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Data standards for systems biology

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    Data standards for systems biology Data standards for systems biology Presentation Transcript

    • Data Standards for Systems Biology
      • Neil Swainston
      • Manchester Centre for Integrative Systems Biology
      • n [email_address]
    • Introduction
      • Experimental standards
        • Proteomics
        • Metabolomics
        • Enzyme kinetics
      • Modelling standards
        • Models
        • Simulations
        • Results
    • Why do we need standards?
      • Aids researchers by facilitating management of experimental data
      • Facilitates open-source soft ware development and interoperability
      • Allows data to be shared
        • Increasingly becoming a requirement for journal submissions
    • When are standards developed?
      • Standards generally are generated organically
      • Not for pioneers
      • When an experimental technique becomes established
      • Need for a standard becomes obvious
    • Who develops standards?
      • Usually two or more academ ic groups
        • Commercial providers often less enthusiastic
      • Often formed by a Working Group
        • Proteome Standards Initiative
        • Metabolomics Standards Initiative
      • “ Minimum information req uired ” specification provided
      • Followed by data schema , XML standard
    • MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (K M , K cat ) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web service Web service Web service MeMo-RK Web service
    • Proteomics
      • We wish to store:
        • Raw experimental mass spectrometry data
        • Protein / peptide identifications
        • Protein / peptide quantitations
        • Metadata (instrument, search algorithm, user, etc.)
    • Mass spectrometry data
      • How do we represent the following?
    • Mass spectrometry data
      • The simple approach:
    • Mass spectrometry data
      • The simple approach does provide a list of masses and intensities , but…
        • What instrument was used?
        • Who ran the instrument?
        • What sample was used?
        • … etc.
      • The simple approach lacks metadata
      • Many simple approaches (formats) exist
    • Mass spectrometry data
      • The less simple approach: mzData
        • Developed by the Proteome Standards Initiative, 2005
        • Put together by Working Group of academics and commercial parties
        • Regular meetings, both real and virtual
      • Goal: unify the existing “ simple ” formats into one
        • Support “ tagging ” with metadata
    • mzData
      • http://www.psidev.info/index.php?q=node/80#mzdata
      • XML format, includes…
        • Peak lists (mz / intensities)
        • Experimental protocols
        • Admin (Who? When?)
        • Instrument details
        • etc.
    • Controlled vocabularies
      • Use of free text is “ dangerous ”
        • Non-standard , ambiguous terms
        • Difficult to match / compare
      • Controlled vocabularies
        • Collection of standardised terms
        • Organised into vocabularies or ontologies
        • Ontologies contain controlled terms and relationships between them (predicates)
    • Controlled vocabularies
      • Ontology Lookup Service, EBI
    • mzData
    • Proteomics data
      • Proteomics data is not solely mass spectrometry data
        • Sample preparation protocol?
        • Peptide / protein identifications ?
        • Post-translational modifications
        • Identification scores ?
      • To support this, an extension is required
        • Extension based on defined set of “ minimum requirements ”
        • MIAPE
    • MIAPE
    • PRIDE
      • Pr oteomics ide ntifications database
        • Both a format and a database
        • Centralised, standards compliant, open source, public data repository for proteomics data
        • Query, submit and retrieve proteomics data in standardized XML formats
        • Public version housed at the EBI
        • http://www.ebi.ac.uk/pride/
    • PRIDE
      • Peptide / protein identifications
    • PRIDE Converter
      • User interface
      • Usable by biologists
      • Interfaces with Ontology Lookup Service
      • Developed by EBI
      • Automatic upload to PRIDE database
    • PRIDE database
    • Future directions
      • PRIDE does NOT hold:
        • Protein and peptide quantitations
      • New approaches being developed
        • mzML – mass spectrometry format, enhancement of mzData, including support for richer datase ts
        • mzIdentML – storage of protein and peptide identifications
        • mzQuantML – storage of protein and peptides quantitations
    • Metabolomics
      • We wish to store:
        • Raw experimental mass spectrometry (and NMR) data
        • Metabolite identifications
        • Metabolite quantitations
        • Metadata (instrument, search algorithm, user, etc.)
    • Metabolomics
      • Data standard does NOT currently exist
        • C ore I nformation for M etabolomics R eporting
      • Metabolites Standard Initiative (MSI)
        • http://msi-workgroups.sourceforge.net/
      • MetaboLights being developed at EBI
        • Not many details as yet
      • In the mean time…
        • MCISB has developed its own repository
    • MeMo
      • Me tabolomics Mo del database
      • Designed initially for metabolomics data
      • SQL / XML hybrid approach
      • Holds:
        • Experimental meta-data (submitter, lab, date)
        • Sample meta-data (including biological source)
        • Instrumentation meta-data
        • Mass spectra
        • Metabolite identifications
    • MeMo
    •  
    • MeMo web interface
    • Enzyme kinetics
      • How fast does a given reaction occur?
      • Enzyme
        • A B
      • Determination of kinetic constants which define the kinetics of the reaction
      • Experimental approach: perform kinetic assays
    • Enzyme kinetics
      • Many approaches:
        • Absorbance
        • Fluorescence
        • others
      • Currently concentrating on absorbance assays on BMG NOVOstar instrument
      • Requirement: determination of K M and k cat for a given reaction under particular conditions (pH and temperature)
    • Enzyme kinetics: Michaelis-Menten
      • Traditionally, for each assay, initial rate, v is determined
    • Enzyme kinetics: Michaelis-Menten
      • Performing this at various substrate concentrations allows K M and V max to be determined:
    • STRENDA guidelines
      • St andards for R eporting E nzymology Da ta
        • http://www.beilstein-institut.de/en/projects/strenda/
      • Specifies…
        • Reactants / products
        • Enzyme ( wild-type, modified, purification, expressed in
        • Experimental conditions (pH, temperature, buffer)
        • Instrument , experiment type
        • Submitter (contact details)
    • SABIO-RK
      • http ://sabio.villa-bosch.de /
      • Comprehensive collection of enzyme kinetic constants
      • Adheres to STRENDA recommendation
      • Harvested from literature
      • Searchable web interface
    • SABIO-RK
    • SABIO-RK
    • SABIO-RK
    • BRENDA
      • http://www.brenda-enzymes.org /
      • Even more comprehensive
      • Slightly less well-curated
      • Again, searchable web interface
    • BRENDA
    • Other experimental standards
      • MIBBI: Minimum Information for Biological and Biomedical Investigations
        • http://mibbi.org/
      • Over thirty recommendations for a range of experimental techniques
    • Modelling standards
    • MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (K M , K cat ) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web service Web service Web service MeMo-RK Web service
    • MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (K M , K cat ) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web service Web service Web service MeMo-RK Web service
    • Modelling
      • What is a model?
      • “ An analytic or computational model proposes specific testable hypotheses about a biological system ”
      • Mathematical / computational representation of a biological system
      • May allows computational simulations of the system
    • Pathway databases
      • Building a model often starts with a topological description of a pathway or pathways
      • What reacts with what ?
      • A number of existing data resources
        • Biochemical knowledge, curated from literature
    • KEGG
    • KEGG Metabolite Enzyme Reaction
    • MetaCyc
    • Reactome
    • Simulation tools
      • The systems biology community has developed a strong software infrastructure
      • Many tools exist, including simulators
        • Several hundred
      • How do we link pathway databases to these simulators ?
      • A standard: SBML
        • Systems Biology Markup Language
        • Recently celebrated its 10 th birthday
    • SBML
      • XML markup language describing models
      • Contains concepts such as…
        • compartments
        • species (metabolites, enzymes, RNA, etc.)
        • reactions
      • Similar to pathway databases
        • KEGG2SBML tool exists for converting KEGG pathway maps to SBML files
    • Mathematical SBML
      • Also contains concepts allowing simulations
        • Many of these driven by experimental work
      • Specification of metabolite and enzyme concentrations
      • Specification of kinetic laws and kinetic parameters
      • Parameterised mod el = pathways + experimental data
    • SBML
    • SBML data resources
      • Biomodels.net
        • http://www.ebi.ac.uk/biomodels-main /
        • Curated collection of biochemical models at EBI
      • JWS Online
        • http://jjj.mib.ac.uk /
        • Also curated
        • BUT also includes an online simulator
        • You ’ ll learn more next month…
    • SBML tools
      • Hundreds of ‘ em (205)
        • http ://sbml.org/ SBML_Software_Guide
      • Different goals
        • Whole cell / single pathway
        • Deterministic / stochastic simulators
        • Different platforms / programming languages
      • Matrix exists, describing capabilities of each tool
        • http://sbml.org/SBML_Software_Guide/ SBML_Software_Matrix
    • Making SBML models: CellDesigner
    • Other model representations
      • CellML
        • http ://www.cellml.org /
        • Larger scale modelling
        • Inter-cellular, used in whole organ modelling
      • BioPAX
        • http://www.biopax.org /
        • Similar goals to SBML
      • Overlap between “ competing ” representations is being reduced
        • Regular “ COMBINE ” meetings
    • MIRIAM
      • Minimum Information Required in the Annotation of Models
        • http ://www.ebi.ac.uk/miriam /
      • Set of guidelines describing how to make models reusable
        • Specify model creator contact details
        • Ensure consistent annotation of terms with database resources
        • e.g. use UniProt identifiers for unambigous identification of enzymes
    • SBML visualisation: SBGN
      • Until recently, no standardised way of viewing models
        • Systems Biology Graphical Notation
        • Attempts to generate standard “ wiring-diagram ” for biological representations
    • Model simulation
    • Model simulation
      • Many simulators exist
      • How do we tell a simulator what to simulate?
        • Simulation Experiment Description Markup Language (SED-ML)
      • Contains concepts…
        • Model (what to run the simulation on)
        • Simulation (define what to simulate, duration, step-size)
        • Data generation (post-processing normalisation)
        • Output (2D plot, 3D plot)
    • Simulation results: SBRML
      • Simulation results are data too, and are represented by SBRML
        • Systems Biology Results Markup Language
        • Developed by Joseph Dada, et al. (Manchester)
      • Structured format for representing simulation results
      • Dada JO, et al . SBRML: a markup language for associating systems biology data with models. Bioinformatics 2010, 26 , 932-938.
    • SBRML
    • Conclusion
      • Data standards greatly facilitate computational systems biology
      • Standards exist (and are being continually developed) for both experimental and modelling data
      • Provides a framework for data sharing and open-source software tool development
    • Data Standards for Systems Biology
      • Neil Swainston
      • Manchester Centre for Integrative Systems Biology
      • n [email_address]