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Metadata in my Grid: Finding Services for in silico Science

Metadata in my Grid: Finding Services for in silico Science. Dr Katy Wolstencroft myGrid University of Manchester. ……or how to use metadata and semantics to add value in a ‘standards free’ environment. Outline. Introduction to Taverna, my Grid and myExperiment

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Metadata in my Grid: Finding Services for in silico Science

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  1. Metadata in myGrid: Finding Services for in silico Science Dr Katy Wolstencroft myGrid University of Manchester

  2. ……or how to use metadata and semantics to add value in a ‘standards free’ environment

  3. Outline • Introduction to Taverna, myGrid and myExperiment • Bioinformatics – use of Web services and other services • Semantic Service Discovery in myGrid • myGrid ontology • Our experiences • BioCatalogue – bioinformatics service registry

  4. Taverna Workflow Workbench • Design and execution of workflows • Access to local and remote resources and analysis tools • Automation of data flow • Iteration over large data sets • Part of the myGrid project

  5. Client Applications myGrid Provenance Ontology myExperiment Web Interface Taverna Workbench GUI Workflow Warehouse Provenance Warehouse Service / Component Catalogue Feta Information Services Taverna Workflow Enactor LogBook Provenance Management Default Results Service Ontology Custom Datasets 3rd Party Resources (Web Services, Grid Services) Service Management Resources

  6. Lots of Resources NAR 2008 – over 1000 databases

  7. Where From? • Over 3500 services available • Major Service Providers • European Bioinformatics Institute • DNA DataBank of Japan • NCBI – USA • ‘Boutique’ Services • Individual research labs producing public data sets • Specialist tools for niche experiments

  8. What types of services? • HTML • WSDL Web Services • BioMart • R-processor • BioMoby • Soaplab • Local Java services • Beanshell • Workflows Variable or non-existent documentation or help

  9. Taverna in a ‘open’ world Advantages • Connection to lots of resources • Flexible system • Can adapt to new technologies Disadvantages • Services are developed for other purposes • We can’t control how that work • We have to deal with the heterogeneity

  10. Taverna Use • Users worldwide • Over 48500 downloads • Bioinformatics – largest group of users • Other users from • astronomy, • chemoinformatics, • health informatics • Systems Biology • Social sciences

  11. Sleeping Sickness in African Cattle • Caused by infection by parasite (Trypanosoma brucei) • Some cattle breeds more resistant than others • Differences between resistant and susceptible cattle? • Can we breed cattle resistant to infection? Steve Kemp Andy Brass High throughput experiments Microarray QTL analysis Fisher et al (2007).Nucleic Acids Res.35(16):5625-33 Paul Fisher http://www.genomics.liv.ac.uk/tryps/trypsindex.html

  12. Bioinformatics Workflows • Workflows allow high throughput experiments and automation • Workflows are encapsulations of experiments • Workflows developed for one experiment can be reused for others • Easier to share, reuse and repurpose The METHODS section of a scientific publication

  13. Workflow Reuse • Downloaded 836 times • Viewed 799 times • Jo Pennock, lab biologist with no bioinformatics experience – Mouse whipworm infection • Identified no candidate genes in 2 years with manual analysis • Identified candidate genes in several hours using Paul’s workflow

  14. In Silico Science Life Cycle Workflows are combinations of different services Locations and descriptions of services required at the design phase Reusing workflows – need to understand what they do

  15. Finding Services When using services, scientists need to: • Find them – in distributed locations, produced by different host institutions • Interpret them – what do the services do - what experiments can they perform using them? • Know how to invoke them – what data and initial parameters do they need to supply?

  16. We could Google for them… • If a service is called by the name you expect, you’ll find it • Search for ‘clustalw’ and ‘web service’ • What if its not? • The clustalw program from emboss is called ‘emma’ • What if it’s the only web service version of clustalw? • Does it stop you designing your workflow?

  17. Metadata from a WSDL <wsdl:message name="getGlimmersResponse"> <wsdl:part name="getGlimmersReturn" type="xsd:string"/> </wsdl:message> <wsdl:message name="aboutServiceRequest"/> <wsdl:message name="getGlimmersRequest"> <wsdl:part name="in0" type="xsd:string"/> <wsdl:part name="in1" type="xsd:string"/> <wsdl:part name="in2" type="xsd:string"/> <wsdl:part name="in3" type="xsd:string"/> <wsdl:part name="in4" type="xsd:string"/> <wsdl:part name="in5" type="xsd:string"/> <wsdl:part name="in6" type="xsd:string"/> <wsdl:part name="in7" type="xsd:int"/> <wsdl:part name="in8" type="xsd:string"/> Name of the service Uninformative names for parameters What kind of string? Pathport Web service from the Virginia Bioinformatics Institute http://pathport.vbi.vt.edu/services/wsdls/beta/glimmer.wsd

  18. Semantics and Web Services • SAWSDL – Semantic Annotations for WSDL working group • Virtually no uptake by bioinformatics service providers • Doesn’t address non-WSDL services

  19. Adding Semantics – Annotating Services Find services by their function instead of their name • The services might be distributed, but a registry of service descriptions can be central and queried • We need to annotate services with semantics In myGrid, we use the Feta Semantic Discovery tool and a semantic annotation tool – and expert curation

  20. myGrid Ontology Logically separated into two parts: • Service ontology Physical and operational features of (web) services • Domain ontology Annotation vocabulary for core bioinformatics data, data types and their relationships

  21. Service Ontology • Models services from the point of view of the scientist • Where is it? • How many inputs/outputs? • Who hosts it? • Invocation details are hidden by the Taverna workbench • Differs from related initiatives in this respect

  22. Domain Ontology • Informatics: captures the key concepts of data, data structures, databases and metadata. • Bioinformatics: The domain-specific data sources (e.g. the model organism sequencing databases), and domain-specific algorithms for searching and analyzing data (e.g. the sequence alignment algorithm, clustalw). • Molecular biology: Concepts include examples such as, protein sequence, and nucleic acid sequence. • Formats: A hierarchy describing bioinformatics file formats. For example, fasta format for sequence data, or phylip format for phylogenetic data • Tasks: A hierarchy describing the generic tasks a service operation can perform. Examples include retrieving, displaying, and aligning.

  23. myGrid Ontology sequence protein_structure_feature biological_sequence Similarity Search Service protein_sequence BLAST service nucleotide_sequence DNA_sequence BLASTp service InterProScan service Specialises Web Service ontology Contributes to Task ontology Informatics ontology Molecular Biology ontology Bioinformatics ontology

  24. Example Service Annotation • Example : BLAST from the DDBJ • Performs task: Alignment • Uses Method: Similarity Search Algorithm • Uses Resources: DNA/Protein sequence databases • Inputs: • biological sequence (and format) • database name (and format) • blast program (and format) • Outputs: Blast Report • Minimum Information model

  25. Minimum Models in Biology • MIBBI – Minimum Information about Biomedical and Biological Investigations • MIAME – Microarray experiments • MIAPE - Proteomics • MIRIAM – Biochemical models (SBML models) • Etc • MIOAWS – Minimum Information About the Operation of the Web Service

  26. myGrid Ontology First version of the ontology ~ 2002 Originally developed in DAML+OIL Now developed in OWL and a version exported to RDFS Number of classes in the ontology ~750 Domain and service ontology used by myGrid users and developers of myGrid related plugins Service ontology also used by BioMoby W3C compliant WRT ontology modelling

  27. How do we use the ontology? Two methods of service description • Decision Making - reasoning Single description – whole service model Ontology used to build a single, complete service description and annotations are classified Enables automated composition of workflows 2. Decision Support - querying Composite matches to ontology terms Multiple terms are used to query the annotations

  28. Predicted Genes out Sequence RepeatMasker Web service Gene Prediction Web Service BlastWeb Service Originally – Decision Making • Difficult and time consuming to produce the detailed service descriptions • Assumption that people would want automated workflow composition Works over underlying databases Many different algorithms – effective with different organisms etc Only 1 exists

  29. Resource Compatibility Difference? • Scientists choice – can they be sure the experiments are equivalent? Example: Nucleotide sequence databases • GenBank - USA • EMBL - Europe • DDBJ - Japan Nightly updates – mirrored data BUT the sequence annotation could be different

  30. myGrid – Decision Support • Reducing the list of know services from thousands to several • Scientist makes the final decision about which of a selection of services to use • Services are ‘tagged’ with terms from the ontology – very simple! • No requirement for OWL-DL reasoning • Generating service annotations is much easier

  31. So why do we need OWL? Building workflows is a two-stage process • Assembly – identifying services that perform the scientific functions needed for the experiment • Gluing – identifying how (or more usually, if) theses services are compatible If they are incompatible – we need services that convert data formats and act as connectors – we call these services Shims

  32. Cases for using the OWL version • Automatic shim integration • Shims don’t do anything scientific, so choosing one over another makes no difference • Detecting mismatches • A scientist has built a workflow and the output of processor 1 is incompatible with processor 2

  33. Limitations of the Current Model • Feta discovery tool is only accessible from the Taverna Workbench • Only pertinent to Taverna users – other people need to find and use web services • Focuses on finding services, but not workflows. For reuse, we need to do both • Closed annotation system - myGrid curator provides service descriptions – only 700 so far!

  34. BioCatalogue:Public Bioinformatics Service Registry • Collaboration between University of Manchester and EBI • Expanding from a service for Taverna users to a service for anyone using bio web services • Combine service and workflow discovery • Accelerating the process of gathering service descriptions/annotations by engaging the scientific community • Combines the myGrid initiative with BioMoby etc

  35. Combining Service and Workflow Discovery myExperiment – social networking – Web 2.0 • Workflows tagged • No formal model • No control • Services – semantically described, ontology terms • Access each through the same interface • Exchanging metadata objects

  36. ‘Shopping’ for Services and Workflows Screen shot of bio Service shopping site

  37. Getting the Minimum Community annotation • Must be easy and quick • Must allow partial descriptions • Multiple annotations of the same service • What is the minimum information to enable • service discovery • service invocation • Tagging terms to formal models – OWL, SKOS intermediate?

  38. Grading Services • Bronze – enough to locate the service. Example of service invocation • Silver • Gold • Platinum – full description. All properties annotated – including dependencies between them – reliability metrics etc

  39. Annotation Provenance • Who said what about what? • Harvesting community annotation • Verifying and augmenting by a curator • ‘Trust’ Models • Annotation versions • In a workflow context • As stand alone services

  40. Annotation Process

  41. Open Issues • ‘Open’ world means we cannot impose metadata standards • Lots of heterogeneity • Ontology modelling stable standards to build upon • Web services – shifting standards – need flexibility for future-proofing • Other services as well as web services • Combining and exchanging metadata objects behind interfaces • Can we adopt something from the digital library community? e.g. OAI and ORE (Open Archives InitiativeObject Reuse and Exchange )

  42. 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, Alan Williams, Ian Dunlop • 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, Marco Roos, Matthew Pocock, Mark Wilkinson • 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. • IndustrialDennis Quan, Sean Martin, Michael Niemi (IBM), Chimatica. • Funding EPSRC, Wellcome Trust. http://www.mygrid.org.uk http://www.myexperiment.org

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