Evolva Biotech SA Microarray and Macro opportunities for Discovery informatics Head of Informatics Mobile

Locations Basel (HQ) Evolution, Screening Pre-Clinical, Commercial Copenhagen Genetic Analoging, Productionisation Hyderabad Libraries, Bioinformatics, Synthetic Chemistry

Agenda Introduction to DNA microarrays Also please refer Types of Microarray Steps in data analysis Opportunities

DNA Microarrays are simply small glass or silicon slides upon the surface of which are arrayed thousands of genes (usually between ,000) Expression level measurement of genes is done by a conventional DNA hybridization process Data are read using laser-activated fluorescence readers The process is automated with robots and software and hence “ultra-high throughput” What are DNA Microarrays?

Need for Microarrays  What genes are Present/Absent in a cell?  What genes are Present/Absent in the experiment vs. control?  Which genes have increased/decreased expression in experiment vs. control?  Which genes have biological significance?

Applications Discovery Leads PreClinical Clinical Target Discovery Target Validation Screening Validation Optimization Toxicology Optimization Genotyping ADME Screens

Types of Microarray “chips” Two major types: a.“Gene chips” from Affymetrix a. Test and control on different chips b. “Single channel” color c. A probe set, each of 25 oligos in length for a given gene a. Costs $500 or more per chip b.“Spotted, glass chips” originally from stanford a. Test and control on different chips b. “Dual channel” colors c. Probes are single stranded cDNAs of 20 to 100 bases or even longer d. Costs $10 per slide

Types …. Spotted cDNA chip Affy Gene chip

The 6 steps of a DNA microarray experiment (1-3) 1. Manufacturing of the microarray 2. Experimental design and choice of reference: what to compare to what? 3. Target preparation (labeling) and hybridization

The 6 steps of a microarray experiment (4-6) 4. Image acquisition (scanning) and quantification (signal intensity to numbers) 5. Database building, filtering and normalization 6. Statistical analysis and data mining

Frabrication…. cDNA / spotted chips Affymetrix gene chips

Gene chip…..

GeneChip ® Expression Analysis - Hybridization and Staining Array cRNA Target Hybridized Array Streptravidin- phycoerythrin conjugate

Steps in pics… - Quantification of RNA

Steps in pics… - RNA quality check

Steps in pics… - Hybridization Rotation: 60 rpm Temp: 45 C Time: 16hrs

Steps in pics… - Fluidics

Steps in pics… - Scanning

Steps in pics… - Scanning results

Steps in pics… - Data generation

Affymetrix Movie

cDNA or spotted chips

Spotted chips Movie

Spotted chip…

Spotted chip…Scanning results

Overall view of both types together…

Steps in microarray data analysis – Bioinformatics opportunities IMAGE ANALYSIS – assign the degree of expression of genes based on intensity STATISTICAL ANALYSIS – identify the differentially expressed genes (through statistical methods and through other bioinformatics methods) PATHWAY ANALYSIS – correlate the differentially regulated genes to biological context based pathways SYSTEMS BIOLOGY - explain the observed phenotypic (or macro-level) changes/effects at the organism level based on overall changes in “affected” pathways of various cells/tissues

From probe level signals to gene abundance estimates The job of the expression summary algorithm is to take a set of Perfect Match (PM) and Mis- Match (MM) probes, and use these to generate a single value representing the estimated amount of transcript in solution, as measured by that probeset. To do this,.DAT files containing array images are first processed to produce a.CEL file, which contains measured intensities for each probe on the array. It is the.CEL files that are analysed by the expression calling algorithm.

Gene A Over- expressed in normal tissue Gene B Over- expressed in tumour

Cy3Cy5 Cy3 Cy5 Cy3 log 2 Genes Experiments fold Underexpressed Overexpressed Image Analysis & Data Visualization

Two dimensional hierarchical (“Eisen”) Clustering

1.Experimental Design 2.Image Analysis – raw data 3.Normalization – “clean” data 4.Data Filtering – informative data 5.Model building 6.Data Mining (clustering, pattern recognition, et al) 7.Validation Microarray Data Process

Statistical Data Pre-processing Filtering Background subtraction Low intensity spots Saturated spots Low quality spots (ghost spots, dust spots etc) Normalization Housekeeping genes/ control genes

Data to information…..

Molecular Function = elemental activity/task the tasks performed by individual gene products; examples are carbohydrate binding and ATPase activity Biological Process = biological goal or objective broad biological goals, such as mitosis or purine metabolism, that are accomplished by ordered assemblies of molecular functions Cellular Component = location or complex subcellular structures, locations, and macromolecular complexes; examples include nucleus, telomere, and RNA polymerase II holoenzyme The 3 Gene Ontologies

Pathway Knowledgebase Tagging of gene expression data (from Microarray, SAGE, etc) onto the simple clickable pathway maps. In-silico manipulation of pathways – ie predict the alterations in expression levels in any given tissue or disease conditions Ease target prioritization

Systems Biology…..the holy grail..

Allen resident cell activation inflammatory cell influx Bill resident cell activation inflammatory cell influx 8% improvement in FEV121% improvement in FEV1

Job Opportunities Skills in - Image analysis, Statistics, IT, Instrumentation, Knowledge in - NETWORK AND SYSTEMS BIOLOGY Companies in India (South): Software & data analysis Strand Genomics ( ) Siri Technologies ( ) Ocimum Biosolutions ( ) Avesthagen (

Job Opportunities Network Biology Jubilant Biosys ( ) Genotypic tech. ( Connexious (( Kshema Tech. ( TATA infotech ( Molecular connections ( Others services Agilent ( ca05035.html) ca05035.html DS Image - microarray instrumentation (

The Future of DNA chips….

Protein chips…….. Thank you….