FGE-OM: Functional Genomics Experiment - Object Model

Andy Jones

Department of Computing Science

University of Glasgow

Overview

• Introduction to proteomics

• Motivation for shared standards

• FGE-OM

• Database implementation - RAPAD

• Current biological projects

Proteomics Workflow

Sample OriginProtein

Solubilisation

Image Analysis

Database Search

ID Vol X Y 1 454 23 242 222 28 873 12 20 12 4 662 262 1015 49 222 906 113 485 10 7 119 98 987

Multiple GelAnalysis

ID Vol X Y 1 654 23 242 25 28 873 187 21 16 4 672 262 1115 54 222 906 113 487 10 7 125 98 987

MALDI MS/MS

Protein identification

2D-PAGE

StatisticalAnalysis

Mass Spectrometry

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

FGE-OM

Components common to all functional genomics experiments

Microarray specific components

Classes modelling proteomics technologies

Top-level of the Object Model

Namespaces

BioOM

ArrayOM

ProteomicsOM

Functional Genomics Experiment - Object Model

MAGE-OMderived

PEDRo and Gla-PSIderived

• 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. Bioinformatics2003, (in press)

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)

http://pedro.man.ac.uk http://psidev.sourceforge.net/

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)

Experiment ProtocolBio-

MaterialMeasure-

ment

BioAssayBioAssay

Data

BioEvent DescriptionBio-

SequenceBQS

Higher Level

Analysis

AuditAnd Security

Identifiable

Extendable

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

• 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

BioOM: BioAssayData vs ArrayOM:ArrayBioAssayData

BioAssay Data

BioAssay Dimension

BioData Tuples

BioData Values

MeasuredBioAssay

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

QuantitationType

Dimension

QuantitationTypeMap

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

Protein Separation

MassSpec Protocol

Proteome BioAssay

MassSpec Data

ProteinRecord

ProteinData

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

Gel2D

Column

Physical GelSpot

Fraction

Separation techniques Separation products

Source biomaterial

BioMaterialBioAssay Treatment

BioMaterialMeasurement

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

ProteinSeparation Package

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

BioAssay

Image

Channel

Image Acquisition

GelImage Analysis

Measured BioAssay

Feature Extraction

Measured BioAssay

Data

BioAssay Data

targettreatment

ProteomicsOM:ProteomeBioAssay package

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

Image

AcquisitionImage Channel

GelImage Analysis

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

ProteinData Package

Search capabilities over protein name, range of pI, mass or spot volume

Clicking a spot loads protein data pages

Identified Spot

Protein

MassSpecProtocol and MassSpecData

MassSpec Experiment

PeakList

Peak

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

ProteinRecord package

Location

speciesmodificationType

Protein Modification

Protein

OntologyEntry

DatabaseEntry

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

ProteinHit DBSearch

MassSpecData package

ProteinRecord package

Display protein name, species, pI and MW

Data about protein modifications observed

Protein Modification

Protein

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

ProteinHit DBSearch

Link to GeneDB record - parasite genome database

- Accession and database URL are stored in the DatabaseEntry table

Protein

Database Entry

Link to Genbank record(or other database)

Protein

Database Entry

ImageAcquisition

FeatureExtraction

BioAssayTreatment

Physical BioAssay

Physical BioAssay

Image

Measured BioAssay

BioMaterial Measurement

Material TypeDNARNAProteinCell...

Experiment

Treatment BioMaterial

BioMaterial

Gel2D

LCColumn

MassSpecExperiment

MeasuredBio-AssayData

GelImage Analysis

AcquisitionProtocol

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

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

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

ContactEmail: jonesa@dcs.gla.ac.uk

http://www.dcs.gla.ac.uk/~jonesa/FGE/fge.html

Bioinformatics Research Centre - www.brc.dcs.gla.ac.uk

The Functional Genomics Facility at Glasgow is supported by a Wellcome Trust grant. My research is supported by an MRC Bioinformatics PhD studentship.

AcknowledgementsThis 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

ProteomeBioAssay Package

MassSpecProtocol Package

MassSpecData Package

ProteinRecord Package