Data standards for systems biology

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Data standards for systems biology

  1. 1. Data Standards for Systems Biology Neil Swainston Manchester Centre for Integrative Systems Biology neil.swainston@manchester.ac.uk
  2. 2. Introduction • Experimental standards • Proteomics • Metabolomics • Enzyme kinetics • Modelling standards • Models • Simulations • Results
  3. 3. Why do we need standards? • Aids researchers by facilitating management of experimental data • Facilitates open-source software development and interoperability • Allows data to be shared • Increasingly becoming a requirement for journal submissions
  4. 4. When are standards developed? • Standards generally are generated organically • Not for pioneers • When an experimental technique becomes established • Need for a standard becomes obvious
  5. 5. Who develops standards? • Usually two or more academic groups • Commercial providers often less enthusiastic • Often formed by a Working Group • Proteome Standards Initiative • Metabolomics Standards Initiative • “Minimum information required” specification provided • Followed by data schema, XML standard
  6. 6. MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (KM, Kcat) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web serviceWeb serviceWeb service MeMo-RK Web service
  7. 7. Proteomics • We wish to store: • Raw experimental mass spectrometry data • Protein / peptide identifications • Protein / peptide quantitations • Metadata (instrument, search algorithm, user, etc.)
  8. 8. Mass spectrometry data • How do we represent the following?
  9. 9. Mass spectrometry data • The simple approach:
  10. 10. 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
  11. 11. 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
  12. 12. 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.
  13. 13. 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)
  14. 14. Controlled vocabularies • Ontology Lookup Service, EBI
  15. 15. mzData
  16. 16. 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
  17. 17. MIAPE
  18. 18. PRIDE • Proteomics identifications 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/
  19. 19. PRIDE • Peptide / protein identifications
  20. 20. PRIDE Converter • User interface • Usable by biologists • Interfaces with Ontology Lookup Service • Developed by EBI • Automatic upload to PRIDE database
  21. 21. PRIDE database
  22. 22. 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 datasets • mzIdentML – storage of protein and peptide identifications • mzQuantML – storage of protein and peptides quantitations
  23. 23. Metabolomics • We wish to store: • Raw experimental mass spectrometry (and NMR) data • Metabolite identifications • Metabolite quantitations • Metadata (instrument, search algorithm, user, etc.)
  24. 24. Metabolomics • Data standard does NOT currently exist • Core Information for Metabolomics Reporting • 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
  25. 25. MeMo • Metabolomics Model 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
  26. 26. MeMo
  27. 27. MeMo web interface
  28. 28. 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
  29. 29. Enzyme kinetics • Many approaches: – Absorbance – Fluorescence – others • Currently concentrating on absorbance assays on BMG NOVOstar instrument • Requirement: determination of KM and kcat for a given reaction under particular conditions (pH and temperature)
  30. 30. Enzyme kinetics: Michaelis-Menten • Traditionally, for each assay, initial rate, v is determined
  31. 31. Enzyme kinetics: Michaelis-Menten • Performing this at various substrate concentrations allows KM and Vmax to be determined:
  32. 32. STRENDA guidelines • Standards for Reporting Enzymology Data • 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)
  33. 33. SABIO-RK • http://sabio.villa-bosch.de/ • Comprehensive collection of enzyme kinetic constants • Adheres to STRENDA recommendation • Harvested from literature • Searchable web interface
  34. 34. SABIO-RK
  35. 35. SABIO-RK
  36. 36. SABIO-RK
  37. 37. BRENDA • http://www.brenda-enzymes.org/ • Even more comprehensive • Slightly less well-curated • Again, searchable web interface
  38. 38. BRENDA
  39. 39. Other experimental standards • MIBBI: Minimum Information for Biological and Biomedical Investigations • http://mibbi.org/ • Over thirty recommendations for a range of experimental techniques
  40. 40. Modelling standards
  41. 41. MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (KM, Kcat) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web serviceWeb serviceWeb service MeMo-RK Web service
  42. 42. MCISB project overview Enzyme kinetics Quantitative metabolomics Quantitative proteomics Model Parameters (KM, Kcat) Variables (metabolite, protein concentrations) PRIDE XML MeMo SABIO-RK Web serviceWeb serviceWeb service MeMo-RK Web service
  43. 43. 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
  44. 44. 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
  45. 45. KEGG
  46. 46. KEGG Metabolite Enzyme Reaction
  47. 47. MetaCyc
  48. 48. Reactome
  49. 49. 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 10th birthday
  50. 50. 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
  51. 51. 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 model = pathways + experimental data
  52. 52. SBML
  53. 53. 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…
  54. 54. 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
  55. 55. Making SBML models: CellDesigner
  56. 56. 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
  57. 57. 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
  58. 58. SBML visualisation: SBGN • Until recently, no standardised way of viewing models • Systems Biology Graphical Notation • Attempts to generate standard “wiring-diagram” for biological representations
  59. 59. Model simulation
  60. 60. 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)
  61. 61. 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.
  62. 62. SBRML
  63. 63. 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
  64. 64. Data Standards for Systems Biology Neil Swainston Manchester Centre for Integrative Systems Biology neil.swainston@manchester.ac.uk

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