1. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology GS2PATH: Linking Gene Ontology and Pathways Jin Ok Yang Korean BioInformation Center 6 th InCoB 2007

2. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology KOBIC ( Korean BioInformation Center)  The national bioinformatics center of Korea  Integration of diverse biological information  Genome information  Biodiversity information  Bioresource information  Bioinformatics training  International exchange program  Collaborative Development of bioinformatic tools  Bioportal (Biowiki)  Biopipeline (Bioworkflow engine)

3. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology BioWiki  Wiki a web technology that enables anyone to create and update website contents suited for developing online knowledge bases (e.g., Wikipedia )  BioWiki To adopt the wiki paradigm in biology Collaborative development of biological knowledge bases BioWiki Contest ( http://biowiki.net )http://biowiki.net

4. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology BioPipe (http://www.biopipe.net) Design View Ontology View Monitoring View Toolbar Drag the module from the list and drop it into the design view. BioWorkFlow Engine No installation required Drag & Drop, and then Connect BioPipe Contest !! –Aug 15th ~ Sep 20 th –Open free Web 2.0

5. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology GS2PATH: Linking Gene Ontology and Pathways Jin Ok Yang Korean BioInformation Center 6 th InCoB 2007

6. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Background  Efforts on analyzing functional relationships among gene sets with GO term and pathways  Gene Ontology (GO) Term based analysis  Analysis focused on function  GO term related pathways  More useful information How do you interpret the gene set ? GO & Pathways

7. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Gene set enrichment  Enrichment Test  Means test to investigate which specific GO term the given gene set has  P-value for GO term was calculated by using hyper-geometric probability  Gene set enrichment  Derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation  Evaluates microarray data at the level of gene sets which are defined based on prior biological knowledge

8. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Introduction: GO  GO databases and tools  GO term was used mostly to analyze data sets to identify significant biological changes  Pathways also can be exploited to find functional relationships in genes

9. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Introduction: Pathways

10. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology GS2PATH  A system to find gene set enrichment in each Gene Ontology (GO) terms and map the part of gene set on GO term into biological pathways (KEGG and BioCarta)  An integrated search tool for analyzing the functional relationships in gene sets and for providing comprehensive results

11. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Features  Functional relationships between GO term and pathways  Hyper-geometric test for gene set enrichment  Dual search for up- and down- regulation gene set  Various filtering options for GO terms  the number of descendant node, evidence of GO terms and statistical values mapping gene set in each GO term  User-specified coloring for genes onto pathways

12. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Implementation (1/3)  GS2Path consists of  one internal database (mapping database)  four components  Query Processor, GO Accessor, KEGG Accessor, and BioCarta Accessor

13. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Schema of internal mapping DB

14. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Architecture

15. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Implementation (2/3)  Query Processor  receives a user query  Converts query into gene related information  distributes it to the other components, waiting for receiving results from them  GO Accessor  retrieves statistical values mapping gene set in each GO terms to KEGG and BioCarta Pathways  Calculates P-value using cumulative hyper-geometric distribution

16. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Implementation (3/3)  BioCarta and KEGG Accessor  retrieve results from BioCarta and KEGG databases, respectively  To support user-specified coloring,  For KEGG, exploiting the web service API (SOAP/WSDL) of KEGG  For BioCarta, no supporting user-defined coloring API. Thus, after retrieving the image of a pathway from BioCarta database, we color genes in the image on-the-fly.

17. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology GO Term based Pathways Analysis

18. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Search  Gene set enrichment test in organism total profile: GO, KEGG and BioCarta  Single or two parts analysis (up and down regulation)  Pathway viewer for KEGG and BioCarta

19. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Input  Database  GO category  Biological Process  Molecular Function  Cellular Component  Pathways: KEGG and BioCarta  Organism  Human, Mouse, Rat, and Yeast  Gene ID list

20. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Test  Enrichment test  P-value: Hyper-geometric probability  FDR (False Discovery Rate)  Adjustment of p-value

21. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Filtering  GO Term  Evidence  Slim  Number of genes in term  P-value  Pathways: KEGG and Biocarta  Number of genes in term  P-value

22. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Example: microarray clustering data Part A Part B

23. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Interface Select Organism Put the gene set Select GO category or Pathways

24. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Click

25. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Retaining only GO terms having at least 5 genes

26. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology

27. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Select customized colors

28. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology

29. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology

30. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Genes colored in KEGG and BioCarta

31. >>> Korean BioInformation Center >>> KRIBB Korea Research institute of Bioscience and Biotechnology Conclusion  Using Gs2path, users  Get the integrated Gene Ontology terms and pathways information together  Filter the results with various conditions  Capture relationships between Gene Ontology terms and Pathways  Available at http://array.kobic.re.kr:8080/arrayport/gs2path/