This document describes Synapse, a tool that enables transparent and reproducible research. Synapse allows researchers to record analyses in real-time and share living research projects. This facilitates large-scale collaborative science. Examples mentioned include the TCGA Pan-Cancer Consortium project involving multiple studies of cancer data and the CRC Subtyping Consortium involving collaboration around classifying colorectal cancer subtypes from shared data.

1. | brian m. bot| senior scientist | community manager |
Synapse
enabling transparent, reproducible research
| michael kellen | director, technology platform and services |

2. a tool to improve transparency and
reproducibility of data intensive
science by recording analyses in real-time
Synapse

3. a collection of living research projects
enabling researchers to contribute to large-scale
collaborative science pre- and post-publication
Synapse

4. Attractor Metagenes
Columbia Professor Dimitris Anastassiou
MPEG-2 compression of digital audio and video signals
Modules of co-expressed genes shared across cancers
Belief that these ‘attractors’ represent underlying biological
mechanisms (bioinformatic ‘hallmarks of cancer’1)
1D. Hanahan, R. A. Weinberg. Hallmarks of cancer: The next generation. Cell 144, 646–674 (2011)

5. 21 february 2013
17 april 2013

7. 21 february 2013
17 april 2013
???

10. ...

11. ...

12. TCGA Pan-Cancer Consortium
Attractor Metagenes
openly evolving research projects
collaboration around common data

13. Omberg,
et
al.
Nature
Gene*cs
•Analysis of: 12 Tumor types, 6 molecular
profiling platforms
•Focus series of: 4 papers in Nature Genetics,
with 14 more to follow in other NPG journals
TCGA Pan-Cancer Consortium

14. 18papers in press

15. 68core projects

16. 248researchers

17. 28institutions

18. 1070datasets

19. 1723results

20. versioned data, analysis freezes

21. data versioning versus data provenance

22. TCGA Pan-Cancer Consortium
collaboration around common data
CRC Subtyping Consortium
collaboration around common question

23. CRC Subtyping Consortium

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datasets subtypes
analysis
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subtypes

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datasets
analysis
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27. analysis
groups
G

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datasets
analysis
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subtypes

29. CRC Subtyping Consortium
Phase I: per-group subtyping
‣ subtyping calls on common data
‣ assess agreement between methods
‣ assess associations with phenotypic traits
Phase II: meta-analysis and de novo subtyping
‣ consensus subytping
‣ assess associations with clinical outcomes
‣ strategy for adoption from clinicians

30. enables transparency and reproducibility
facilitates large scale collaboration
encourages communication pre- and post-publication
summary

31. commenting / peer review mechanisms
recognition metrics for individuals and teams
deeper integration with cloud compute services
project snapshots linked to publications
future directions

32. Acknowledgements
Sage Bionetworks
Synapse Development Team
Alfred P. Sloan Foundation
Nature Publishing Group
AAAS-Science
PLoS