A Systematic Approach to Modelling, Capturing and Disseminating Proteomics Experimental Data

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Promoting Coherent Minimum Reporting Guidelines
for Biological Biomedical Investigations The
MIBBI Project Chris Taylor, EMBL-EBI NEBC
chris.taylor_at_ebi.ac.uk MIBBI www.mibbi.org
HUPO Proteomics Standards Initiative
psidev.sf.net Research Information Network
www.rin.ac.uk
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On standards bodies

• What defines a standards-generating body?
• A beer and an airline (Zappa)
• Formats, reporting guidelines, controlled
  vocabularies
• Regular open attendance meetings, discussion
  lists, etc.
• e.g., MGED (transcriptomics), PSI (proteomics),
  GSC (genomics)
• Hugely dependent on their respective communities
• Requirements gathering (What are we doing and
  why?)
• Development (By the people, for the people)
• Testing (No it isnt finished, but yes Id like
  you to use it)
• Uptake by stakeholders
• Publishers, funders, vendors, tool/database
  developers
• The user community (capture, store, search,
  analyse)

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Modelling the biosciences
MS
MS
Gels
NMR
Arrays
Columns
FTIR
Scanning
Arrays Scanning
Columns
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Modelling the biosciences (slightly differently)
Investigation Medical syndrome, environmental effect, etc.
Study Toxicology, environmental science, etc.
Assay Omics and miscellaneous techniques
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Multiple all that by three (kinds of standard)
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What biologists need
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Well-oiled cogs meshing perfectly (would be nice)

• How well are things working?
• Cue the Tower of Babel analogy
• Situation is improving with respect to standards
• But few tools, fewer carrots (though some sticks)
• Why do we care about that..?
• Data exchange
• Comprehensibility of work
• Scope for reuse (parallel or orthogonal)

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Rise of the Metaprojects

• Investigation / Study / Assay (ISA)
  Infrastructure
• http//isatab.sourceforge.net/
• Ontology of Biomedical Investigations (OBI)
• http//obi.sourceforge.net/
• Functional Genomics Experiment (FuGE)
• http//fuge.sourceforge.net/

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Reporting guidelines a case in point

• MIAME, MIAPE, MIAPA, MIACA, MIARE, MIFACE,
  MISFISHIE, MIGS, MIMIx, MIQAS, MIRIAM, (MIAFGE,
  MIAO), My Goodness
• MI checklists usually developed independently,
  by groups working within particular biological or
  technological domains
• Difficult to obtain an overview of the full range
  of checklists
• Tracking the evolution of single checklists is
  non-trivial
• Checklists are inevitably partially redundant one
  against another
• Where they overlap arbitrary decisions on wording
  and sub structuring make integration difficult
• Significant difficulties for those who routinely
  combine information from multiple biological
  domains and technology platforms
• Example An investigation looking at the impact
  of toxins on a sentinel species using proteomics
  (eco-toxico-proteomics)
• What reporting standard(s) should they be using?

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The MIBBI Project (mibbi.org)

• International collaboration between communities
  developing Minimum Information (MI) checklists
• Two distinct goals (Portal and Foundry)
• Raise awareness of various minimum reporting
  specifications
• Promote gradual integration of checklists
• Lots of enthusiasm (drafters, users, funders,
  journals)
• 31 projects committed (to the portal) to date,
  including
• MIGS, MINSEQE MINIMESS (genomics, sequencing)
• MIAME (µarrays), MIAPE (proteomics), CIMR
  (metabolomics)
• MIGen MIQAS (genotyping), MIARE (RNAi),
  MISFISHIE (in situ)

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Nature Biotechnol 26(8), 889896
(2008) http//dx.doi.org/10.1038/nbt.1411
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The MIBBI Project (www.mibbi.org)
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The MIBBI Project (www.mibbi.org)
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The MIBBI Project (www.mibbi.org)
Interaction graph for projects (line thickness
colour saturation show similarity)
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The MIBBI Project (www.mibbi.org)
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MICheckout Supporting Users
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The objections to fuller reporting

• Why should I dedicate resources to providing data
  to others?
• Pro bono arguments have no impact
• Sticks from funders and publishers get the bare
  minimum
• This is just a make work scheme for
  bioinformaticians
• Bioinformaticians get a buzz out of having big
  databases
• Bioinformaticians benefitting from others work
• I dont trust anyone elses data Id rather
  repeat work
• Problems of quality, which are justified to an
  extent
• But what of people lacking resource for this, or
  people who want to refer to proteomics data but
  dont do proteomics
• How on earth am I supposed to do this anyway..?
• Perception that there is no money to pay for this
• No mature free tools Excel sheets are no good
  for HT
• Worries about vendor support, legacy systems
  (business models)

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Credit where credits due

• Data sharing is more or less a given now, and
  tools are emerging
• Lots of sticks, but they only get the bare
  minimum
• How to get the best out of data generators?
• Only meaningful credit will work
• Need central registries of data sets that can
  record reuse
• Well-presented, detailed papers get cited more
  frequently
• The same principle should apply to data sets
• So, OpenIDs for people, DOIs for data?
• Side-benefits, challenges
• Would also clear up problems around paper
  authorship
• Would enable other kinds of credit (training,
  curation, etc.)
• May have to be self-policing researchers own
  their credit portfolio (though an enforcement
  body would also be useful)
• Problem of micro data sets and legacy data