GRISSOM Portal: Integrated Microarray Data Processing & Experimental Repository

1. ACT 119153 (NISR+Τ) 3rd Phase ”Open Access Repositories and Electronic Journals” supervised by the Greek Information Society /FP6 Subtask 6 Development of an integrated DNA microarray data processing and meta-analysis platform plus a microarray experimental data repository, in Grid.

2. Overview • Introduction • MicroArray Experiments • Problems • The GRISSOM Portal • System Architecture Overview • Case Study • Technical Issues • GRISSOM Platform Benefits

3. MicroArray Experiments Gene expression microarrays constitute promising high throughput measurement methodologies of the simultaneous expression of the whole genome of an organism at a specific instant. In practice they can be used to compare the level of transcription among different conditions in order to: i) Understand the mechanisms implicated in various stages of the biological system investigated ii) Classify diseases, or in general pathologies e.g. tumours with different prognosis status that are indistinguishable by microscopic histological examination iii) Monitor the response to therapy and iv) Identify and categorize diagnostic or prognostic biomarkers

4. MicroArray Experiment Workflow

5. Problems • Computational Processing steps of Microarray experiments data are laborious, something which represents by far the most considerable bottleneck in the successful exploitation of the technology. • Consequently there is imperative requirement for large storage and computing facilities • This results in compounding costs in a significant yet expensive technology, thus setting back research progress in the field. • Technical setbacks: array artifacts, scratches, scanner sensitivity and settings. • The curse of dimensionality: tens of thousands of genes (variables) with a small number of samples form major challenges in statistical inference. • Noise: non-specific hybridization as well as the difference between the actual amount of mRNA per cell and the relative differential expression measured by microarrays introduce variance and noise in experiments

6. The GRISSOM Portal • http://www.grissom.gr • Access • Restricted Web Access • Registered Users • Special Security Mechanism

7. The GRISSOM Portal • http://www.grissom.gr • Web Portal Access (SSL) • Two Access Modes: HellasGrid Certificates Validation Custom Certificates Validation Signed by NHRF

8. The GRISSOM Portal • HellasGrid Authentication & Access • MyProxy • MyProxy Certification Authority MyPrxy-Server Grid.Auth.GR User MyProxy-logon

9. The GRISSOM Portal http://www.grissom.gr • Features • Experimental data upload • Versatile Data Processing: • Normalization, Filtering, Statistical Selection, Clustering, Genes Annotation • Automated experiment submission to HellasGrid Infrastructure and monitoring • Biological Experiment Repository • Meta-Analysis Methods including gene annotation and GO Analysis

10. The GRISSOM Portal http://www.grissom.gr • Input: • Raw Dataset Files (various image formats,for cDNA/Affy) • Analysis Parameters • Output • Expressed Gene Lists • Interactive Graphs • Annotated Genes • References to similar Experiments

11. The GRISSOM Portal http://www.grissom.gr • Distributed Database: • Data records on Portal while actual data on SEs • Interconnection with other open biological databases (EBI ArrayExpress, NCBI GEO) for finding other related experiments • Annotation of genes performed using specialized databases (Biomart)

12. System Architecture Overview • Main Components: • Web Portal (User Interface) • Local DB • Grid Middleware • PHP + Java • gLite 3.1 • Parallel Execution Code (MPI + Octave)/ in the phase of development job submission through gLite DAG for fully distributed code execution • Grid Storage Elements

13. System Architecture Overview

14. System Architecture Overview Analysis steps are executed using the MPI technology over multiple nodes The number of Nodes are equal to the number of experimental conditions found in every experiment

15. Case Study – Test Scenarios The system was tested using multiple datasets that differ in size and architecture:

16. Case Study – Performance Measures Intel Core 2 Duo E4300 1.8GHz processor with 2.0 GB RAM system used running GNU Octave 2.1.73 on Linux Ubuntu 7.10 operation system

17. Case Study – Performance Measures Analysis Run using the Same Dataset with different Parallelization Level. First Run: 3 Nodes - Second Run: 9 Nodes

18. Grid-related Performance Limitations • Different node H/W generations • Heterogeneity of node installed S/W (esp. regarding biocomputing packages like Bioconductor) • Maintenance Issues

19. GRISSOM Platform Screenshots

20. GRISSOM Platform Benefits • Parallelization • Time optimization • User Transparency • Automated Job Submission + Monitoring • Open Access Biological Experiment Repository • Shell fully concealing the Grid

21. GRISSOM Development Team Aristotle Chatziioannou (achatzi@eie.gr) IliasMaglogiannis (imaglo@ucg.gr) IoannisKanaris (kanaris.i@aegean.gr) Charalambos Doukas (doukas@aegean.gr) EleftheriosPilalis (epilalis@eie.gr) PanagiotisMoulos (pmoulos@eie.gr) • Under the supervision of the Institute of Biological Research & Biotechnology, National Hellenic Research Foundation FragiskosKolisis (kolisis@eie.gr) • in collaboration with the National Documentation Center, National Hellenic Research Foundation • Funded by ACT 119153 (NISR+Τ) 3rd Phase ”Open Access Repositories and Electronic Journals” supervised by the Greek Information Society /FP6

22. http://www.grissom.gr Thank you Questions ?