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FGE-OM: Functional Genomics Experiment - Object Model Andy Jones Department of Computing Science University of Glasgow.

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Presentation on theme: "FGE-OM: Functional Genomics Experiment - Object Model Andy Jones Department of Computing Science University of Glasgow."— Presentation transcript:

1 FGE-OM: Functional Genomics Experiment - Object Model Andy Jones Department of Computing Science University of Glasgow

2 Overview Introduction to proteomics Motivation for shared standards FGE-OM Database implementation - RAPAD Current biological projects

3 Proteomics Workflow Sample Origin Protein Solubilisation Image Analysis Database Search ID Vol X Y 1 454 23 24 2 222 28 87 3 12 20 12 4 662 262 101 5 49 222 90 6 113 485 10 7 119 98 987 Multiple Gel Analysis ID Vol X Y 1 654 23 24 2 25 28 87 3 187 21 16 4 672 262 111 5 54 222 90 6 113 487 10 7 125 98 987 MALDIMS/MS Protein identification 2D-PAGE Statistical Analysis Mass Spectrometry

4 Motivation for Shared Standards Data from large studies using multiple techniques can be compared more easily Proteomics standardisation can learn from past efforts of MGED Shared aspects of microarrays & proteomics: –Overview of experiment –Sample origin –Experimental protocols (similarity between RNA extraction & protein solubilisation) –Higher level analysis across multiple samples –RNA fluorescence signal, similar to protein volume on a 2-D gel

5 FGE-OM Components common to all functional genomics experiments Microarray specific components Classes modelling proteomics technologies Top-level of the Object Model Namespaces BioOMArrayOMProteomicsOM Functional Genomics Experiment - Object Model MAGE-OM derived PEDRo and Gla-PSI derived

6 A database for microarrays and proteomics Based on RAD microarray database at Penn Additional tables to store proteomics Interface based on the RAD Study-Annotator RAD Study-Annotator: Manduchi et al. Bioinformatics 2003, (in press)

7 Proteomics Standards PEDRo - Proteomics Experiment Data Repository –Proposal for standard covering sample origin, protein separation and mass spec –Accepted by Proteomics Standards Initiative as a draft standard –Published in Nature Biotech 21:247-254 (2003)

8 Proteomics Standards Gla-PSI –Glasgow proposal for PSI More detailed coverage of: Image analysis Multiple analysis of 2D gels DIGE Statistical analysis Comparative and Functional Genomics 4:492-501 (2003)

9 Experiment Protocol Bio- Material Measure- ment BioAssay BioAssay Data BioEvent Description Bio- Sequence BQS Higher Level Analysis AuditAnd Security IdentifiableExtendable Describable Packages Classes Overview of BioOM packages BioAssay: removed Hybridization class into ArrayOM BioAssayData: removed BioDataCube and related classes into ArrayOM Other packages: unchanged from MAGE-OM

10 Array,ArrayDesign, DesignElement Describe layout of array QuantitiationType - microarray specific classes e.g. Signal But, standard statistical tests could be incorporated into BioOM in the future ArrayBioAssay contains only Hybridization class ArrayBioAssayData contains BioDataCube - data dimensions Not directly applicable to proteomics or other experiments Overview of ArrayOM packages Array Array BioAssay ArrayDesign Array BioAssayData Quantitation Type DesignElement

11 BioOM: BioAssayData vs ArrayOM:ArrayBioAssayData BioAssay Data BioAssay Dimension BioData Tuples BioData Values Measured BioAssay Data Relationships between classes are the same as MAGE-OM BioAssay Datum BioAssay Map BioData Cube Composite Sequence Dimension BioAssay Mapping Derived BioAssay Data Design Element Dimension Design Element Map Design Element Mapping Feature Dimension Quantitation Type Dimension Quantitation TypeMap Quantitation Type Mapping Reporter Dimension Transform- ation BioOM ArrayOM Only the most generic classes kept in BioOM Data model from MAGE does not fit proteomics Matching spots across gels is more complex

12 Protein Separation MassSpec Protocol Proteome BioAssay MassSpec Data ProteinRecordProteinData Overview of ProteomicsOM packages Packages derived from PEDRo and Gla-PSI Linked to classes in BioOM for adding generic descriptions and protocols Different design principles from MAGE-OM Classes have attributes that specify many of the datatypes to be captured

13 Gel2DColumn Physical GelSpot Fraction Separation techniquesSeparation products Source biomaterial BioMaterial BioAssay Treatment BioMaterial Measurement ProteomicsOM:ProteinSeparation package Separation techniques: subclass of BioAssayTreatment Separation products: subclass of BioMaterial Product of one separation technique can lead into another using BioMaterialMeasurement A generic protocol can be attached to BioAssayTreatment

14 ProteinSeparation Package

15 BioOM ProteomicsOM Legend GelImageAnalysis - analysis of 2-DE by specialist software Re-uses Image and ImageAcquisition from BioOM Linked by Physical- BioAssay BioAssay Treatment Physical BioAssay BioAssayImage Channel Image Acquisition GelImage Analysis Measured BioAssay Feature Extraction Measured BioAssay Data BioAssay Data targettreatment ProteomicsOM:ProteomeBioAssay package

16 Gel2D - 1 st, 2 nd dimension, stain protocols, operator, MW & pI range

17 Image Acquisition ImageChannel

18 GelImage Analysis

19 ProteomicsOM:ProteinData package GelImage Analysis Feature Extraction Identified Spot Physical GelSpot BioMaterial DIGESingle Spot BioData Tuples BioData Values Multiple Analysis Matched Spots Physical BioAssay BioAssay Data BioAssay Dimension SpotRatio IdentifiedSpot stores spot data e.g. volume Subclass of Physical GelSpot and BioMaterial for capturing further treatments DIGESingleSpot captures single channel BioAssayDimension captures spots matched across gels

20 ProteinData Package

21 Search capabilities over protein name, range of pI, mass or spot volume Clicking a spot loads protein data pages Identified Spot Protein

22 MassSpecProtocol and MassSpecData MassSpec Experiment PeakListPeak MassSpecProtocol Package MassSpecData Package BioOM ProteomicsOM Legend BioAssay Treatment PEDRo derived classes modelling MS protocol PEDRo derived classes modelling database searches MassSpecExperiment at top level BioAssayTreatment links to source of material and protocol (via BioEvent) Also links to specific classes for MS details e.g. ion source Data stored as a list of peaks Classes for capturing database searches from PEDRo BioMaterial Measurement

23 ProteinRecord package Location species modificationType Protein Modification Protein Ontology Entry Database Entry BioOM ProteomicsOM Legend Proteins identified by MS and database searches Class Protein stores a single protein record Protein modifications stored using OntologyEntry Link to external records stored in DatabaseEntry ProteinHitDBSearch MassSpecData package ProteinRecord package

24 Display protein name, species, pI and MW Data about protein modifications observed Protein Modification Protein

25 Measures of quality of match by MS. Link to MASCOT results ProteinHitDBSearch

26 Link to GeneDB record - parasite genome database - Accession and database URL are stored in the DatabaseEntry table Protein Database Entry

27 Link to Genbank record (or other database) Protein Database Entry

28 ImageAcquisitionFeatureExtractionBioAssayTreatment Physical BioAssay Image Measured BioAssay BioMaterial Measurement Material Type DNA RNA Protein Cell... Experiment Treatment BioMaterial Gel2DLCColumn MassSpec Experiment MeasuredBio- AssayData GelImage Analysis Acquisition Protocol Proteomics Workflow Top level stores experiment description Extraction of protein mixture: BioMaterial and Treatment 2-DE and liquid chromatography: subclasses of BioAssayTreatment BioMaterialMeasurement used to link multiple separations together Image scan and image analysis - link to PhysicalBioAssay MeasuredBioAssay links to spot data and MS data

29 Current Project: Trypanosoma brucei Trypanosomes cause sleeping sickness and other diseases in Africa and Latin America Model organism for parasitology Aims: Genome sequencing, microarrays and proteomics to find all the expressed genes and proteins - GeneDB at Sanger Proteomics component in Glasgow 2-DE and MS to find approx. 4000 proteins Find potential drug targets and improve genome annotation

30 Work In Progress Develop RAPAD prototype, store and query data from a range of experiment types Support Trypanosome project - future integration with microarray and genomics Tools for generating FGE-ML and XMLSchema Incorporate proteomics component into database system at Penn (GUS) –Add proteomics support to ToxoDB, PlasmoDB, GeneDB

31 Contact Email: Bioinformatics Research Centre - The Functional Genomics Facility at Glasgow is supported by a Wellcome Trust grant. My research is supported by an MRC Bioinformatics PhD studentship. Acknowledgements This work is in collaboration with the CBIL at Penn, in particular Chris Stoeckert and Angel Pizarro. Trypanosome data is from studies by Mike Turner and Anne Faldas in IBLS at Glasgow. PhD supervisors: Ela Hunt and Jonathan Wastling

32 ProteomeBioAssay Package

33 MassSpecProtocol Package

34 MassSpecData Package

35 ProteinRecord Package

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