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Pharos
Shining Light on the Druggable Genome
Dac Trung Nguyen, Timothy Sheils, Geetha Mandava,
Ajit Jadhav, Noel Southall,...
The interface to the KMC
Entity browsing (filterable & linked)Search (full text, auto-suggest)
Detailed view of entities B...
Target Audience
Biologists &
Clinical Researcher
• Characterize &
validate novel
targets
• Identify key small
molecules or...
Infrastructure
• Built using industry standard tools
• Open Source, straightforward to run locally
• Sources at https://sp...
What’s Included?
• Pharos presents data from a variety of
sources, integrated by U. New Mexico
• Primary focus is the prot...
The Data Sources
Antibodypedia.com, BioPlex, Druggable
Epigenome Domains, DrugCentral, Ensembl
Cross References, GO Consor...
Drug Target Ontology
TCRD
DISEASE
TIN-X
Interactions inside
& outside the IDG
Drug Target Ontology
• Employed as a navigation tool as well as a
filtering tool
• Currently DTO
terms are used as
labels
...
Target Ranking in PubMed
Novelty measures the scarcity of publications about a
target: How much was published about it, as...
C Bologa, D Cannon et al.
KNOWLEDGE
VALIDATION
TIN-X newdrugtargets.org
Harmonizome
Ma’ayan et al. Trends Pharmacol Sci. 2014 Sep;35(9):450-60.http://amp.pharm.mssm.edu/Harmonizome/
Harmonogram (Tclin, Kinase)
Harmonogram (Tdark, GPCR)
Compute target
similarity in
“data
availability
space”
Tdark targets
whose most
similar target is
not Tdark
Different Ways to Use Pharos
Random
Access
Direct
Access
Manual Interaction Programmatic Interaction
Search Entity Info
Pr...
Supporting Both Types of Users
• Efficient full text search, coupled to relevant auto-
suggestion
– Primary entry point wh...
Entity Dossier
• As you explore the knowledge base it’s useful
keep track of data
• Pharos implements a dossier function
–...
Entity Dossier
Visualizations
• Interactive dashboard
– Use visualizations as filters
• Inline visualizations for summary
– Radar charts,...
Visualization Dashboard
• Different facets visualized appropriately
• Directly filter results from visualization
Summary Visualizations
• Summarize text mined publications using
word clouds, but also provide access to list
Summary Visualizations
• Consensus gene expression across three
datasets (GTEx, HPA & HPM)
Original figure from Christian ...
Summary Visualizations
• Quickly scan targets that have similar types of
data associated with them
Summary Visualizations - Drilldown
Facet Visualization
Pharos Usage
Pharos Indexing
The Long Term Vision
• Provide access to all known
data about targets
– Multi-scale, multi-domain –
bioactivity to symptom...
Feedback
• Explore the UI, try it, break it, and let us know
what works and what doesn’t
• Are there data types and relati...
Acknowledgements
• Steve Mathias, Oleg Ursu, Jeremy Yang, Jayme
Holmes, Christian Bologa, Daniel Canon, Tudor
Oprea
• Step...
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Pharos Shining Light on the Druggable Genome

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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.

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Pharos Shining Light on the Druggable Genome

  1. 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. 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. 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. 4. Infrastructure • Built using industry standard tools • Open Source, straightforward to run locally • Sources at https://spotlite.nih.gov/ncats/pharos
  5. 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. 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. 7. Drug Target Ontology TCRD DISEASE TIN-X Interactions inside & outside the IDG
  8. 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. 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. 10. C Bologa, D Cannon et al. KNOWLEDGE VALIDATION TIN-X newdrugtargets.org
  11. 11. Harmonizome Ma’ayan et al. Trends Pharmacol Sci. 2014 Sep;35(9):450-60.http://amp.pharm.mssm.edu/Harmonizome/
  12. 12. Harmonogram (Tclin, Kinase)
  13. 13. Harmonogram (Tdark, GPCR)
  14. 14. Compute target similarity in “data availability space” Tdark targets whose most similar target is not Tdark
  15. 15. Different Ways to Use Pharos Random Access Direct Access Manual Interaction Programmatic Interaction Search Entity Info Precomputation converts analysis in to browsing
  16. 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. 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. 18. Entity Dossier
  19. 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. 20. Visualization Dashboard • Different facets visualized appropriately • Directly filter results from visualization
  21. 21. Summary Visualizations • Summarize text mined publications using word clouds, but also provide access to list
  22. 22. Summary Visualizations • Consensus gene expression across three datasets (GTEx, HPA & HPM) Original figure from Christian Stolte
  23. 23. Summary Visualizations • Quickly scan targets that have similar types of data associated with them
  24. 24. Summary Visualizations - Drilldown
  25. 25. Facet Visualization
  26. 26. Pharos Usage
  27. 27. Pharos Indexing
  28. 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. 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. 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

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