an introduction to taverna workflows - bioinformatics · 2013-06-28 · an introduction to taverna...
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An Introduction to Taverna Workflows
Katy WolstencroftmyGrid
University of Manchester
What is myGrid?
• myGrid is a suite components to support in silicoexperiments in biology
• Taverna workbench = myGrid user interface
• Originally designed to support bioinformatics
Expanded into new areas:
Chemoinformatics
Health Informatics
Medical Imaging
Integrative Biology
• Open source – and always will be
History
EPSRC funded UK eScience Program Pilot Project
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
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
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
Lots of Resources
NAR 2007 – 968 databases
Traditional Bioinformatics
12181 acatttctac caacagtgga tgaggttgtt ggtctatgtt ctcaccaaat ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag tcaatagcct tttttagctt 12301 gaccatccta atagatacac agtggtgtct cactgtgatt ttaatttgca ttttcctgct 12361 gactaattat gttgagcttg ttaccattta gacaacttca ttagagaagt gtctaatatt 12421 taggtgactt gcctgttttt ttttaattgg gatcttaatt tttttaaatt attgatttgt 12481 aggagctatt tatatattct ggatacaagt tctttatcag atacacagtt tgtgactatt 12541 ttcttataag tctgtggttt ttatattaat gtttttattg atgactgttt tttacaattg 12601 tggttaagta tacatgacat aaaacggatt atcttaacca ttttaaaatg taaaattcga 12661 tggcattaag tacatccaca atattgtgca actatcacca ctatcatact ccaaaagggc 12721 atccaatacc cattaagctg tcactcccca atctcccatt ttcccacccc tgacaatcaa 12781 taacccattt tctgtctcta tggatttgcc tgttctggat attcatatta atagaatcaa
Cutting and Pasting
• Advantages:– 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!
Pipeline Programming
• Advantages– Repeatable
– Allows automation
– Quick, reliable, efficient
• Disadvantages– Requires programming skills
– Difficult to modify
– Requires local tool and database installation
– Requires tool and database maintenance!!!
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
myGrid as a solution
myGrid allows the automated orchestration of in silicoexperiments over distributed resources from the scientist’s desktop
Built on computer science technologies of:
• Web services
• Workflows
• Semantic web technologies
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
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
RepeatMasker
Web service
GenScanWeb Service
BlastWeb Service
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 <=> ModelWorkflow is the integrator of knowledge
The METHODS section of a scientific publication
Workflows
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)
Different Workflow Systems
• Kepler
• Triana
• DiscoveryNet
• Taverna
• Geodise
• Pegasus
• Pipeline Pilot
Each has differences in action, language, access restrictions, subject areas
Taverna Workflow Components
Scufl Simple Conceptual Unified Flow LanguageTaverna Writing, running workflows & examining resultsSOAPLAB Makes applications available
SOAPLABWeb Service
Any Application
Web Service e.g. DDBJ BLAST
An Open World
• Open domain services and resources.• Taverna accesses 3000+ 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.
• Quality Web Services considered desirable
Adding your own web services
• SoapLab • Java API Consumer
import Java API of libSBML as workflow components
http://www.ebi.ac.uk/soaplab/
Services Landscape
Shield the Scientist – Bury the Complexity
Workflow enactor
Processor Processor
PlainWeb
Service
Soaplab
Processor
LocalJavaApp
Processor
Enactor
Processor
BioMOBY
Processor
WSRF
Processor
BioMART
Styx
Styxclient
Processor
Rpackage
...
...
Scufl Model
TavernaWorkbench
Workflow Execution
Application
Simple Conceptual Unified Flow Language
What can you do with myGrid?
• ~37000 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
http://www.genomics.liv.ac.uk/tryps/trypsindex.html
Trypanosomiasis in Africa
Andy B
rassS
teve Kem
pP
aul Fisher
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?
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
?200
Microarray + QTL
Phenotypic response investigated using microarray in form of expressed genes or evidence provided through QTL mapping
Genotype Phenotype
Genes captured in microarray experiment and present 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
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.
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?”
Trichuris muris(mouse whipworm) infection
parasite model of the human parasite -Trichuris trichuria)
• Identified the biological pathways involved in sex dependence inthe 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.
Joanne Pennock, Richard GrencisUniversity of manchester
Workflow Reuse – Workflows are Scientific Protocols – Share them!
Addisons Disease
SNP design
Protein annotation
Microarray analysis
A workflow marketplace
A Practical Guide to Building and Managing in silico Experiments
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
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
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
Specialises
myGrid Ontology
Upper level ontology
Task ontology
Informatics ontology
Molecular Biology ontology
Bioinformatics ontology
Web Service ontology
Contributes to
sequence
biological_sequence
protein_sequence
nucleotide_sequence
DNA_sequence
protein_structure_feature
BLASTp service
Similarity Search Service
BLAST service
InterProScan service
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
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
From which Ensembl gene does pathway mmu004620 come from?
Advanced Provenance Features
Smart re-runningExperiment miningCross experiment mining
Conclusions
Web services and workflows are powerful technologies for in silico science
– automation
– high throughput experiments
– systematic analysis
– Interoperability of distributed resources
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:
http://www.mygrid.org.uk
http://taverna.sourceforge.net
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.
• Industrial Dennis Quan, Sean Martin, Michael Niemi (IBM), Chimatica.• Funding EPSRC, Wellcome Trust.
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
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