Bioinformatics and Grids

1
Bioinformatics and Grids

• Professor Carole Goble,
• University of Manchester,UK
• carole_at_cs.man.ac.uk
• Director of myGrid e-Science project
• Co-director ESNW e-Science Regional Centre

2
Roadmap

• Post Genome biology
• Challenges for bioinformatics
• Why biology isnt physics
• Information-centric Grids
• An example myGrid
• Other projects
• Take home

3
Take home

• Complexity Diversity - Size isnt everything.
• Computation is important but information and
  knowledge services dominate.
• Integration, curation, annotation, fusion
• Automating support for integration and fusion
  means moving from
• human interaction to machine interaction.
• machine readable to machineunderstandable.
• Metadata using ontologies for finding, managing
  controlling services content

4
Functional Genomics

• An integrated view of how organisms work and
  interact in growth, development and pathogenesis
• From single gene to whole genome
• From single biochemical reactions to whole
  physiological and developmental systems
• What do genes do?
• How do they interact?

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Genotype to Phenotype
DNA chips
Modelling
Expression
Folding
population
protein sequence

• Synchrotron

• Proteomics
• Domain analysis

• SNP
• Gene prediction
• HTP Sequencing

• Link the observable behaviour of an organism with
  its genotype

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Drug Discovery
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Pharmacogenomics Knowledge/Information Flow
Hypotheses
Design
Integration
ClinicalResourcesIndividualisedMedicine
Data Mining Case-BaseReasoning
InformationFusion
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Use Cases (I3C)

• Show me all the genes in the glucose metabolism
  pathway and get their GenBank accession numbers
• Find all the citations for the HOX gene family
  for human and mouse
• Find all the kinase genes from Wormbase and
  retrieve the DNA sequence

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Use Cases

• Show me Nucleotide binding proteins in mouse
• Answer
• P12345 in Swiss-Prot is an ATPase
• Terri Attwood is an expert on this
• Jackson labs have a database but you need to
  register
• A paper has just been published in Proteins by
  the Stanford lab on this.

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Which compounds interact with (alpha-adrenergic
receptors) ((over expressed in (bladder
epithelial cells)) but not (smooth muscle
tissue)) of ((patients with urinary flow
dysfunction) and a sensitivity to the
(quinazoline family of compounds))?
Enzyme database
SNPs database
Tissue database
Drug formulary
High throput screening
Receptor database
Clinical trials database
Chemical database
Expressn. database
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Large amounts of data
http//www3.ebi.ac.uk/Services/DBStats/

• EMBL July 2001
• 150 Gbytes
• Microarray
• 1 Petabyte per annum
• Sanger Centre
• 20 terabytes of data
• Genome sequences increase 4x per annum

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High throughput experimental methods

• Micro arrays for gene expression
• Robot-based capture
• 10K data points per chip
• 20 x per chip
• Cottage industry to industrial scale

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Heterogeneity

• Data types forms
• Community
• Autonomy
• Over 500 different databases
• Different formats, structure, schemas, coverage
• Web interfaces, flat file distribution,

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Heterogeneity

• Complexity
• Diversity

Proteome
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Heterogeneity

• Complexity
• Diversity

Genomic, proteomic, transcriptomic, metabalomic,
protein-protein interactions, regulatory
bio-networks, alignments, disease, patterns
motifs, protein structure, protein
classifications, specialist proteins (enzymes,
receptors),
Proteome
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Heterogeneous Data

• Multimedia
• Images Video
• Text annotations literature
• Descriptive as well as numeric
• Knowledge-based

Text Extraction
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Swiss-Prot
SWISSPROTTET9_ENTFA ID TET9_ENTFA
STANDARD PRT 639 AA. AC P21598 DT
01-MAY-1991 (REL. 18, CREATED) DT 01-MAY-1991
(REL. 18, LAST SEQUENCE UPDATE) DT 01-OCT-1993
(REL. 27, LAST ANNOTATION UPDATE) DE
TETRACYCLINE RESISTANCE PROTEIN TETM (TRANSPOSON
TN916). GN TETM(916). OS ENTEROCOCCUS
FAECALIS (STREPTOCOCCUS FAECALIS). RA BURDETT
V. RL NUCLEIC ACIDS RES. 186137-6137(1990). CC
-!- FUNCTION ABOLISH THE INHIBITORY EFFECT OF
TETRACYCLIN ON PROTEIN CC SYNTHESIS BY A
NON-COVALENT MODIFICATION OF THE RIBOSOMES. CC
-!- SIMILARITY VERY HIGH TO OTHER TETM/TETO
PROTEINS. CC -!- SIMILARITY TO GTP-BINDING
ELONGATION FACTORS. DR EMBL X56353 G47062
-. DR PIR S13142 S13142. DR PROSITE
PS00301 EFACTOR_GTP 1. KW PROTEIN
BIOSYNTHESIS ANTIBIOTIC RESISTANCE
GTP-BINDING KW TRANSPOSABLE ELEMENT. FT
NP_BIND 10 17 GTP (BY
SIMILARITY). FT NP_BIND 74 78
GTP (BY SIMILARITY). SQ SEQUENCE 639 AA
72464 MW 523F1359 CRC32 gtTET9_ENTFA MKIINIGVLAH
VDAGKTTLTESLLYNSGAITELGSVDKGTTRTDNTLLERQRGITIQTGI
TSFQWENTKVNIIDTPGHMDFLAEVYRSLSVLDGAILLISAKDGVQAQTR
ILFHALRKMG IPTIFFINKIDQNGIDLSTVYQDIKEKLSAEIVIKQKVE
LYPNVCVTNFTESEQWDTVIE GNDDLLEKYMSGKSLEALELEQEESIRF
QNCSLFPLYHGSAKSNIGIDNLIEVITNKFYS STHRGPSELCGNVFKIE
YTKKRQRLAYIRLYSGVLHLRDSVRVSEKEKIKVTEMYTSING ELCKID
RAYSGEIVILQNEFLKLNSVLGDTKLLPQRKKIENPHPLLQTTVEPSKPE
QREM LLDALLEISDSDPLLRYYVDSTTHEIILSFLGKVQMEVISALLQE
KYHVEIEITEPTVIY MERPLKNAEYTIHIEVPPNPFWASIGLSVSPLPL
GSGMQYESSVSLGYLNQSFQNAVMEG IRYGCEQGLYGWNVTDCKICFKY
GLYYSPVSTPADFRMLAPIVLEQVLKKAGTELLEPYL SFKIYAPQEYLS
RAYNDAPKYCANIVDTQLKNNEVILSGEIPARCIQEYRSDLTFFTNGR S
VCLTELKGYHVTTGEPVCQPRRPNSRIDKVRYMFNKIT
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Heterogeneity

• Lymphocyte associated receptor of death
• LARD
• WSL-LR WSL-S1 WSL-S2 proteins
• WSL-1 protein precursor
• Apoptosis-mediating receptor DR3
• Apoptosis-mediating receptor TRAMP
• Death Domain receptor 3
• WSL protein
• apoptosis inducing receptor AIR
• APO-3

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• Data resources have been built introspectively
  for human researchers
• Information is machine readable not machine
  understandable
• CONTROLLED VOCABULARIES ONTOLOGIES

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Shared data-gt shared meaning
Service provider
Service provider
Service provider
Service provider
Service provider
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Complexity

• Multiple views
• Interrelated
• Intra and inter cell interactions and
  bio-processes

"Courtesy U.S. Department of Energy Genomes to
Life program (proposed) DOEGenomesToLife.org."
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Instability Quality

• Exploring the unknown
• At least 5 definitions of a gene
• The sequence is a model
• Other models are work in progress
• Names unstable
• Data unstable
• Models unstable

• the problem in the field is not a lack of good
  integrating software, Smith says. The packages
  usually end up leading back to public databases.
  "The problem is the databases are God-awful," he
  told BioMedNet. If the data is still
  fundamentally flawed, then better algorithms add
  little.
• Temple Smith, director of the Molecular
  Engineering Research Center at Boston University,

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Curation
SWISS-PROT
MEDLINE papers
annotation
PRINTS
BLOCKS
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Infrastructure Integration

• Technologies
• CORBA and the OMA
• Java and JavaBeans
• Data mining
• Algorithm development
• Knowledge discovery
• Knowledge representation
• Visualisation
• Query tools and services
• Database replication
• OO technology
• OO databases
• Networks and security
• Data cleaning validation

Structural Genomics
SNPs
Sequence Data
Expression Data
Gene Analysis
Mutation/Variation Pattern Discovery Gene
Prediction Splice Sites Promoters EST
Differential Temporal In situ
Functional Genomics
Gene Identification
Gene Networks
Proteomics
Gene Annotation and Function Regulation of
Metabolism Biochemical Pathways Signal
Transduction
Metabolomics
CORBA / Java / BSA / SRS
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Bioinformatics Analysis

• Different algorithms
• BLAST, FASTA, pSW
• Different implementations
• WU-BLAST, NCBI-BLAST
• Different service providers
• NCBI, EBI, DDBJ

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In silico experimentation
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In silico experimentation
myProteins
BLAST
Swiss-Prot
BLAST
PIR
Go-Blast visualisation
BLAST
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In silico experimentation
myProteins
BLAST
Interpro
Swiss-Prot
BLAST
PIR
Go-Blast visualisation
BLAST
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In silico experimentation
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In silico experimentation
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In silico experimentation
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In silico experimentation

• Discovery, interoperation, fusion, sharing of
  data, knowledge and workflows
• Explicit management of workflows
• information processes best practice
• Improving quality of experiments data
• provenance propagating change
• Scientific discovery is personal global
• personalisation collaborative working
• Security, ownership -gt valuable assets

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myGrid

• Personalised
• extensible environments for
• data-intensive
• in silico experiments in biology
• http//www.mygrid.org.uk

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myGrid

• UK e-Science Grid programme pilot (EPSRC)
• Generic middleware
• Bioinformatics Genomics setting
• 1st October 2001 -- 31st March 2005
• (36 months funded in 42 execution period)
• 16 full-time researchers/developers

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myGrid Partners
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A Desiderata (cf. Grid)
A p p l i c a t i o n s

• Software development toolkits
• Standard protocols, services APIs
• A modular bag of technologies
• Enable incremental development of grid-enabled
  tools and applications
• Reference implementations
• Learn through deployment and applications
• Open source

Diverse global services
Core services
Local OS
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Approach
myGrid Stack
Personalisation
Metadata
Agent-based Interoperation layer
I.E
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myGrid Outcomes

• e-Scientists
• Environment built on toolkits for service access,
  personalisation community
• Gene function expression analysis using S.
  cerevisiae
• Annotation workbench for the PRINTS pattern
  database
• Developers
• Protocols and service descriptions
• myGrid-in-a-Box developers kit
• Re-purposing DAS, AppLab and OpenBSA
• Integrating ISYS GlaxoSmithKline platforms

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myGrid tech outcomes

• Services, service descriptions (ontologies),
  message protocols APIs
• Database access from the Grid
• Process enactment on the Grid
• Personalisation services
• Provenance services
• Metadata services DAMLOIL, RDF(S)
• Laying the foundations for Agent Services

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Converging technologies
Grid Computing
Globus, Sun Grid Engine, Condor, DS (Jini, Corba)
Web Service Semantic Web Technologies
Agents
SOAP, WSDL, UDDI, WSIL, DAMLOIL, OWL, RDF(S),
WSFL
ACL, methodology
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myGrid Services
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Standards and Activities
Open Source Open Bio Foundation BioJava, BioPerl
Consortium Expertise View propagation,
reasoning, workflow
(DeFacto) Standards OMG LSR, I3C, MGED, Gene
Ontology
Semantic Web RDF, RDFS, DAMLOIL
Bioinformatics integration platforms DAS,
OpenBSA, ISYS, OpenMMS, Kleisli, Ensembl, AppLab,
SRS, BioNavigator, DiscoveryLink, K1 TAMBIS.
MOBY
Distributed Computing Environments CORBA, RMI,
JavaOne
Web Services XML, SOAP, WSDL, UDDI
GRID Globus/SRB/Condor/Sun Grid Engine
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Other BioGrids

• BioOpera
• North Carolina BioGrid
• Novartis Grid
• Scientific Annotation Middleware project
• Entropia AIDS modelling Grid .
• DiscoveryNet
• Proteomics analysis
• Protein structure prediction
• Biodiversity
• CLEF Clinical records

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myGrid Summary

• myGrid aims to develop infrastructure middleware
  for an e-Biologists workbench
• The setting is bioinformatics but the results are
  intended to be generally applicable to e-Science
• A mix of standard, vanguard and bleed edge
  technologies, advanced development and (some)
  research
• Academic commercial partnership
• myGrid project is timely reflects a community
  desire to collaborate, or die

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Take home reprise

• Complexity Diversity - Size isnt everything.
• Computation is important but information and
  knowledge services dominate.
• Integration, curation, annotation, fusion
• Automating support for integration and fusion
  means moving from
• human interaction to machine interaction.
• machine readable to machineunderstandable.
• Metadata using ontologies for finding, managing
  controlling services content

46
Acknowledgements

• Colleagues on myGrid
• Robert Stevens
• Norman Paton
• Alan Robinson at EMBL-EBI
• I3C Interoperable Informatics Infrastructure
  Consortium http//www.i3c.org

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URLs

• EBI
• http//www.ebi.ac.uk/
• LSR
• http//www.omg.org/homepages/lsr/
• Open-Bio
• http//www.open-bio.org/
• I3C
• http//www.i3c.org/

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• "Molecular biologists appear to have eyes for
  data that are bigger than their stomachs. As
  genomes near completion, as DNA arrays on chips
  begin to reveal patterns of gene sequences and
  expressions, as researchers embark on
  characterising all known proteins, the
  anticipated flood of data vastly exceeds in scale
  anything biologists have been used to."
• (Editorial Nature, June 10, 1999)

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• Presented over the AccessGrid to the CSC Finnish
  IT Centre for Science Grid Seminar
• Otaniemi, Espoo, Finland
• 6th March 2002
• http//www.csc.fi/suomi/tapahtumat/GridSeminar/