1. Distributed Computing for System Biology using Taverna Workflows
Dr Katy Wolstencroft
myGrid
University of Manchester

2. Workshop Outline myGrid and its workflow workbench, Taverna
What workflows are and why they are useful
What you can do using myGrid – current research
Genotype-phenotype correlations – Microarray and QTL analysis for trypanosome resistance, Paul Fisher, Andy Brass, University of Manchester
Superimposing array data onto pathway maps and displaying in SBML models – Peter Li, MCISB
Data and metadata management
Workflow building demonstration
How you can use Taverna in the future - some new developments

3. What is myGrid?
myGrid middleware components to support in silico experiments in biology
Originally designed to support bioinformatics
Expanded into new areas:
Chemoinformatics
Health Informatics
Medical Imaging
Integrative Biology
Open source – and always will be

4. History EPSRC funded UK eScience Program Pilot Project
NOW – OMII uk node

5. OMII Open Middleware Infrastructure Institute
University of Manchester (myGrid) joined with the Universities of Edinburgh (OGSA-DAI) and Southampton (OMII phase 1) in March 2006
OMII-UK aims to provide software and support to enable a sustained future for the UK e-Science community and its international collaborators.
A guarantee of development and support

6. myGrid in OMII-UK myGrid OMII Stack OGSA-DAI
middleware which supports in silico experiments in science, predominantly biology
myGrid
Grid resources, such as schedulers and registries as well as software engineering processes, testing and dissemination
OMII Stack
middleware to assist with access and integration of data from separate sources via the grid.
middleware which supports the exposure of distributed data resources, such as relational or XML databases, on to grids
OGSA-DAI

7. The Life Science Community
In silico Biology is an open Community
Open access to data
Open access to resources
Open access to tools
Open access to applications
Global in silico biological research

8. The Community Problems
Everything is Distributed
Data, Resources and Scientists
Heterogeneous data
Very few standards
I/O formats, data representation, annotation
Everything is a string!
Integration of data and interoperability of resources is difficult

9. Lots of Resources NAR 2007 – 968 databases Well over 200 from there.
139 different database for biopathways alone.
Lincoln Stein describes it a as a bionation – like italy in the 19th Century.

10. Systems Biology Problems
Systems biology and bioinformatics share similar challenges
Bioinformatics and systems biology in particular are about integrating pieces of data and knowledge from distributed sources
Systems biology problems magnify those existing in bioinformatics – increase the number of different resources/data types for integration

11. Traditional Bioinformatics
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12. Cutting and Pasting Advantages: Disadvantages:
Low Technology on both server and client side
Very Robust: Hard to break.
Data Integration happens along the way
Disadvantages:
Time Consuming (and painful!)
Can be repeated rarely
Limited to small data sets.
Error Prone:
Poor repeatability
How do you do this for a genome/proteome/metabolome of information!

13. Pipeline Programming Advantages Disadvantages Repeatable
Allows automation
Quick, reliable, efficient
Disadvantages
Requires programming skills
Difficult to modify
Requires local tool and database installation
Requires tool and database maintenance!!!

14. What we want as a solution
A system that is:
Allows automation
Allows easy repetition, verification and sharing of experiments
Works on distributed resource
Requires few programming skills
Runs on a local desktop / laptop

15. myGrid as a solution
myGrid allows the automated orchestration of in silico experiments over distributed resources from the scientist’s desktop
Built on computer science technologies of:
Web services
Workflows
Semantic web technologies

16. Web Services
Web services support machine-to-machine interaction over a network. Note: NOT the same as services on the web
Web services are a:
technology and standard for exposing code / databases with an API that can be consumed by a third party remotely.
describes how to interact with it.
They are:
Self-contained
Self-describing
Modular
Platform independent
Most people familiar with doing a blast online – this is more like submitting a blast job online

17. Workflows General technique for describing and enacting a process
Describes what you want to do, not how you want to do it
High level description of the experiment
Repeat Masker
Web service
GenScan
Web Service
Blast Web Service

18. Workflows
Workflow language specifies how bioinformatics processes fit together.
High level workflow diagram separated from any lower level coding – you don’t have to be a coder to build workflows.
Workflow is a kind of script or protocol that you configure when you run it.
Easier to explain, share, relocate, reuse and repurpose.
Workflow <=> Model
Workflow is the integrator of knowledge
The METHODS section of a scientific publication
Reuse
Flexibility
Not transparent
Not prescriptive
Expose the protocol of the in silico experiment
Easy to explain
Mainly data pipelines
Control flow – BPEL…

19. Workflow Advantages Automation
Capturing processes in an explicit manner
Tedium! Computers don’t get bored/distracted/hungry/impatient!
Saves repeated time and effort
Modification, maintenance, substitution and personalisation
Easy to share, explain, relocate, reuse and build
Releases Scientists/Bioinformaticians to do other work
Record
Provenance: what the data is like, where it came from, its quality
Management of data (LSID - Life Science Identifiers)

20. Different Workflow Systems
Kepler
Triana
DiscoveryNet
Taverna
Geodise
Pegasus
Pipeline Pilot
Each has differences in action, language, access restrictions, subject areas

21. Taverna Workflow Components
SOAPLAB
Web Service
Any Application
e.g. DDBJ BLAST
Scufl Simple Conceptual Unified Flow Language
Taverna Writing, running workflows & examining results
SOAPLAB Makes applications available

22. An Open World Open domain services and resources.
Taverna accesses services
Third party – we don’t own them – we didn’t build them
All the major providers
NCBI, DDBJ, EBI …
Enforce NO common data model.
Wanted to access as many resources as possible
Tidy upOpen source (LGPL)
Open domain services and resources
Open community
Open application
Nothing specific to biology, although oriented to it
Open model and open data
No prescribed typing or domain data model
A layered, descriptive, information model
Open architecture
Service Oriented Architecture
Loosely coupled, Web services based
We want to access as many as possible
Quality Web Services considered desirable

23. Adding your own web services
SoapLab
Java API Consumer
import Java API of libSBML as workflow components

24. Shield the Scientist – Bury the Complexity
Scufl Model
Taverna
Workbench
Workflow Execution
Application
Simple Conceptual Unified Flow Language
Not just web services.
Data pipelines.
Domain services work with workflow engine not each other.
Workflow enactor
...
Processor
Processor
Processor
Processor
Processor
Processor
Processor
Styx
Processor
...
Bio
MART
WSRF
Plain
Web
Service
Soap
lab
Bio
MOBY
Local
Java
App
Enactor
Styx
client R
package

25. What can you do with myGrid?
~27700 downloads
Users worldwide
US, Singapore, UK, Europe, Australia
Systems biology
Proteomics
Gene/protein annotation
Microarray data analysis
Medical image analysis
Heart simulations
High throughput screening
Genotype/Phenotype studies
Health Informatics
Astronomy
Chemoinformatics
Data integration
Commercial use by Apple Corp + BioTeam
Users across USA and Europe
EU EMBRACE NoE
Soaplab services supported by European Bioinformatics Institute
Service providers
BioMOBY, BluePrint, EMBOSS, BioMART …
Middleware developers
Semantic Grid technologies, workflow
SIMDAT,EGEE, GridSphere, SCEC
Life Science users and tool developers
Virginia Bioinformatics Institute, SDSC, UC Davis, USC, VL-e, Purdue, caBIG, EMBRACE
Commercial
Lexicon Genetics, Apple, BioTeam
High profile – three citations in Science May 2004 Distributed Computing issue
International invitations

26. Trypanosomiasis in Africa
Steve Kemp
Andy Brass
Paul Fisher

27. Trypanosomiasis Study
A form of Sleeping sickness in cattle – Known as n’gana
Caused by Trypanosoma brucei
Can we breed cattle resistant to n’gana infection?
What are the causes of the differences between resistant and susceptible strains?

28. Trypanosomiasis Study
Understanding Phenotype
Comparing resistant vs susceptible strains – Microarrays
Understanding Genotype
Mapping quantitative traits – Classical genetics QTL
Need to access microarray data, genomic sequence information, pathway databases AND integrate the results

29. Genotype Phenotype 200 ? Microarray + QTL
The middle-out approach
Utilising microarray data as a middle ground to investigating genotype-phenotype correlations enables us to begin our investigations from an information rich perspective
From the previous slide we showed that the expression of genes form proteins, and the interaction of proteins into pathways influence the overall phenotype.
From this we therefore find an ideal place in which to narrow down our search for possible correlations between genotype and phenotype
Pathways tell us what proteins, and therefore genes are involved in a particular biological process
If we know what pathways are involved in the phenotype we can find out what genes are involved
Using pathways in this manner is basically a noise and dimensionality reduction procedure
By determining which genes, and subsequently their pathways, are over or under expressed in our microarray study allows us to see what processes may be influencing our phenotype
However some of these may be due to noise in the data
We can then begin to look at the pathways from the genes in the QTL region
Cross correlating these two pathway lists shows us which genes in the QTL region and which pathways are directly influencing the phenotype
Web definitions for Quantitative trait loci a combination of genes that often controls economically significant genetic traits, such as disease resistance in animals and, in dairy cattle, milk quality and quantity.
Genes captured in microarray experiment and present in QTL region
Phenotypic response investigated using microarray in form of expressed genes or evidence provided through QTL mapping
Microarray + QTL

30. A – Retrieve genes in QTL region
Key:
A – Retrieve genes in QTL region
B – Annotate genes with external database Ids
C – Cross-reference Ids with KEGG gene ids
D – Retrieve microarray data from MaxD database
E – For each KEGG gene get the pathways it’s involved in
F – For each pathway get a description of what it does
G – For each KEGG gene get a description of what it does
Taverna workflow diagram of QTL to pathway

31. Results
Identified a pathway for which its correlating gene (Daxx) is believed to play a role in trypanosomiasis resistance.
Manual analysis on the microarray and QTL data had failed to identify this gene as a candidate.
A Systematic Strategy for Large-Scale Unbiased Analysis of Genotype-Phenotype Correlations
Paul Fisher, Cornelia Hedeler, Katy Wolstencroft, David Withers, Stuart Owen, Helen Hulme, Harry Noyes, Steve Kemp, Robert Stevens and Andy Brass.
Bioinformatics in review
Confirmed by the biologists

32. Why was the Workflow Approach Successful?
Workflow analysed each piece of data systematically
Eliminated user bias and premature filtering of datasets and results leading to single sided, expert-driven hypotheses
The size of the QTL and amount of the microarray data made a manual approach impractical
Workflows capture exactly where data came from and how it was analysed
Workflow output produced a manageable amount of data for the biologists to interpret and verify
“make sense of this data” -> “does this make sense?”

33. Trichuris muris (mouse whipworm) infection parasite model of the human parasite - Trichuris trichuria)
Identified the biological pathways involved in sex dependence in the mouse model, previously believed to be involved in the ability of mice to expel the parasite.
Manual experimentation: Two year study of candidate genes, processes unidentified
Workflows: trypanosomiasis cattle experiment was reused without change.
Analysis of the resulting data by a biologist found the processes in a couple of days.
Worm Lady's name is Joanne Pennock and as far as I know she works for Prof. Richard K.Grencis.
DescriptionTrichuris muris - the mouse whipworm is a useful parasite model of the human parasite - Trichuris trichuria. Whipworms derive their name from their characteristic morphology. Adults occupy the large intestine with their anterior ends embedded in the cells lining the intestine. Transmission occurs by ingestion of contaminated material.
Jo didn’t know about the tools; she didn’t know how to do it properly. REUSE
Identified sex-dependant biological pathways involved in mouse model. The correlation of sex depandance and the ability of mice to expel the parasite had previously been hypothesised, however, had not been verified using conventional manual analysis techniques.
Joanne Pennock, Richard Grencis
University of manchester

34. Workflow Reuse – Workflows are Scientific Protocols – Share them!
Addisons Disease
SNP design
Protein annotation
Microarray analysis
myGrid Workflow Repository

35. A workflow marketplace
Share the experiments.
We did all sorts of fancy repository ideas and data sharing ideas. But actually something simple is what they needed.
Would be preceeded by old MIR screenshot.
A workflow marketplace

36. MCISB Case Study Manchester Centre for Integrative Systems Biology
Case study for their informatics infrastructure
Superimposing array data onto pathway maps using Taverna workflows
As we saw in Paul’s workflow – pathway-centric model reduces complexity for scientist. Building the sbml model shows the scientist changes in regulation clearly. They can drill-down into the workflow for exact values using the provenance browser
[Peter Li, Doug Kell, 2006]

37. MCISB Study Involves Combining public and local data
Gathering, creating and storing data in SBML models
Visualising results using pre-existing SMBL-based tools
Why?
To view transcriptome data from the context of pathways
To see the effects of up/down regulation on pathways

38. Pathway maps are first saved as SBML models using bespoke SMBL software - CellDesigner
Glycolysis pathway

39. Transcriptome pathway workflow
Read gene names of enzymes
from SBML Cell Designer model
Query Big Expt data in MaxD
using gene names
MaxD
Create new SBML model
Calculate colour of enzyme
nodes based on mRNA
expression levels

40. libSBML using the API Consumer
Building the SBML model was possible because of Taverna’s extensibility
Did not have to write new services – just used the API consumer to add SBML library
API consumer can be downloaded from the myGrid website

41. New SBML models viewed using Cell Designer
Decreased levels
of GPM3
JC_C _Measurement
JC_N _Measurement

42. Results
Visualisation of one data source over another by the building of a common model during the workflow run
Promotes re-use by providing a simple interface for interpretation by the scientist
Offers a proof of principle for the overlay of other omics data:
Proteomics data from PRIDE
Metabolomic data from MEMO

43. A Practical Guide to Building and Managing in silico Experiments

44. Semantic Web Technologies
myGrid built on Web Services, Workflows AND semantic web technologies
Semantic web technologies are used to:
Find appropriate services during workflow design
Find similar workflows for reuse and repurposing
Record the process and outcome of an experiment, in context
->>>> the experimental provenance

45. Finding Services
There are over 3000 distributed services. How do we find an appropriate one?
Find services by their function instead of their name
We need to annotate services by their functions.
The services might be distributed, but a registry of service descriptions can be central and queried
Same problem as finding the web resources

46. Feta Semantic Discovery
Feta is the myGrid component that can query the service annotations and find services
Questions we can ask:
Find me all the services that perform a multiple sequence alignment And accept protein sequences in FASTA format as input

47. myGrid Ontology Upper level ontology Task Informatics
Specialises
Upper level
ontology
Task
Informatics
Molecular Biology
Bioinformatics
Web Service
Contributes to
sequence
biological_sequence
protein_sequence
nucleotide_sequence
DNA_sequence
protein_structure_feature
BLASTp service
Similarity Search Service
BLAST service
InterProScan service
Within myGrid a suite of ontologies have been developed. These ontologies not
only model the knowledge of the domain but also provide the necessary mod-
elling elements for describing bioinformatics resources (databases, tools) and the
services that provide access to them.
Figure 3.2 shows the suite of myGrid domain ontologies [95]:
² The upper level ontology is a foundation for all other ontologies; it provides
the high-level categories that can be commonly found in a life-sciences on-
tology, such as Structure and Substance.
² The Informatics ontology captures basic informatics concepts such as data,
database, metadata and so forth.
² The Bioinformatics Ontology builds on the Informatics ontology which has a
rather generic view and introduces bioinformatics speci¯c resource descrip-
tions such as the SWISS-PROT database, BLAST Application or EMBOSS
Tool suite
² The Molecular Biology ontology describes molecular biology concepts, data
of which is largely subject to processing and integration within the myGrid
domain. Examples of concepts in this ontology are protein, nucleic acid or
DNA sequence.
² The Publishing Ontology provides the concepts to be used to describe sci-
enti¯c literature, which is important source of biological knowledge in the
domain. Examples include article, abstract, citation, reference.
² The Organization Ontology provides concepts to describe organizations and
the instances of those concepts such as European Bioinformatics Institute.
² The Task Ontology provides a classi¯cation of tasks that can be performed
by a bioinformatics service in myGrid's domain. Examples include retriev-
ing, aligning, global aligning, local aligning.

48. Feta Architecture Obtain descriptions Taverna Workbench 3
Obtain Classification
Feta GUI Client
DL Reasoner
User
Feta Engine
Service
Semantic Discovery 3
Ontology Editor 4
Classification
- In RDF(S) -
Build myGrid Domain Ontology
Ontologist

49. Annotations Feta has been available for over a year
Only just been included in the release
Need critical mass of service annotations before release
By demonstrating the use of service annotation, we aim to encourage service providers to provide the annotations in the future
Annotation experiments with users and domain experts
Domain expert annotations much better
We now have a domain expert for full-time service annotation

50. Data Management
Workflows can generate vast amount of data - how can we manage and track it?
We need to manage
data AND
metadata AND
experiment provenance
Workflow experiments may consist of many workflows of the same, or different experiments.
Scientists need to check back over past results, compare workflow runs and share workflow runs with colleagues

52. Provenance – the myGrid logbook
Smart Tea
Who, What, Where, When, Why?, How?
Context
Interpretation
Logging & Debugging
Reproducibility and repeatability
Evidence & Audit
Non-repudiation
Credit and Attribution
Credibility
Accurate reuse and interpretation
Just good scientific practice
Figures from smarttea and biomoby
BioMOBY

53. Advanced Provenance Features
Smart re-running
Experiment mining
Cross experiment mining
From which Ensembl gene does pathway mmu come from?

54. Workflows over Results
Automatically backtrack through the data provenance graph
Entrez dF dF dF dF
Pathway_id
KEGG_id
Uniprot
Ensembl_gene_id

55. Workflow Demonstrations

56. Conclusions
Web services and workflows are powerful technologies for in silico science
automation
high throughput experiments
systematic analysis
Interoperability of distributed resources

57. Changes to Scientific Practice
Systematic and comprehensive automation
Eliminated user bias and premature filtering of datasets and results leading to single sided, expert-driven hypotheses
Dry people hypothesise, wet people validate
“make sense of this data” -> “does this make sense?”
Workflow factories
Different dataset, different result
Workflow market
Accurate provenance

58. Contact Us Taverna development is user-driven
Please tell us what you would like to see via the mailing lists:
Taverna- Users and Taverna-Hackers
Download software and find out more at:

59. myGrid acknowledgements
Carole Goble, Norman Paton, Robert Stevens, Anil Wipat, David De Roure, Steve Pettifer
OMII-UK Tom Oinn, Katy Wolstencroft, Daniele Turi, June Finch, Stuart Owen, David Withers, Stian Soiland, Franck Tanoh, Matthew Gamble.
Research Martin Szomszor, Duncan Hull, Jun Zhao, Pinar Alper, Antoon Goderis, Alastair Hampshire, Qiuwei Yu, Wang Kaixuan.
Current contributors Matthew Pocock, James Marsh, Khalid Belhajjame, PsyGrid project, Bergen people, EMBRACE people.
User Advocates and their bosses Simon Pearce, Claire Jennings, Hannah Tipney, May Tassabehji, Andy Brass, Paul Fisher, Peter Li, Simon Hubbard, Tracy Craddock, Doug Kell.
Past Contributors Matthew Addis, Nedim Alpdemir, Tim Carver, Rich Cawley, Neil Davis, Alvaro Fernandes, Justin Ferris, Robert Gaizaukaus, Kevin Glover, Chris Greenhalgh, Mark Greenwood, Yikun Guo, Ananth Krishna, Phillip Lord, Darren Marvin, Simon Miles, Luc Moreau, Arijit Mukherjee, Juri Papay, Savas Parastatidis, Milena Radenkovic, Stefan Rennick-Egglestone, Peter Rice, Martin Senger, Nick Sharman, Victor Tan, Paul Watson, and Chris Wroe.
Industrial Dennis Quan, Sean Martin, Michael Niemi (IBM), Chimatica.
Funding EPSRC, Wellcome Trust.