Standards and gene expression data

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Standards and gene expression data from data
archiving to extracting biological knowledge
Helen Parkinson, PhD Production
Coordinator European Bioinformatics Institute
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Talk content
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Data sharing
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Standards Landscape
Nature Reviews Genetics, Vol 7, p.593-605 (August
2006)
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MIAME Minimal Information about a microarray
experiment
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So Has MIAME been successful?
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ArrayExpress?
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ArrayExpress history
2003 100 Expts
2004 TIGR Export 420 Expts
2005 Re-funded SMD Export 1200 Expts
2006 New UI 1600 Expts
2002 12 Expts
2007 GEO Affy Data Import Phase 1 gt2607 Expts
2008 6631 Expts
2001
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ArrayExpress Overview
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Getting to a summary level data Atlas
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Our Use Cases

• Query support (e.g, query for 'cancer' and get
  also 'leukemia')
• Over-representation analysis in groups of samples
  (analogous to the use of GO terms in
  over-representation analysis in groups of genes)
• Ontology visualisation e.g., presenting an
  ontology tree to the user of what is in the
  database
• Data integration by ontology terms e.g., we
  assume that 'kidney' in independent studies
  roughly means the same, so we can count how many
  kidney samples we have in the database
• Intelligent template generation for different
  experiment types in submission or data
  presentation
• Summary level data

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Oh the complexity!
Publication
External links
Normalisation
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Application Ontology Status Quo

• Text mining at data acquisition
• Tuned for queries, structured for use in
  ArrayExpress GUI
• Multi-species aspect

DW
AE
06.04.2014
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Semantic Roadmap

• Position of the ArrayExpress Experimental Factor
  Ontology in the bigger picture

• Key is orthogonal coverage, reuse of existing
  resources and shared frameworks

Chemical Entities of Biological Interest (ChEBI)
Relation Ontology
Cell Type Ontology
Various Species Anatomy Ontologies
Anatomy Reference Ontology
Disease Ontology
AE Ontology
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What lies beneath?
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Where does the data come from
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What is curation?
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2007 Affymetrix Data landscape
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Data exchange or the failure to federate

• We need all the data in house to re-process it
• We do not have a data exchange agreement with GEO
• SOFT vs. MAGE-ML/MAGE-TAB
• No ontology usage
• Some free text annotation, little process
  annotation
• Mass data acquisition
• 80 solution (or less)
• Employing text mining
• Data reprocessing
• Cost effective, eliminates user support
• Using spreadsheets (not XML)
• We could almost eliminate the database if we can
  index the files

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2008 Data Landscape
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Flexible Data Access Models

• GUIs biologists
• Hyperlinks
• FTP bioinformaticians
• Web services workflows
• XML data dumps
• Spreadsheets
• Direct SQL access (not for ArrayExpress)
• Schema and code if you want it
• Geek for a week

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Lessons learned

• Complex architecture means a lot of SW
  engineering
• Biologists like excel, Bioinformaticians like
  tab-delimited files
• Spreadsheets scale, easy to check, harder to
  parse
• Generic systems will be future proof
• Legacy format converters are needed
• You dont need to keep everything
• Text based queries most common
• Text mining very useful
• Scaling problems are hard to fix
• Bleeding edge technologies should be used
  sparingly
• Federation doesnt really work for the goals we
  have
• Archiving alone does not add value
• Training is important and expensive

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Useful tools for life sciences data management

• Excel
• Whatizit text mining software from EBI
• Our spreadsheet builder, checkers and format
  parsers tab2mage.sf.net
• OBO foundry ontologies esp OBI, CTO, Disease
  Ontology
• Taverna for building workflows
• BASE open source microarray data management
  tool
• BioMart data warehouse for biological data
  www.biomart.org

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Acknowledgements

• ArrayExpress Production Team
• Tomasz Adamusiak, Tony Burdett, Anna Farne, Ele
  Holloway, James Malone, Margus Lukk, Helen
  Parkinson, Tim Rayner, Eleanor Williams, Holly
  Zheng
• Ugis Sarkans ArrayExpress Development Team Leader
• Misha Kapushesky Main Atlas Developer
• Gabriella Rustici Training officer
• Alvis Brazma Group Leader
• Uniprot and Ensembl teams
• Funders EC (FELICS, EMERALD, DIAMONDS, GEN2PHEN,
  MUGEN), NIH-NHGRI, EMBL
• The submitters and microarray collaborators
• GEO especially Tanya Barrett