MongoDB and the Connectivity Map: Making Connections Between Genetics and Disease
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This document discusses the Connectivity Map (CMap), a resource for exploring connections between genetics and disease. CMap contains over 1.4 million gene expression profiles from experiments with over 15 cell types and 12,488 compounds. It has been cited in over 1,200 scientific reports and has over 19,000 registered users. The document outlines CMap's data model for storing experimental signatures and metadata in a way that is flexible, scalable, and optimized for querying. It also discusses APIs for accessing and analyzing CMap data, as well as tools for tasks like predicting drug function and finding novel drug targets.
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MongoDB and the Connectivity Map: Making Connections Between Genetics and Disease
- 1. making connections between genetics and disease MongoDB and the Connectivity Map
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- 7. a common language Gene Expression
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- 13. .13 ~7,000 experiments Over 19,000 registered users Cited by over 1,200 scientific reports
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- 17. CMap-LINCS dataset 1.4 million gene expression profiles 3,800 Genes (shRNA & cDNA) • Targets/pathways of approved drugs • Candidate disease genes • Community nominations 15 Cell types • Banked primary cell types • Cancer cell lines • Primary hTERT-immortalized • Patient-derived iPS cells • Community nominated 12,488 Compounds • FDA approved drugs • Bioactive tool compounds • Screening hits
- 18. • Diverse use-cases • Users with varying technical expertise • Annotations are complex and incomplete • Frequent updates CMap Data! Easy to describe, tough to Model
- 19. Store just what’s needed Refactor frequently Test and use daily Data Model! An agile philosophy keeps the model tractable
- 20. Data Model! An inventory of signatures siginfo
- 21. Data Model! Shared fields as separate collections siginfo cellinfo pertinfo
- 22. Data Model! Add computed fields and external meta-data siginfo cellinfo
- 23. Data Model! Duplicate data to optimize lookups siginfo pertinfo
- 24. APIs! Are awesome, we need more of them Picked functionality over convention! /siginfo?q={“cell”:”A”} vs /siginfo/cell/A
- 25. API! MongoDB inspired a rich query syntax Function Example Query /siginfo?q={“cell:”A”,”name”:”B”} Field selection /siginfo?q={}&f={“name”:1} Document count /siginfo?q={}&c=true Document limit /siginfo?q={}&l=10 Skip documents /siginfo?q={}&l=10&sk=10 Sort order /siginfo?q={}&s={“name”:-‐1,”cell”:1} Distinct values /siginfo?q={}&d=name Aggregation /siginfo?q={}&g=name
- 26. API! Node and Mongoose enable easy API creation
- 27. Language Bindings! JSON as a universal format Javascript Python R
- 31. Analytic Tools! A compute API liberates command line scripts
- 32. Compute API! Messaging handled via a capped collection
- 33. Input Validation! JSON Schema simplifies validation
- 34. GCTX : A binary format based on HDF5 Cross platform Multi-language support Efficient I/O Storage size for 30 billion data points is 110 Gb Numeric Matrix Data! HDF5 offers efficient storage for large matrices
- 35. Sign up at lincscloud.org Lincscloud! A platform for easy access to perturbational data Free for academic use
- 36. Predicting Drug Function! Diverse structures, common activities
- 37. Predicting Drug Function! Diverse structures, common activities VEGFR inhibitor PPARG agonist PI3K/MTOR inhibitor ROCK inhibitor Estrogen agonist
- 38. Finding Novel Drug Targets! Repurposing failed drugs Original target
- 39. Finding Novel Drug Targets! Repurposing failed drugs Original target Failed in Phase 2 clinical trial due to lack of efficacy
- 40. Finding Novel Drug Targets! Repurposing failed drugs Original target Novel Target A Novel Target B Novel Target C Novel Target D
- 44. Acknowledgements Todd Golub Core Team: Analysis & Software Arvind Subramanian Jacob Asiedu Larson Hogstrom Ian Smith David Lahr Aravind Subramanian Josh Gould Ted Natoli David Wadden ! Core Team: Lab John Davis David Peck Xiaodong Lu Melanie Donahue Daniel Lam Jackie Rosains (Project Manager) Collaborators Bang Wong Steven Corsello (Golub lab) Jake Jaffe (Proteomics) David Takeda (Hahn lab) Pablo Tamayo ! Chemistry & Therapeutics Lucienne Ronco Josh Bittker Arthur Liberzon Mathias Wawer Paul Clemons ! Genetic Perturbation Platform John Doench Federica Piccioni David Root