1. LOGO/ICON Keval Mehta School of Informatics Master of Science in Bioinformatics Andrews Dalkilic Team Dr. Mehmet Dalkilic, Dr. Justen Andrews, Dr. John Colbourne, Dr. Brian Eads, James Costello, Rupali Patwardhan, Sumit Middha, Junguk Hur I ndigene - Data Mining & Visualization Component

2. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 2  Problem Statement  Motivation  Data Mining & Integration  Visualization Software Features  Eye in the Future  Questions? Overview < Outline

3. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 3 Classmates.com Face book Hi5 myYearbook Yahoo 360° Sally Jim Social Network Overview Problem Statement <  Connections based on:  Common Interests (Parameters)  Supplementary or complimentary work (Functionality)  The strength of the connection decided by how often you interact (weight)  People form groups of similar interests and motivation (clustering)  A second and a third level connection and new friendships can be made from friends of friends (similar questions can be asked in Gene Networks) Analogy

4. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 4  What does gene expression of say alpha- synuclein of value 0.9 in this condition mean with respect to other genes?  What can I do with large datasets of gene expression data and protein assays?  How can I make sense of so many disparate datasets from the experiments by scientists?  What can I know about a gene and how it acts in the presence of other genes? Problem Statement Overview Problem Statement <

5. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 5 Motivation Overview Problem Statement Motivation < Abundance of high-throughput information from DNA, RNA and protein assays Decades of detailed genetic investigations linking to phenotypes Next big question: Next big question: Insights into functional relationships among genes Brazhnik et al., 2002. Trends in Biotechnology 20: 467-472

6. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 6 Gene Expression  Time Course  Tissue Specific  Sex Specific  Developmental Protein-Protein Interaction Transcription Factor Binding Site Genetic Interaction Phenotypic Annotation Overview Problem Statement Motivation Data Mining & Integration < Data Mining & Integration

7. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 7  High-throughput Microarray Data  Arbeitman, Larval and Parisi datasets  Keval Mehta and Rupali Patwardhan  Protein-Protein Interaction  Junguk Hur  Genetic Interaction  James Costello  Allelic Phenotype  James Costello  Transcription Factor Binding Site  Sumit Middha Drosophila melanogaster Data Mining & Integration Overview Problem Statement Motivation Data Mining & Integration <

8. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 8  Arbeitman (Life cycle of Drosophila)  159 slides (79 slides with one replicate each)  Larval tissue-specific transcripts  15 slides (no replicates across slides)  Parisi  14 slides (no replicates across slides)  Using OLIN (from R’s Bioconductor marray package) we generated the Normalized values  Dealing with replicates within the slide (spot replicates)  We averaged the M and A values and back-calculated the new merged intensity values  Pearson’s correlation is computed for all unique combinations of genes = [n * (n-1)] / 2 Gene Expression Overview Problem Statement Motivation Data Mining & Integration <

9. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 9 0.5-1.2…0.9 1.3-3.2…0.2 G1 Slide 1….Slide n G2 Pearson’s Correlation Formula X = G1 Y = G2 Finding Pearson’s Correlation Overview Problem Statement Motivation Data Mining & Integration <

10. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 10  Example1: Positive Correlation FBgn0029947 and FBgn0033888 - Correlation Value = 0.94  Example2: Negative Correlation FBgn0035588 and FBgn0010488 - Correlation Value = -0.9 Overview Problem Statement Motivation Data Mining & Integration <

11. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 11  Normalized the Integration Overview Problem Statement Motivation Data Mining & Integration <

12. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 12 How can I visualize all this data effectively and still at the same time keeping it perceptible? Inspiration: Visual Thesaurus Referred by Dr. Youn Lim Overview Problem Statement Motivation Data Mining & Integration Software Features < A Big Challenge

13. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 13  A user interface toolkit for interactive information visualization  built in Java using Java2D graphics library  data structures and algorithms  pipeline architecture featuring reusable, composable modules  animation and rendering support  architectural techniques for scalability Overview Problem Statement Motivation Data Mining & Integration Software Features < prefuse API

14. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 14 Overview Problem Statement Motivation Data Mining & Integration Software Features User centered Interface < User Friendly Interface

15. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 15 Overview Problem Statement Motivation Data Mining & Integration Software Features User centered Interface Our Website <  Querying this Integrated Database  Our website https://projects.cgb.indiana.edu/ingene/cgi- bin/upload.cgi  I/P: Upload a file with list of FBgn IDs of interest  O/P: Output’s an XML file Website

16. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 16 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network < Load Network XML input file

17. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 17 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture < FlyBase: online database Our Indigene DB Website: XML file Visualization Component Data Mining Query Architecture

18. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 18 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors < Highlight Immediate Neighbors

19. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 19 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click < Left Mouse Click

20. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 20 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors < Show Neighbors

21. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 21 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph < Move Graph

22. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 22 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph Randomly place nodes < Randomly place nodes

23. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 23 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph Randomly place nodes Select Nodes < Neighbors of more than one node

24. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 24 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph Randomly place nodes Select Nodes Zoom OUT & Zoom IN < Zoom OUT & IN

25. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 25 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph Randomly place nodes Select Nodes Zoom OUT & Zoom IN Node & Edge Info. < Node & Edge Information

26. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 26 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph Randomly place nodes Select Nodes Zoom OUT & Zoom IN Node & Edge Info. Load Details < Load Details

27. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 27 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph Randomly place nodes Select Nodes Zoom OUT & Zoom IN Node & Edge Info. Load Details Search < Search

28. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 28 Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph Randomly place nodes Select Nodes Zoom OUT & Zoom IN Node & Edge Info. Load Details Search ReLoad Graph < ReLoad GraphDemo

29. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 29  Cytoscape www.cytoscape.org  Graphviz packages and various other packages  Advantage  Highly customizable to ones need and platform independent  Some exclusive features such as reload the graph and playing with cutoffs on run time  Searching and pruning the graph  Highly modular coding style allows extensibility to analysis of any graph network  Make the software talk with live data by connecting to database and querying it in real time  Apply machine learning algorithms that can throw light on possible paths that are otherwise not easy to perceive Eye in the Future & advantage over other tools Overview Problem Statement Motivation Data Mining & Integration Software Features User Centered Interface Our Website Load Network Architecture Highlight Immediate Neighbors Left Mouse Click Show Neighbors Move the Graph Randomly place nodes Select Nodes Zoom OUT & Zoom IN Node & Edge Info. Load Details Search ReLoad Graph

30. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 30  Advisors Dr. Mehmet Dalkilic and Dr. Justen Andrews  Research Team John Colbourne, Brian Eads, James Costello, Rupali Patwardhan, Sumit Middha, Junguk Hur  Visualization Expert Advice Ketan Mane, Ph.D student – SLIS  Computing Facilities Center for Genomics, School of Informatics & Department of Computer Science  Special Thanks Dr. Youn-Kyung Lim and Dean Marty Siegel  Thank you My Parents and my friends Acknowledgements

31. LOGO/ICON Capstone Presentation Keval Mehta April 21, 2006 31 Questions?