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making connections between genetics and disease
MongoDB and the Connectivity Map
.
.
.
.
.
Corey Rajiv
a common language
Gene Expression
.
.
.
.
.
.13
~7,000 experiments
Over 19,000 registered users
Cited by over 1,200 scientific reports
.
2006
.
2014
.16
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
• Diverse use-cases
• Users with varying technical expertise
• Annotations are complex and
incomplete
• Frequent updates
CMap Data!
Easy to describe, tough to Model
Store just what’s needed
Refactor frequently
Test and use daily
Data Model!
An agile philosophy keeps the model tractable
Data Model!
An inventory of signatures
siginfo
Data Model!
Shared fields as separate collections
siginfo
cellinfo
pertinfo
Data Model!
Add computed fields and external meta-data
siginfo cellinfo
Data Model!
Duplicate data to optimize lookups
siginfo pertinfo
APIs!
Are awesome, we need more of them
Picked functionality over convention!
/siginfo?q={“cell”:”A”}	
  vs	
  /siginfo/cell/A
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
API!
Node and Mongoose enable easy API creation
Language Bindings!
JSON as a universal format
Javascript
Python
R
Analytic Tools!
A compute API liberates command line scripts
Compute API!
Messaging handled via a capped collection
Input Validation!
JSON Schema simplifies validation
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
Sign up at lincscloud.org
Lincscloud!
A platform for easy access to perturbational data
Free for academic use
Predicting Drug Function!
Diverse structures, common activities
Predicting Drug Function!
Diverse structures, common activities
VEGFR inhibitor
PPARG agonist
PI3K/MTOR inhibitor
ROCK inhibitor
Estrogen agonist
Finding Novel Drug Targets!
Repurposing failed drugs
Original target
Finding Novel Drug Targets!
Repurposing failed drugs
Original target
Failed in Phase 2 clinical trial due to lack of efficacy
Finding Novel Drug Targets!
Repurposing failed drugs
Original target
Novel Target A
Novel Target B
Novel Target C
Novel Target D
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

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Connecting genetics and disease with gene expression data and MongoDB

  • 1. making connections between genetics and disease MongoDB and the Connectivity Map
  • 2. .
  • 3. .
  • 4. .
  • 5. .
  • 8. .
  • 9. .
  • 10. .
  • 11. .
  • 12. .
  • 13. .13 ~7,000 experiments Over 19,000 registered users Cited by over 1,200 scientific reports
  • 16. .16
  • 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
  • 28.
  • 29.
  • 30.
  • 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
  • 41.
  • 42.
  • 43.
  • 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