1. The Life Cycle of Structural Biology Data: A discipline with a culture of sharing
Chris Morris, STFC
DI4R
Sept 2016

2. West-Life: Life Sciences in the Cloud
Background

3. Structural Biologists are mature computer users
Protein Data Bank
Log new entries by year
PDB founded 1971 [Protein Data Bank Nature New Biology 233:223]
First submissions by mailing punch cards
First use of digital computers in 1940s
FAIR principles come naturally to structural biologists

4. New scientific goals Larger macromolecular machines
Membrane association
4D (structure + dynamics)
Transient interactions
> 25,000 experimental sessions in 2015

5. New experimental methods
Combined techniques
Users are not always experts
Small samples
Data noisy and incomplete
Deliver results to other life scientists
 Calls for integrative, user-friendly solutions

6. INSTRUCT user survey
73% working on eukaryotic rather than prokaryotic systems
84% working on complexes rather than single gene products
Each research team routinely uses three-four different techniques
83% would use combined SB techniques more often if it was easier to get access to experimental facilities
73% of the cases found it hard to combine software tools for different techniques in integrated workflows

7. New data challenges Data volume:
Combined output of European SB facilities > LHC
XFEL will double it
DLS archived >1PB in 2015
Improve archiving of data and metadata
Support for data moving / replication
Improve automated pipelines for MX
… create pipelines for other techniques

8. Crowdsourcing from the middle tier
Community includes:
Life scientists who use computers
End user programmers
Algorithm developers
We aim at easing the process of creating web- based services

9. West-Life H2020 Project 10 Partners: Budget: €4 000 0000
STFC (UK) (lead partner, Martyn Winn Coordinator)
Dutch Cancer Institute (NKI) (NL)
EMBL (DE)
Masaryk University (MU) (CZ)
Consejo Superior De Investigaciones Cientificas (CSIC) (ES)
Consorzio Interuniversitario Risonanze Magnetiche Di Metallo Proteine (CIRMPP) (IT)
INSTRUCT (UK)
Utrecht University (NL)
Luna (FR) – (SME)
INFN (IT)
Budget: €
Duration: 36 months
Started: 1 Nov 2015
Proposal ID

10. West-Life: Life Sciences in the Cloud
Data Life Cycle

11. SB Data Life Cycle
“four stages ...: gathering of data; choosing the representation and encoding of all data within a numerical scoring function consisting of spatial restraints; configurational sampling to identify structural models with good scores; and analyzing the models, ... ” (Sali et al).

12. General Data Life Cycle
Life cycle defined by UK Data Archive
Six stages:
Creating
Processing
analysing
preserving + giving access
reusing
Also
Retiring data

13. Creating data Data acquired in experiment MX: gigabytes
NMR: megabytes to gigabytes
EM: terabytes
Metadata
About experiment e.g. wavelength
About sample e.g. construct
In reality, usually lacking
Facilities:
47 synchrotrons
200 NMR groups in Europe
Growing numbers of electron microscopes

14. Creating data
The wwPDB Hybrid/Integrative Methods Task Force “all relevant experimental data and metadata … should be archived”
MyTardis, SBGrid, EMPIAR, BMRB
Instruct: “storage of data is the responsibility of the User to whom it belongs … Instruct Centres aspire to offer an archive to store data, especially in cases where the data volume makes this more practical that transferring the data ...”
DLS processing pipeline and archive
Want “research project” view, not facility view
Orthogonality:
Data size
Data lifetime
Authentication method

15. Processing data: data reduction
“ choosing the representation and encoding of all data within a numerical scoring function consisting of spatial restraints ” [Sali et al]:
MX: integration and merging, megabytes
EM: class assigment, megabytes
NMR: Fourier transform, gigabytes, then peaking picking
Instruct: “supporting data must be deposited in a public database or …. made otherwise available within one year after publication of the results”
Licenses:
Free for academic use
Not always open source
Automated provenance
PROV-O please, don’t re-invent!

16. Analysing data: structure determination and interpretation
“all structures are in fact integrative models that have been derived both from experimental measurements involving a physical sample of a biological macromolecule and prior knowledge of the underlying stereochemistry. ” [Sali et al]
Initial structure
Direct methods e.g. experimental phasing
Molecular replacement uses existing model
The refinement
Uses experimental data and prior knowledge
Processing on:
MX: laptops
NMR: cloud
EM: HPC with Infiniband

17. Preserving data Giving access to data
“For all structural studies of macromolecules, coordinates and the related experimental data … must be deposited at a member site of the Worldwide Protein Data Bank” [IUCR author guidelines]

18. Reusing data
2012 to 2014 there were 5913 papers citing one or more PDB entries [Bousfield]
Synoptic studies
“ the totality of the data in the PDB provides a rich source of more generalized knowledge about proteins, their molecular biology, and evolution” [Furman et al, 2013]
Molecular replacement
2015: 526,126,409 downloads from PDB
Automatic search by MrBUMP, BALBES, etc
PDB-REDO recalculates structures with new software
FAIR principles are baked into SB workflow

19. Discarding data 3,404 PDB entries are marked as obsolete
Better samples
Better analysis
Rare cases of fabricated data
Self-policed community
Charter of PDB permits obsolete mark only if author or institution retracts
E-Infrastructure facilitates research
Should not facilitate research misconduct

20. Conclusion Common data infrastructure
giving a simple user interface and simple programmatic access to scattered data.
workflows to use of datasets from different facilities and techniques.
automatic acquisition of metadata
Data management integrated with data processing
Standard package for small experimental centres
Support for crowdsourcing

21. Cryo-EM workshop @ ISGC 2016 in Taipei?

22. Supplementary material

23. Reinvent nothing Existing best practise includes:
weNMR
PaNData
Diamond: pipelines and archives
Scipion
Data Life Cycle Lab
Integration, not competition

24. AAI requirements Saved sessions, data access Collaborations
“I am the person you gave these credentials to…”
Collaborations
“I am the person you think I am”
Remote experiments
“I am definitely the person you think I am”
Personal certificates
Implausible that our community would use them at broad (but examples within WeNMR)

25. Current AAI status WeNMR uses SSO
Accepts eduGAIN and social media id
Experimental facilities issue userids
Moving to Moonshot / Umbrella integration with eduGAIN
Check passports at gate
… but moving to remote access
Instruct issues userids
Verifies identity by phone call to PI (small community)
West-Life not started yet

26. AAI solutions Solution for homeless users
create a local id without associating it with a homeid
We have colleagues not in eduGAIN
Solutions to handle user attributes
Stored locally
updates are checked by administrator
Preferred technology
Shibboleth has become standard
SAML probably sufficient for authorization
Web access, with delegation

27. References Biasini et al. (2013). Acta Cryst. D69, 701-709.
Gutmanas et al. (2013). Acta Cryst. D69,   
Karaca, E. & Bonvin, A. M. J. J. (2013). Acta Cryst. D69,   
Marabini, et al. (2013). Acta Cryst. D69,   
Morris, C. & Segal, J. (2012). IEEE Software, 29, 9-12.  
Perrakis et al.  J. Struct. Biol. 175,
DiMaio et al., Nature Methods, Improved protein crystal structures at low resolution by integrated refinement with Phenix and Rosetta, in press
   

28. Main Concepts