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BioVLAB-Microarray: Innovative Data Analysis Environment

Explore the virtual realm of microarray data analysis with BioVLAB, a high-level workflow execution workbench powered by computing clouds. This tool simplifies the complex process of gene expression analysis for biologists and small research labs, offering on-demand resources and a suite of applications within a reconfigurable GUI. Discover the dynamics of cell activity, gene interaction networks, and protein function predictions through this efficient and user-friendly platform.

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BioVLAB-Microarray: Innovative Data Analysis Environment

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  1. BioVLAB-Microarray: Microarray Data Analysis in Virtual Environment Youngik Yang, Jong Youl Choi, Kwangmin Choi, Marlon Pierce, Dennis Gannon, and Sun Kim School of Informatics Indiana University

  2. CONTENTS • Introduction • Approach • Related Works • Microarray technology • System Architecture • Experiments • Conclusion • Demo

  3. INTRODUCTION • Analysis of high throughput microarray experiment • Performing microarray analysis is a demanding task for biologists and small research labs • Computing infrastructure issue • Computationally intensive • Nontrivial to integrate various bioinformatics applications • Exploratory data analysis issue • Multiple tasks in a single batch • Repetitive execution

  4. APPROACH • On-demand computing resources • A suite of microarray analysis applications • Reconfigurable GUI workflow composer can alleviate technical burden • Well defined workflow can be repetitively used • Web portal • Reusable, reconfigurable, high-level workflow execution workbench powered by computing clouds for microarray gene expression analyses

  5. RELATED WORKS • Efficient and user-friendly workflow composers and execution engine • SIBIOS, BioWBI, KDE Bioscience • Distributed and heterogeneous computing resources + Workflow system • Taverna, Triana, Kepler, GNARE, RENCI-Bioportal

  6. MICROARRAY TECHNOLOGY • A subset of genes is expressed corresponding to environmental changes and its changing needs • Dynamics of cell activity • Measure gene expression levels of hundreds of thousands of genes within a cell • Usage • Function prediction: Guilt by association • Interaction: Co-expression of genes in transcription networks reveals how they interact. • Drug discovery: Identify genes related to certain disease and detect effectiveness of new drugs Source: www.liv.ac.uk/lmf/about_microarrays.htm

  7. RESEARCH GOALS • Gene expression analysis • Search for similar patterns of genes • Similar patterns of gene may reveal the function of a gene with unknown function • Extraction of differentially expressed genes • Statistical evaluation • Clustering • Protein function prediction • Genes with similar expression may need to be studied as a group • Component analysis • Hidden structure of expression patterns may be revealed • Expression network analysis • Expose hidden structures • Protein-protein interaction (PPI) network analysis • Central issue: key role in understanding how a cellular system works • Modularity in structure in a network may reflect higher-level functional organization of cellular components

  8. MICROARRAY ANALYSIS COMMON TASK • Output of a task can plugged into another task • Repeat the same set of tasks with small changes of parameters

  9. SYSTEM ARCHITECTURE • Workflow composer and execution engine • Application services • Web portal Application Services Workflow Composer & Execution Execute Create Manage Data Web Portal

  10. WORKFLOW COMPOSER & EXECUTION ENGINE • Introduced in the scientific communities to execute a batch of multiple tasks • Enables repetitive tasks easily • Directed acyclic graph • Node: application to execute • Starting node: input • End node: output • Edge: a flow of data Input Task A Task B Task C Output

  11. XBaya • GUI Workflow composer and execution engine • Developed at IU • Drag-and-drop compose from workbench • Monitor status of workflow execution Workbench Panel Workflow Composer Panel Drag-and-drop Application Information Panel Monitor Panel

  12. APPLICATION SERVICES • Interoperability among applications can be achieved by Application Services • Generic Service Toolkit (Gfac) • Gfac converts command-line bioinformatics application into a web service • On-demand computing resources • Amazon Elastic Computing Cloud (EC2) • Remote storage services • Amazon Simple Storage Services (S3) • Microsoft Application-Based Storage

  13. BioVLAB APPLICATIONDEVELOPMENT PROCEDURE User • Develop a command line app. Gfac Registration form Admin • Install the app. in Amazon EC2 • Let the app. store any output to Amazon S3 / Microsoft Application-Based Storage • Make a virtual machine image • Register the app. by using Gfac User • Instantiate EC2 and run the app. by using XBaya (Gfac user manual)

  14. WEB PORTAL • Adiministrator • Management of registered applications by Gfac registry portlet • User management and access control • User • access of stored data • Built by Open Grid Computing Environments (OGCE)

  15. ANALYSIS RESOURCES • R: statistical learning • Bioconductor: microarray analysis • Data acquisition: NCBI GEO Microarray DB • Similar expression pattern: correlation • Differentially expressed gene: limma package • Clustering: K-means, hierarchical clustering, QT clustering, biclustering, Self organizing map (SOM) • Component Analysis: principal component analysis (PCA) and Independent component analysis (ICA) • Network: Database of Interacting Proteins (DIP), Perl Graph package and GraphViz

  16. EXPERIMENT • Data set: GDS38 • Remotely retrieved from the NCBI GEO database • Time-series gene expression data to observe cell cycle in Saccharomyces cerevisiae yeast genome. • 7680 spots in each 16 samples • Each sample was taken every 7 minutes as cell went through cell cycle. • Expression analysis • PPI network analysis

  17. EXPERIMENTS

  18. CONCLUSION • Microarray data analysis in virtual environment • Coupling computing clouds and GUI workflow engine • Effective system design for small research labs

  19. FUTURE WORKS • Integration of more packages and analyses • A system of great flexibility • Integrate various high throughput data • Microarray, mass spectronomy, massively parallel sequencing, etc • Integrate various computing resources • Clouds, grid, and multi-core PCs • Integrate various public resources • NCBI, KEGG, PDB, etc

  20. SCREEN SHOTS

  21. S3 BROWSER

  22. EC2 ACTIVE INSTANCE

  23. WORKFLOW FOR CLUSTERING

  24. INPUT PARAMETERS

  25. WORKFLOW EXECUTION

  26. DATA ACQUISITION

  27. SUBSET EXTRACTION

  28. CLUSTERINGS

  29. WORKFLOW TERMINATION

  30. EXPERIMENT RESULT

  31. DOWNLOAD FILE

  32. HEATMAP FOR K-MEANS CLUSTERING

  33. ACKNOWLEDGEMENT • The work is partially supported by NSF MCB 0731950 and a MetaCyt Microbial Systems Biology grant from Lilly Foundations. • Extreme Computing Group at IU • Suresh Marru, Srinath Perera, and Chathura Herath

  34. Thank You

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