BioNetworkBioNetwork Biological Modeling and Analysis Biological Modeling and Analysis

Microarray and VisualizationMicroarray and Visualization

Data IntegrationData Integration

• Bioinformatics data is currently spread across Bioinformatics data is currently spread across the internet and throughout organizations with the internet and throughout organizations with different format.different format.

• With data integration, we integrate different With data integration, we integrate different data into our single data. data into our single data.

• With data integration, scientists can discover With data integration, scientists can discover relationship between genes, proteins, etc that relationship between genes, proteins, etc that enable them to make better and faster decision enable them to make better and faster decision about diseases and drug compoundsabout diseases and drug compounds

Data SourceData Source

• Large, complex data structures, reflecting the Large, complex data structures, reflecting the richness of the scientific concepts.richness of the scientific concepts.

• Bioinformatics data sources cover similar Bioinformatics data sources cover similar domains; such as genes, proteins, structures, domains; such as genes, proteins, structures, DNAs, or microarray results.DNAs, or microarray results.

• We need integrated view of all data sources We need integrated view of all data sources above that are relevant for a particular researchabove that are relevant for a particular research

• Data is often incomplete, different format and Data is often incomplete, different format and missing certain attributes.missing certain attributes.

MicroarrayMicroarray

• Form of an array for the purpose of Form of an array for the purpose of expression profiling, monitoring expression expression profiling, monitoring expression levels for thousands of genes levels for thousands of genes simultaneously simultaneously

Part I: Microarray of Hair and Skin Part I: Microarray of Hair and Skin Epidermis MelanocytesEpidermis Melanocytes

• Figure 1Figure 1Information

A. melanocytesB. muscleC. sebaceous gland D. hair shaftE. epidermisF. dermisG. subcutaneous tissueH. fatI. arteryJ. sweat glandK. hair follicleL. Pacinian corpuscle

Data SourceData Source

• Data is from Prof Des Tobin, School of Data is from Prof Des Tobin, School of Life SciencesLife Sciences

• Contains Contains ++ 24,000 genes 24,000 genes• Over express and Under express genesOver express and Under express genes• Data is incompleteData is incomplete

BioNetworkBioNetwork

• System ArchitectureSystem Architecture

Functional FunctionsFunctional Functions

• 1.Searching by given any keywords1.Searching by given any keywords

• 2.Searching by given multiple keywords using Boolean Operator2.Searching by given multiple keywords using Boolean Operator

• 3.Extract Gene Information3.Extract Gene Information

• 4.Over In expression and Only in expression4.Over In expression and Only in expression

• 5.Save the search result back to Excel Format5.Save the search result back to Excel Format

• 6.Excel Reader6.Excel Reader

• 7.Connection to Public Microarray Database (Gene Onthology, GenBank, 7.Connection to Public Microarray Database (Gene Onthology, GenBank, Kegg)Kegg)

Part II: Modeling and AnalysisPart II: Modeling and Analysis

• Purpose: to Identify and Modeling GenesPurpose: to Identify and Modeling GenesPreparations

Background AdjustmentNormalization

Summary

Differential ExpressionOver expressed geneUnder expressed gene

Pearson CalculationCo-expression of gene

Biology NetworkScale free networkRandom Network

PreparationPreparation

• Data Source : Lung (Data Source : Lung (http://genome-www5.stanford.eduhttp://genome-www5.stanford.edu))

• Normalization BackgroundNormalization Background

• Fortunately, all data downloaded are Fortunately, all data downloaded are normalizednormalized

x-µλ

DifferentialDifferential Expression Expression

• Over express p>0 Over express p>0 • Under express p<0Under express p<0

Co-expressionCo-expression

• Similarity between each genes either under or over expressSimilarity between each genes either under or over express• Pearson Correlation Pearson Correlation • Here is how to interpret correlationsHere is how to interpret correlations::• -1.0 To -0.7 strong negative associations.-1.0 To -0.7 strong negative associations.• -0.7 To -0.3 weak negative association.-0.7 To -0.3 weak negative association.• Modeling and AnalysisModeling and Analysis• -0.3 to +0.3 little or no association.-0.3 to +0.3 little or no association.• +0.3 to +0.7 weak positive association.+0.3 to +0.7 weak positive association.• +0.7 to +1.0 strong positive association.+0.7 to +1.0 strong positive association.

VisualizationVisualization

• Matrix of MicroarrayMatrix of Microarray• Biology Network (random, free scale Biology Network (random, free scale

network)network)

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BioNetworkBioNetwork Biological Modeling and Analysis Biological Modeling and Analysis

Frederik Surya TjoeFrederik Surya Tjoe