Ewan Birney's slides from Biocuration 2013

1. Curation
Ewan Birney (tweetable)

2. Who am I?
• Associate Director at
European Bioinformatics
Institute (EBI)
• Involved in genomics since I
was 19 (> 20 years!)
• Trained as a biochemist –
most people think I am CS
EBI is in Hinxton, South
• Analysed – sometimes lead
Cambridgeshire
–
human/mouse/rat/platypus
EBI is part of EMBL, ~like
etc genomes, ENCODE,
CERN for molecular biology
Others.

3. Molecular Biology
• The study of how life works – at a molecular level
• Key molecules:
• DNA – Information store (Disk)
• RNA – Key information transformer, also does stuff (RAM)
• Proteins – The business end of life (Chip, robotic arms)
• Metabolites – Fuel and signalling molecules (electricity)
• Theories of how these interact – no theories of to predict what
they are
• Instead we determine attributes of molecules and store them in
globally accessible, open, databases

4. Theory  Observation
Can accurately predict from models
Must directly observe
Molecular Geology, Climate High Energy
Biology Astronomy modelling Physics

5. This ratio is not well correlated with data size
~60PB High Energy Physics
Data Size
Molecular Astronomy
Biology
~5PB Climate Models
Ratio of model predictability

6. “Knowing stuff” is critical to biology…
• The bases of the human genome
• … and the Mouse, Rat, Wheat, Ecoli, Plasmodium, Cow….
• The functions of proteins
• Enzymes, Transcription Factors, Signalling….
• The types of cells, their lineages and organ composition
• …and all the molecular components in each cell
• Small molecules
• … and their conversions, binding partners
• Structures of molecules, complexes and cells
• … at atomic and higher resolution

7. Two fundamental types of information
• Experimental data • Consensus Knowledge
• The result of a specific • Integration of different
experiment strands of information on a
• Often an experiment topic
specific, data heavy part • Realised as a
plus a “meta-data” part computationally accessible
• Might be contradictory scheme
• “Primary paper” • “Review article”

8. Five types of curation

9. Experimental Data Entry
• Intact – Protein:Protein
interactions
• GWAS Catalog –
extraction of summary
statistics

10. Experimental Meta data capture
• Sample, CDS lines in
ENA
• Sample in Metabolights,
PRIDE etc
• Machine and analysis
specification in PDB,
PRIDE, ENA

11. Consensus integration of information
• GenCode gene models in
human
• Summaries and GO
assignment in UniProt
• Pathway information in
Reactome
• GO assignment and
summaries in MODs (eg,
PomBase, WormBase,
PhytoPathDB etc)

12. Knowledge frameworks
• The EC classification
• Cell type ontologies
• Cell lineages – Worms!
• SnowMed, HPO etc
• GO ontologies

13. Knowledge management
• Creation of rules
representing ENA
standards compliance
• Cross-ontology
coordination (eg, EFO) or
tieing (GO  ChEBI)
• RuleBase / UniRule
curation processes

14. Data Entry vs Programming
Direct Programmatic
Data Entry Data Entry
“Messy” Scripting
Improved
Data entry
tools RuleBase,
Computational Accessible
Standards

15. Thank You!

16. Curation Dilema
• If you do your job well… • If you do your job badly…
• Everyone assumes it’s • Everyone assumes it’s
easy easy
• People forget about the • People forget about the
complexity complexity
• You are ignored  • People complain 

17. Why we need an infrastructure…

18. Infrastructures are critical…

19. But we only notice them when they go wrong

20. Biology already needs an information
infrastructure
• For the human genome
• (…and the mouse, and the rat, and… x 150 now, 1000 in the
future!) - Ensembl
• For the function of genes and proteins
• For all genes, in text and computational – UniProt and GO
• For all 3D structures
• To understand how proteins work – PDBe
• For where things are expressed
• The differences and functionality of cells - Atlas

21. ..But this keeps on going…
• We have to scale across all of (interesting) life
• There are a lot of species out there!
• We have to handle new areas, in particular medicine
• A set of European haplotypes for good imputation
• A set of actionable variants in germline and cancers
• We have to improve our chemical understanding
• Of biological chemicals
• Of chemicals which interfere with Biology

22. ELIXIR’s mission
To build a sustainable
European infrastructure for
biological
information, supporting life
science research and its
medicine
translation to:
environment
bioindustries
society
22

23. How?
Fully Centralised Fully Distributed
Pros: Stability, reuse, Pros: Responsive, Geographic
Learning ease Language responsive
Cons: Hard to concentrate Cons: Internal communication overhead
Expertise across of life science Harder for end users to learn
Geographic, language placement Harder to provide multi-decade stability
Bottlenecks and lack of diversity

24. Research Healthcare
International National
EBI / Elixir Healthcare
English National Language
Low legalities Complex legalities
2

25. Other infrastructures needed for biology
• EuroBioImaging
• Cellular and whole organism Imaging
• BioBanks (BBMRI)
• We need numbers – European populations – in particular for rare
diseases, but also for specific sub types of common disease
• Mouse models and phenotypes (Infrafrontier)
• A baseline set of knockouts and phenotypes in our most tractable
mammalian model
• (it’s hard to prove something in human)
• Robust molecular assays in a clinical setting (EATRIS)
• The ability to reliably use state of the art molecular techniques in a
clinical research setting

26. (you can follow me on twitter @ewanbirney)
I blog and update this on Google Plus publically