The Broad Institute has developed a novel high-throughput gene-expression profiling technology and has used it to build an open-source catalog of over a million profiles that captures the functional states of cells when treated with drugs and other types of perturbations. Referred to as the Connectivity Map (or CMap), these data when paired with pattern matching algorithms, facilitate the discovery of connections between drugs, genes and diseases. We wished to expose this resource to scientists around the world via an API that is easily accessible to programmers and biologists alike. We required a database solution that could handle a variety of data types and handle frequent changes to the schema. We realized that a relational database did not fit our needs, and gravitated towards MongoDB for its ease of use, support for dynamic schema, complex data structures and expressive query syntax. In this talk, we’ll walk through how we built the CMap library. We’ll discuss why we chose MongoDB, the various schema design iterations and tradeoffs we’ve made, how people are using the API, and what we’re planning for the next generation of biomedical data.

1. MongoDB and the Connectivity Map
making connections between genetics and disease

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Corey
cflynn@broadinstitute.org
@CoreyJFlynn

7. Gene Expression
a common language for biology

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2006
~7,000 experiments
Over 19,000 registered users
Cited by over 1,200 scientific reports

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2006

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2014

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17. Connectivity Map Dataset
1.4 million gene expression profiles
12,488 Compounds
• FDA approved drugs
• Bioactive tool compounds
• Screening hits
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

18. Connectivity Map Data
Easy to describe, tough to Model
• Diverse users and use-cases
• Annotations are complex and often
incomplete
• Frequent updates

19. Data Model
An agile philosophy keeps the model tractable
Store just what’s needed
Test and use daily
Refactor frequently

20. Data Model
An agile philosophy keeps the model tractable
Store just what’s needed
Test and use daily
Refactor frequently

21. Data Model
An inventory of signatures
signature_info

22. Data Model
Shared fields as separate collections
signature_info
cell_info

23. Data Model
Shared fields as separate collections
signature_info treatment_info

24. Data Model
Add computed fields and external meta-data
signature_info cell_info

25. Data Model
Denormalize to optimize lookups
signature_info treatment_info

26. APIs
Are awesome, life science needs more of them
/siginfo/cell/A

27. APIs
Are awesome, life science needs more of them
/siginfo?q={“cell”:“A”}

28. 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

29. API
Node and Mongoose enable easy API creation

30. Language Bindings
JSON as a universal format
Javascript
Python
R

34. Analytic Tools
A compute API liberates command line scripts

35. Compute API
Message queuing via a capped collection

36. A research platform for functional
genomics

37. Predicting Drug Function
Diverse structures, common activities

38. Predicting Drug Function
Diverse structures, common activities
VEGFR inhibitor
PPARG agonist
PI3K/MTOR inhibitor
ROCK inhibitor
Estrogen agonist

39. Finding Novel Drug Targets
Repurposing failed drugs
Original target

40. Finding Novel Drug Targets
Repurposing failed drugs
Original target
Failed in Phase 2 clinical trial due to lack of efficacy

41. Finding Novel Drug Targets
Repurposing failed drugs
Original target
Novel Target A
Novel Target B
Novel Target C
Novel Target D