Data Access

1
Data Access Integration in the ISPIDER
Proteomics Grid

• L. Zamboulis, H. Fan, K. Bellhajjame, J. Siepen,
• A. Jones, N. Martin, A. Poulovassilis, S.
  Hubbard,
• S. M. Embury, N. W. Paton

2
Overview

• The ISPIDER project
• Data Access Integration of Proteomics Resources
• Challenges
• Middleware
• Proteomics resources global schema
• System architecture query processing
• Future Work

3
ISPIDER

• Project Goals
• Build an integrated platform of proteomic
  resources
• Use existing resources produce new ones
• Create clients for querying, visualisation, etc.

4
ISPIDER

• Objective develop an integrated platform of
  proteome-related resources, using existing
  standards
• Benefits
• Access to increased breadth of information
• More reliable analyses
• Integration brings added value

5
Challenges

• Proteomics repositories in disparate locations
• ?need for distributed solution
• common access, distributed query processing
• ?need for integration
• overlapping data, different representations
• Data/schemas constantly updated/evolve
• ? need virtual or hybrid integration
• ? need schema evolution support

6
Middleware (1/2)

• OGSA-DAI middleware exposing data sources on
  Grids via web services
• open-source and extensible
• uniform access to relational XML data sources
• supports a variety of operations, e.g.
  querying/updating, data transformation, data
  delivery
• OGSA-DQP service-based distributed query
  processor
• supports querying of relational OGSA-DAI data
  sources
• offers implicit parallelism for data-intensive
  requests

7
Middleware (2/2)

• AutoMed heterogeneous data transformation and
  integration system
• subsumes traditional data integration approaches
• handles various data models easily extensible
• virtual/materialised/hybrid integration
• schema evolution
• data warehousing tools

8
Data Integration Approaches

• Global-As-View (GAV) approach describe GS
  constructs with view definitions over LSi
  constructs
• Local-As-View (LAV) approach describe LSi
  constructs with view definitions over GS
  constructs

9
Both-As-View (BAV) Approach

• Schema transformation approach
• For each pair (LSi,GS) incrementally modify
  LSi/GS to match GS/LSi

10
BAV Example

• Transformation pathway consists of primitive
  transformations
• Pathway contains both GAV LAV definitions
• Transformations are automatically reversible
• Metadata in AutoMed Repository

11
Proteomics Resources

• PEDRo
• collection of descriptions of experimental data
  sets in proteomics
• has been used as a format for exchanging
  proteomics data
• gpmDB
• contains a large number of proteins and peptide
  identifications
• initially designed to assist in the validation of
  peptide MS/MS spectra and protein coverage
  patterns
• PepSeeker
• developed as part of the ISPIDER project
• comprehensive resource of peptide/protein
  identifications
• PRIDE
• centralised, standards compliant, public
  proteomics repository
• contains protein/peptide identifications
  evidence supporting them

12
Global Schema

• Trade-off between
• being able to answer specific user queries
• a full integration
• Properties
• Based on PEDRos peptide/ protein identification
  section and
• expanded with information unique in other
  resources
• Entities identified by LSIDs

13
System Architecture

• Sources wrapped with OGSA-DAI
• AutoMed toolkit wraps OGSA-DAI resources
• Integration of OGSA-DAI resources
• Queries submitted to AutoMed QP are evaluated
  with the help of OGSA-DQP

14
System Architecture

• Sources wrapped with OGSA-DAI
• AutoMed toolkit wraps OGSA-DAI resources
• Integration of OGSA-DAI resources
• Queries submitted to AutoMed QP are evaluated
  with the help of OGSA-DQP

15
System Architecture

• Sources wrapped with OGSA-DAI
• AutoMed toolkit wraps OGSA-DAI resources
• Integration of OGSA-DAI resources
• Queries submitted to AutoMed QP are evaluated
  with the help of OGSA-DQP

16
System Architecture

• Sources wrapped with OGSA-DAI
• AutoMed toolkit wraps OGSA-DAI resources
• Integration of OGSA-DAI resources
• Queries submitted to AutoMed QP are evaluated
  with the help of OGSA-DQP

17
System Architecture

• Sources wrapped with OGSA-DAI
• AutoMed toolkit wraps OGSA-DAI resources
• Integration of OGSA-DAI resources
• Queries submitted to AutoMed QP are evaluated
  with the help of OGSA-DQP

18
Query Processing

• Query is submitted to AutoMeds GQP
• Reformulated
• Optimised
• AutoMed-DQP Wrapper
• IQL ? OQL
• OGSA-DQP evaluates OQL queries
• OQL result ? IQL result

19
Query Processing

• Query is submitted to AutoMeds GQP
• Reformulated
• Optimised
• AutoMed-DQP Wrapper
• IQL ? OQL
• OGSA-DQP evaluates OQL queries
• OQL result ? IQL result

20
Summary

• Proteomics repositories in disparate locations
• ?need for distributed solution
• ?need for integration
• Data/schemas constantly updated/evolve
• ? need virtual or hybrid integration
• ? support schema evolution

21
Future Work

• Schema evolution
• Evaluation of AutoMed advantage
• Expose AutoMed functionality to the Grid
• AutoMed and Taverna integration

22
Future Work

• Taverna tool for Web Service orchestration in
  workflows
• Related services may be incompatible
• Current solution involves writing custom code for
  every pair of WS
• Use AutoMed toolkit for semi-automatic
  integration of XML Web Services
• mappings from WS to ontologies
• automatic integration

23
ISPIDER Project Members

• Birkbeck College
• Nigel Martin
• Alex Poulovassilis
• Lucas Zamboulis (R.A.)
• Hao Fan (former R.A.)
• European Bioinformatics Institute
• Rolf Apweiler
• Henning Hermjakob
• Weimin Zhu
• Chris Taylor
• Phil Jones
• Nisha Vinod

• University of Manchester
• Simon Hubbard
• Steve Oliver
• Suzanne Embury
• Norman Paton
• Carol Goble
• Robert Stevens
• Khalid Belhajjame (R.A.)
• Jennifer Siepen (R.A.)
• U.C.L.
• David Jones
• Christine Orengo
• Melissa Pentony (R.A.)