1. IBU 'A bioinformatic Problem Solving Environment in the e-BioLab' VL-e Sub Program 1.5: Bioinformatics Timo Breit Micro-Array Department & Integrative Bioinformatics Unit Faculty of Science, University of Amsterdam

2. IBU Dutch telescience Data intensive science Medical diagnosis Where in the Virtual Laboratory for e-Science? Generic Virtual Laboratory e -science layer Application Layer Bioinformatics Bio diversity Food Informatics Grid Layer ‘BI- PSE’ BioInformatics Problem Solving Environment

3. IBU Why in the VL-e? Data explosion in life sciences research. RNA analysis by Northern blot: 1-15 genes Analyzed genes A B C D E F G H I J K L M N O P Q R S T Samples of cellular experiments RNA analysis by micro-array: 1.000-40.000 genes A B C D E F G H I J K L M N O P Q R S T

4. IBU Life sciences research today: whole system –omics data. Biology Genomics Transcriptomics Proteomics Metabolomics Integrative biology or Systems biology Experiment RNA protein metabolite DNA Biotechnology Results Bioinformatics Data storage Data handling Data preprocessing Data analysis Data integration Data interpretation Biologist Informatics ICT infrastructure

5. IBU How in VL-e? A bioinformatics problem solving environment (BI-PSE) a.o.: security (AAA) ICT infrastructure Life sciences domain e-bio science Generic virtual laboratory Grid- layer a.o.: analysis methods information management semantic modeling adaptive inf. disclosure a.o.: domain knowledge domain information domain data a.o.: semantic modeling Hypothesis generation In-silico experiment Decision process Experiment design Hypotheses Wet-lab experiment Enhancing knowledge model Results X ICE ICE: Interactive & Creative Environment RESULT: Rauwerda et al: The Promise of a virtual lab. Drug Discov Today. 2006 Mar;11(5-6):228-36. ESE ESE: Experiment Support Environment DSE DSE: Decision Support Environment Problem solving environment

6. IBU Parts of the BI-PSE we work on VL-e Biological use case Huntington Disease Biological use case Toxicogenomics Grid computing Resource ICE Resources identification model Staff PD Christian Henkel PD Ramin Monajemi e-BioLab Staff SS Han Rauwerda SP vacancy Staff PD Scott Marshall PD Tessa Pronk SP Frans Verster Staff PD Marco Roos AIO Lennart Post IB-ICE IB-ESE Integrative bioinformatics knowledge model experiment design Staff MAD Martijs Jonker MAD Oskar Bruning Staff PD Marcia ad Inda SP vacancy M-A ESE Microarray analyses methods workflows VL-e Use case SigWin VL-e Use case Histone

7. IBU Basic configuration of e-BioLab VL-e use case SigWin finder Goal: A workflow to find significant windows in data related to a given sequence (of any type). Motivation: Find sets of genes (windows) with increased overall gene expression (significance) in expression data ordered by gene location on the chromosomes (sequence).

8. IBU Basic configuration of e-BioLab SigWin: Significant Windows* Márcia Alves de Inda, Dimitri, Frans Verster, Marco Roos Given a data set we compute Sliding Window (SW) Medians for a given window size. Using the SW Medians data we compute a False Discovery Rate (FDR) threshold. Windows with values above the FDR threshold are called significant windows (or Windows Beyond the Threshold) *R. Versteeg et al. Genome Res 2003 13: 1998-2004.

9. IBU Basic configuration of e-BioLab VLAM SigWin-finder workflow 1) Read sequence 2) Rank sequence 3) SW Medians 4) Sample to Frequency 5) SW Medians Prob 6) FDR Threshold 7) WinBeTs 8) GnuPlot Modules

10. IBU Basic configuration of e-BioLab SigWins and periodic data

11. IBU Basic configuration of e-BioLab Example periodic data: Temperature in Amsterdam

12. IBU Basic configuration of e-BioLab Integration genomic & transcriptomics data

13. IBU Basic configuration of e-BioLab Integration genomic & transcriptomics data (zoom)

14. IBU Basic configuration of e-BioLab VL-e use case Histone code and semantic modeling Lennart Post, Scott Marshall, Marco Roos Hypothesis A relationship exists between histone modification and transcription factor binding sites Histones Histone modification Transcription factor binding site Transcription factor Transcription

15. IBU Basic configuration of e-BioLab Design ‘myModel’: Protégé - OWL plug-in http://protege.stanford.edu

16. IBU Basic configuration of e-BioLab Data integration through semantic modeling

17. IBU Basic configuration of e-BioLab Result data integration via semantic modeling L L UCSC genome browser snapshot Result: Correlation between histone modification and transcription factor binding sites etc… Overlap

18. IBU Bioinformatics Problem Solving Environment Domain interaction: Basic concept of an e-BioScience Laboratory (e-BioLab) non formalized knowledge + ideas + intuition + discussion Biologists e-BioScientist ToolsGridMethodsWorkflows Basic model of problem area e-BioOperator Readily accessible data + models data mining Easy visualization Small integration experiments + integration methods Vague results

19. IBU Basic configuration of e-BioLab Basic set-up of the e-BioLab

20. IBU Basic configuration of e-BioLab Anticipated tiled display in e-BioLab SOM P1 cluster 1 P2 cluster 1 P3 cluster 1 1 2 P2 cluster 2 P3 cluster 2 P1 cluster 2 3 P2 cluster 3 P3 cluster 3 P1 cluster 3 Hier.clust. Video remote collaboration Gene lists Chrom.map 1 Chrom.map 2 Chrom.map 3 Remote whiteboard Pathways displayed

21. IBU Basic configuration of e-BioLab Acknowledgements Within SP1.5: Marco RoosMolecular biologistHan RauwerdaBioinformaticia Roel van DrielBiochemistChristiaan HenkelMolecular Biologist Lennart PostAIO (vDriel) Martijs JonkerBioinformatician Marcia Alves de Inda Computational scientists Oskar BrunningBioinformatician Scott MarshallInformaticianTessa PronkMolecular biologist Frans VersterScientific programmerRamin MonajemiInformatician Timo BreitMolecular biologist Within VL-e SP1.2; use ontologies in semantic modeling SP1.4; use case R on Grid, e-bioscience SP2.2; AID; ontologies and semantic modeling SP2.4; information management SP2.5; workflow methods and tools Sp3.3; e-BioLab SP4.1: VLEIT team More information: www.micro-array.nl Outside VL-e BioRange, NBIC; Dutch bioinformatics - Content driven data modeling (Kok-LUMC, Adriaans,-UvA etc…) - Test case systems biology (RUG, CMBI, TNO, UvA, etc…) - SigWin (vKampen-AMC etc…) - E-BioLab (vdVeer-VU, vd Vet-UT, Nikhef, SARA,etc…) BioAssist - Microarray workflow (many….) - Reannotatie (Leunissen-WU, Neerincx-WU etc…) Vacancies @ IBU: Bioinformatician: micro-array data analysis (HBO/WO, 2 years) Scientific Programmers: building the e-BioLab

22. IBU Where in the Virtual Laboratory for e-Science? Dutch telescienc e Data intensive science Medical diagnosis Generic Virtual Laboratory e -science layer Application Layer Bioinformatics ASP Bio diversity Food Informatics Grid Layer ‘IB- PSE’ Integrative Bioinformatics Problem Solving Environment BioRange & BioAssist

23. IBU SP1. Bioinformatics for Microarray Technology 1.Experimental design 2.Understanding biological processes 3.Genotype-phenotype analysis 4.Dissemination of bioinformatics tools and expertise, and education SP2. Bioinformatics for Proteomics and Metabolomics 5.Preprocessing and identification tools 6.Analysis and modeling tools 7.Molecular interactions tools SP3. Integrative Bioinformatics. 8.Structural genomics 9.Comparative genomics 10.Phenotype-genotype modelling 11.Pathway modelling and visualisation 12.Content driven data modelling 13.Content driven text mining SP4. VL-E Informatics for Bioinformatics Applications. 14.Adaptive information disclosure 15.User interface and visualization 16.Collaborative information management SP5. Test bed with “Real-Life Applications”. 17.Selection of bioinformatics applications, scaling approach, & real-life test applications 18.Dedicated scaling and validation approach 19.Integrated scaling and validation approach Dissemination Subprograms & research themes in national bioinformatics initiative BioRange. Bioinformatics Informatics ICT infrastructure

24. IBU Use cases (user scenarios) 1.R on grid (IUC1.5.1) (finished) Creation of a web service that executes an R-script that invokes a LAM-MPI distributed calculation on the grid on a number of nodes that can be chosen by the user. 2.R in workflows (IUC1.5.4) (started) Proof of principle of a micro-array analysis workflow by invocation of web services. Requirements are visualization of intermediate results and enabling human interaction. 3.Re-annotation of micro-array libraries (IUC1.5.5) (started, with J. Leunissen WU) Re-annotation from sequence by invocation of remotely hosted web services in a workflow environment. 4.‘SigWin’ (IUC1.5.3) => Significant Window Finder (proof of principle given) Generalization of method that finds ‘Regions of IncreaseD Gene Expression’ (RIDGEs) into workflow in VLAM environment that finds significant windows in sequences of values. 5.Histone Code case 1 (IUC1.5.2) (proof of principle given) Proof-of-concept data integration via semantic models 6.Scaling problems semantic data integration (RUC1.5.1) (Finished, lead to 2 new IUCs) Provide guidelines for the infrastructure to use for semantic data integration

25. IBU A view on bioinformatics research and IBU Informatics research Applied bioinformatics Bioinformatics research Biology research Bio -- informatics IBU

26. Outline of presentation. -Where are we in positioned in the VL-e project? -Why do we need a Integrative Bioinformatics Problem Solving Environment? -What do we want to do with a IB-PSE? -How do we think to create a functional IB-PSE? -Who are we? -Where do we start? -When do we think we will have a functional IB-PSE? -Who are our collaborators?

27. IBU What do we want to do with a IB-PSE? Concept of integrative bioinformatics Analysis methods ICT infra- structure Experiment design VL-e Integrative & computational bioinformatics experiment Model Visualization Biological solutions Biological phenomena Biological knowledge Omics data Data- driven hypothesis Problem- driven hypothesis biological problem Biological research domaine-bioscience core domainEnabling science domain

28. IBU Computational experimentation through advanced data integration. Data source A Computational experiment Semantic modelling Interface model A Data source B Semantic modelling Ontology B Interface model B Ontology A

29. IBU Bioinformatics in the Netherlands University of Amsterdam NBIC- Bioinformatics NBIC, national foundation for Dutch bioinformatics. Involves all academic and several industrial life sciences research organizations. VL-e Consortium- Informatics VL-e, informatics Bsik project by WTCW supporting BiOrange. Local bioinformatics initiatives, mainly focused on directly supporting specific local life sciences research questions. VL-E Experimental (rapid prototyping) Environment VL-E Proof-of- concept Environment VL-E Exploitation Environment (SARA) BiOrange Proof-of- concept Environment Life sciences researchers mainly focused on resolving specific life sciences research questions. BioRange Bioinformatics Research BiOrange, bioinformatics Bsik project by NBIC and “Nationaal Regieorgaan Genomics”. BioASP, Bioinformatics Service Provider for life sciences researchers by NBIC and “Nationaal Regieorgaan Genomics”. Bio- Application Support Program (Bio-ASP)

30. IBU componentinteraction stimuli mechanism program history response presencestate Data integration: basic concept of any cell Assumption: the complexity of life is organized via a limited number of general cellular mechanisms. ED DA DI LC