The Scientific

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The Scientific Regulatory Drivers for Omic
Data Standards What Wider Community Benefits can
be Derived? Dr Jason Snape AstraZeneca Global SHE
eGenomes, Cambridge, September 2005
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NERC Genomic Research Effort

• Directed Programmes- Environmental Genomics-
  Post-Genomics Proteomics
• Knowledge Transfer- Connect B- NERC/ EA
  Fellowship
• Responsive Mode
• Advanced Fellows- includes Environmental
  Metabolomics

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Environmental Genomics

• Identification of important phenotypes
  genotypes- ecologically driven- bottom-up
  fitness approach (genes to populations)
• Determine the ecological significance of
  molecular variation- natural variation (spatial/
  temporal)- consequence of mans activities
  (pollution/ climate change)
• Determine the impact the environment has on
  phenotype- map to genotype (phenotypic
  anchoring)- basis and theory of adaptive
  evolution
• Genome architecture- quantitative traits-
  comparative mapping (model vs. wildlife sentinel)
• Data management Analysis (min. 10 of budget)

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Post-Genomics Proteomics

• Integrating post-genomics and proteomics to
  studyhost-parasite interactions in aquatic and
  terrestrial hosts
• What makes species, populations and taxa
  resistant or vulnerable to environmental change
  and environmentalimpact? - Plant responses to
  abiotic stress at range margins- Molecular
  ecotoxicology in aquatic vertebrate populations
• Nutrient flux and biogeochemistry
• Environmental regulation of the proteome
• Data management Analysis (min. 10 of budget)

Systems-level biology funded
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NERC Directed Programmes
CCGCC
BBSRC MRC EPSRC ESRC
NERC has a genomic data policy
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Data Standards

• Data standards creep
• MIAME for everything
• Minimum perceived as too detailed/ time consuming
• Detracts from experiment work
• Ms not spent on science
• What value do they add?

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Scientific Drivers

• Multivariate post-genomic analyses have diverse
• applications
• Pathway discovery/ functionality
• Chemicals management (e.g. preclinical
  drugsafety assessment, toxicology
  ecotoxicology)
• Disease diagnosis/ susceptibility
• Environmental science

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Scientific Drivers
Wide variety of analytical techniques
(transcripts,proteins metabolites) and
diversity of data typesdictates core Standards
to facilitate communication/ collaboration
betweendifferent fields of activity and fulfill
the needs ofjournal editors regulatory
agencies

• More about accessibility than accountability
• Minimise duplication
• Foster collaborative science
• Realise potential for comparative genomics

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Approaches to Omic Science
From Kell (2003)
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Approaches to Omic Science
Increase in - assumption free- hypothesis
generating- fishing
proposals
For hypothesis-driven science the PI should
knowwhat metadata should be collected For
hypothesis-generating science, should the
PI collect all possible metadata?
How do we collect the data in an added value
format?
Standards need to be community driven
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Regulatory Drivers
RCUK, including NERC, must demonstrate effective
Knowledge Transfer One aspect of KT is science
into policy Chemicals management as an
example- toxicology - ecotoxicology
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Toxicological/ Ecotoxicological Drivers

• EU REACH Directive- Registration, Evaluation
  Authorisation of Chemicals- 30,000 chemicals
  (little or no data/ pre-legislation)- 11 years-
  gt7.5B
• Animal welfare concerns- in vitro possibilities

Industry regulatory agencies are looking to
omics- cheap effective screen to assist
prioritisation
Industry regulators work in a standards
environment- this ethos is being pushed hard
(e.g. SMRS)
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OECD 210 Fish Early Life Stage
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OECD 210 Fish Early Life Stage

• MIAME/ Tox has been developed to build
  confidencein toxicogenomics
• Platform issues still not resolved validation
  problems

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Toxicological/ Ecotoxicological Drivers
Cheap effective screen to assist prioritisation-
target chronic testing- opp. for pathway
discovery pathway biomarkers - no false
positives- approaches must be validated (Corvi,
EHP, 2005)- platform independent- outcomes must
be associated with validity criteria-
transparent QA/ QC- reproducible (withstand ring
testing - interlab)
All this against the background of poor
arrayreproducibility
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Community Benefits

• Omic science is still data rich knowledge poor
• Open community-driven standards are required to
• instil confidence (hype is over, it is delivery
  time)
• address sceptics
• facilitate/ maximise the value of the data
• improve knowledge generation
• assist cross-species extrapolation to realise
  comparative genomics

Lessons from array-based work must be applied to
proteomics,metabolomics metagenomics
Knowledge
Data
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Environmental Genomics
The Challenge
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Environmental Genomics
Earth System Science
Systems Biology
NEBC versus other NERC DCs
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Regulatory Drivers