1. Review of Ondex Bernice Rogowitz G2P Visualization and Visual Analytics Team March 18, 2010

2. Ondex at a Glance Open source, Gnu Public Library License on SourceForge Large data sets (100,000’s of data objects) Parsers for multiple inputs Multiple data output formats Generalized data structure to link different biological networks Goal: Integrate, analyze and visualize data from heterogeneous sources Key focus: data that can be represented in graph form

3. Data Integration Import multiple data formats Tab delimited Fasta GO (OBO 1.2)GO PSI MI (version 2.5)PSI MI SBML Import from a wide array of databases (via parsers) AraCyc AtRegNet BioCyc BioGRID Brenda Cytoscape Multiple network data export formats and multiple image output formats SGD TAIR TIGR Transfac Transpath UniProt WordNet EcoCyc Gene Ontology GOA Gramene Grassius KEGG Medline MetaCyc O-GlycBase OMIM PDB Pfam Plant Ontology

4. Graph Construction Gene1 Gene 2 value = 1 Gene 1 Gene 3 value = 4 Gene 1 Gene 4 value= 5 Gene 2 Gene 3 value = 1 Gene 2 Gene 4 value = 2 Gene 3 Gene 4 value = 5 G1G2G3G4 G1145 G212 G35 G4 Similarity matrix Graph Table of Tuples Nodes are data entities– genes, proteins, etc Edges show connectivity and degree of connection Metadata can enhance the graph Node size, node color Edge width, edge color Any data that can be expressed in this form can be represented as a graph

5. Gene Expression Array Data and its associated Graph (dendogram) Various metrics can be used, e.g. different statistical correlation measures, noise- reduced measures The graph is re- computed on each set of values in the similarity table

6. Many Different Biological Networks Microarray expression level data – relationships between different genes being expressed Protein-protein interactions Signal transduction networks Metabolic pathways Gene regulatory networks

7. Semantics for Graph Based Representations Protein Interaction database –Proteins as nodes, interactions as edges Metabolic network –Metabolites, enzymes and reactions as different types of nodes, connected by directed edges Semantics– does a node represent a protein or a metabolite? Does the edge mean “binds to” “produced by” or consumed by? ONDEX Approach- define typed nodes as “concepts “ and edges as semantically well-defined relations Generic graph-based representation of different types of biological data, resulting in ontologies. Data and metadata imported in a way that fits into the graph template through various parsers

8. Generalized data structure in ONDEX

9. Ondex “Special Sauce” Data schema that combines graph-based methods and generalized data structure(GDS), making use of ontologies and metadata. Core idea: store biological networks as graphs; use GDS to store related information (metadata)

10. Example: Nitrogen uptake in Arabidopsis Protein-protein interaction (TAIR) combined with co- expression data (ATTEDII) Clusters of nodes show interaction

11. Example: Drought stress in three cultivars of durum wheat over time Metabolic pathways and functional protein annotations Identification of functional orthologs to genes on Affymetrix chip Enzymes are significantly up or down regulated within Jasmonic acid biosynthesis pathway after 3 days of stress. Key: Squares: target sequence s, size proportion al to expressio n Red- up- regulation Green- down regulation Blue squares: ortholog proteins Blue circles reactions Pink squares- pathways

12. References www.ondex.orgwww.ondex.o Jacob Köhler et al., Graph-based analysis and visualization of experimental results with Ondex, Bioinformatics 22(11), 2006.Graph-based analysis and visualization of experimental results with Ondex Köhler et al.,Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures Ontoogy Workshop, Gottingen, 2004Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures