Pharos: Putting targets in context
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This document describes Pharos, a web application and database that provides integrated access to data on biological targets and their relationships to diseases, publications, drugs and other entities. It summarizes the current status and capabilities of Pharos, how it can be used by different audiences, and the long term vision to improve inference across data types and generate natural language summaries.
Pharos: Putting targets in context
- 1. Dac Trung Nguyen, Timothy Sheils, Geetha Mandava, Noel Southall, Rajarshi Guha NCATS, NIH Putting Targets in Context
- 2. IDG Knowledge Management Center
- 4. Current Status 191 facets 17.8 GB database 30 GB Lucene indexes 36K LoC (Java) 14K LoC (Scala) Image available Source code available 20,120 targets 15,094 diseases 2.3M publications 4,500 drugs Nguyen & Mathias et al, NAR, 2017 https://spotlite.nih.gov/ncats/pharos/issues https://hub.docker.com/r/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 • Wherever possible, targets are linked to other entities (which are also interlinked) Ø Small molecules, Diseases, Publications • Target related data include Ø Identifiers, ontology terms, sequence, expression data, publications (curated & text mined), phenotypes, PPI
- 6. Data Sources Full data source list at http://targetcentral.ws/Pharos Full data source list at http://targetcentral.ws/Pharos
- 7. 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 Target Audience
- 8. Do You Know What You Want? • Efficient full text search • Primary entry point when exploring and for hypothesis generation • Fast autosuggestion facility Ø Suggestions grouped by type (disease, ligand, …) • Searches run across all entity types Ø But can be restricted to specific ones
- 9. Multiple Search Options Batch search Sequence search Structure searchText search
- 10. (Possibly) Lots of Results
- 11. Filters
- 12. Visualization • Key requirement for efficient exploration, summary • Increase information density in limited screen real estate, take context into account • Interactivity is desirable, high quality for easy inclusion in documents • Simple is better than fancy but pretty pictures have value, make for a better experience • Integrate and link to external visualization
- 15. Documentation
- 18. Use Case – Targets for Obesity 20K targets 3K targets 616 targets 4 targets ALPK1 7 targets listed in 5R01NS044385-14 KIF7 Disease Obesity GWAS Trait Obesity IDG Family GPCR/IC/ Kinase Grants 5R01NS...
- 19. Use Case - Target Similarity • Find understudied targets that have similar data profiles to well studied targets • Supports recommendations, prioritization Tdark targets whose most similar target is not Tdark
- 20. Outreach & Dissemination Activities User Feedback Deployment Webinars Documentation NER API for targets & diseases @idg_pharos Recent papers to Pharos links via Tweets
- 21. Pharos Usage • Usage statistics over the last one year are generally increasing • 89K pageviews • 14K sessions • 7.5K users
- 22. The Long Term Vision • Incorporate dependencies between data types to support inference and sophisticated filters • From presentation to summarization Ø Use explicit links & computational inference to generate (semi-) natural language summary using all known data Ø 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.
- 23. 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? https://pharos.nih.gov pharos@nih.gov @idg_pharos
- 24. Acknowledgements Dac-Trung Nguyen, Kyle Brinacombe, Timothy Sheils, Geetha Mandava, Noel Southall, Ajit Jadhav Steve Mathias, Oleg Ursu, Jeremy Yang, Christian Bologa, Daniel Canon, Tudor Oprea Nicholas Fernandez, Andrew Rouillard, Avi Mayan Ajay Pillai, Aaron Pawlyk, Christine Colvis Tomita Lab / Finkbeiner lab