HPM, IMPC,
AnimalTFDB, JAX/MGI, Panther, PubChem,
PubMed, NCBI Gene, NIH RePORTER, OMIM,
TIN-X, UniProt, Harmonizome, DISEASES,
TISSUES, DTO, CHEMBL

7. Drug Target Ontology
TCRD
DISEASE
TIN-X
Interactions inside
& outside the IDG

8. Drug Target Ontology
• Employed as a navigation tool as well as a
filtering tool
• Currently DTO
terms are used as
labels
• Exploring novel
uses of the
hierarchy

9. Target Ranking in PubMed
Novelty measures the scarcity of publications about a
target: How much was published about it, as the inverse
of the sum of FRACTIONS of papers/patents
– E.g.: Target A is mentioned in 2 papers, first with other
4 targets, second with other 9 targets
Novelty = 1/(1/5 + 1/10) = 3.33
Importance measures the strength of the associations
betwee a target and a disease: Fractional disease-target
score
– FDT = 1/ (nr targets + nr diseases) for each paper
– Bayesian smoothing is used to compare general terms
(cancer) with specific ones (ovarian carcinosarcoma)
C Bologa, D. Cannon et al. 5/14/15 revision

10. C Bologa, D Cannon et al.
KNOWLEDGE
VALIDATION
TIN-X newdrugtargets.org

11. Harmonizome
Ma’ayan et al. Trends Pharmacol Sci. 2014 Sep;35(9):450-60.http://amp.pharm.mssm.edu/Harmonizome/

12. Harmonogram (Tclin, Kinase)

13. Harmonogram (Tdark, GPCR)

14. Compute target
similarity in
“data
availability
space”
Tdark targets
whose most
similar target is
not Tdark

15. Different Ways to Use Pharos
Random
Access
Direct
Access
Manual Interaction Programmatic Interaction
Search Entity Info
Precomputation converts analysis in to browsing

16. Supporting Both Types of Users
• Efficient full text search, coupled to relevant auto-
suggestion
– Primary entry point when exploring
and for hypothesis generation
• Extensive list of facets
– Supports easy construction of
complex filtering rules
• Extensive details for each
target
– Linked to external and internal
resources

17. Entity Dossier
• As you explore the knowledge base it’s useful
keep track of data
• Pharos implements a dossier function
– Analogous to e-commerce shopping carts
• Support for task-specific dossiers
• Download a dossier as a ZIP file

18. Entity Dossier

19. Visualizations
• Interactive dashboard
– Use visualizations as filters
• Inline visualizations for summary
– Radar charts, word clouds, heatmaps, …
– Context dependent drill down
• Links to external visualization resources
– MSSM harmonogram
– TINX (linkout & reduced version incorporated
locally)

20. Visualization Dashboard
• Different facets visualized appropriately
• Directly filter results from visualization

21. Summary Visualizations
• Summarize text mined publications using
word clouds, but also provide access to list

22. Summary Visualizations
• Consensus gene expression across three
datasets (GTEx, HPA & HPM)
Original figure from Christian Stolte

23. Summary Visualizations
• Quickly scan targets that have similar types of
data associated with them

24. Summary Visualizations - Drilldown

25. Facet Visualization

26. Pharos Usage

27. Pharos Indexing

28. The Long Term Vision
• Provide access to all known
data about targets
– Multi-scale, multi-domain –
bioactivity to symptoms
• Intelligent summarization
– Use explicit links & computational
inference to generate natural language
summary using all known data
– Influenced by the query
• The result is a biological dashboard,
customized for the user and the query
Target X has been implicated in 3
diseases related to skeletal, urological
and nervous systems. It has been
investigated in 5 in vitro assay, 2 in
vivo assays. There are 4 compounds
active against this target, 3 of which
are in clinical trials.

29. Feedback
• Explore the UI, try it, break it, and let us know
what works and what doesn’t
• Are there data types and relations that would
help you but are not available?
http://pharos.nih.gov
pharos@nih.gov

30. Acknowledgements
• Steve Mathias, Oleg Ursu, Jeremy Yang, Jayme
Holmes, Christian Bologa, Daniel Canon, Tudor
Oprea
• Stephan Schurer, Lars Juhl Jensen
• Nicholas Fernandez, Andrew Rouillard, Avi
Mayan
• Tomita Lab, Mike McManus, Gaia Skibinski
• Ajay Pillai, Aaron Pawlyk, Christine Colvis