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Demonstration

Demonstration. Trupti Joshi Computer Science Department 317 Engineering Building North E-mail: joshitr@missouri.edu 573-884-3528(O). Examples. Microarray Data GeneSpring Functional Analysis Pathway Analysis. Microarray Data. DATA. Affymetrix Chips

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Demonstration

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  1. Demonstration Trupti Joshi Computer Science Department 317 Engineering Building North E-mail: joshitr@missouri.edu 573-884-3528(O)

  2. Examples • Microarray Data • GeneSpring • Functional Analysis • Pathway Analysis

  3. Microarray Data

  4. DATA • Affymetrix Chips -Experiments: 4mer,8mer,Chitin-Mix,Mock (Raw data, Expt Details, Gene-Chip Analysis, Processed data.txt) -3 Replicates each

  5. Affymetrix : Raw Data .CEL

  6. Affymetrix : Report .RPT

  7. Affymetrix : Processed Data .TXT

  8. Post- Normalization Calculations: Log Transformations and Fold Change Control

  9. GeneSpring Software • GeneSpring (Silicon Genetics) • Broadly used • Nice user interface • Data Normalization (Lowess, etc.) • Powerful ANOVA statistical analysis • t-test/1-way ANOVA test • 2-way ANOVA tests • 1-way post-hoc tests for reliably identifying differentially expressed genes • Incorporation of different analysis tools • Clustering • Visual filtering • Pathway viewing • Scripting

  10. Normalization in GeneSpring

  11. 8mer 4mer 0 1 5 60 0 308 11 Mix 8mer 4mer 2 2 10 109 1 555 22 Mix UP UP (Functions) Affymetrix Chitin Expts : GeneSpring Results

  12. Function Analysis : GO • Aim: To study functional categories distribution based on Gene Ontology Annotations in order to understand the genes and pathways involved in experimental conditions. • Three key parts: • gene name/id • GO term(s) • evidence for association

  13. 3 Ontologies • A gene product has one or more molecular functions and is used in one or more biological processes; it might be associated with one or more cellular components. • For example, the gene product cytochrome c can be described by the -molecular function termelectron transporter activity, -biological process termsoxidative phosphorylationandinduction of cell death, -cellular component termsmitochondrial matrixandmitochondrial inner membrane.

  14. Example Ontology

  15. How to access the GO and its annotations • 1. Downloads • Ontologies – (various – GO, OBO, XML, OWL MySQL) • Annotations – gene association files • Ontologies and Annotations – MySQL and XML • 2. Web-based access • AmiGO (http://www.godatabase.org) • QuickGO (http://www.ebi.ac.uk/ego)

  16. What can scientists do with GO? • Access gene product functional information • Provide a link between biological knowledge and … • gene expression profiles • proteomics data • Find how much of a proteome is involved in a process/ function/ component in the cell • using a GO-Slim (a slimmed down version of GO to summarize biological attributes of a proteome) • Map GO terms and incorporate manual GOA annotationinto own databases • to enhance your dataset • or to validate automated ways of deriving information about gene function (text-mining).

  17. GO for microarray analysis • Annotations give ‘function’ label to genes • Ask meaningful questions of microarray data e.g. • genes involved in the same process, same/different expression patterns?

  18. Microarray analysis Whole genome analysis (J. D. Munkvold et al., 2004)

  19. Function Distribution of All Annotated Arabidopsis Genes

  20. GO Biological Process

  21. MIPS Function

  22. GO FUNCTIONS WS 5hr Sample 1 : 0-3 mm Sample 2 : 3-11 mm * Numbers of genes observed are shown in brackets

  23. component process function Gene experimental condition GO for microarray analysis

  24. time Defense response Immune response Response to stimulus Toll regulated genes JAK-STAT regulated genes Puparial adhesion Molting cycle hemocyanin Amino acid catabolism Lipid metobolism Peptidase activity Protein catabloism Immune response Immune response Toll regulated genes control attacked MicroArray data analysis Bregje Wertheim at the Centre for Evolutionary Genomics, Department of Biology, UCL and Eugene Schuster Group, EBI.

  25. Color indicates up/down regulation Apotosis Regulator Red: up by 1.5 fold Blue: down 1.5 fold GoMiner Tool, John Weinstein et al, NCI: Genome Biol. 4 (R28) 2003

  26. KEGG Pathways Analysis • List of Arabidopsis genes assigned to KEGG Pathways acquired • UP or DOWN regulated genes mapped to Pathways

  27. Red : 5hr 0-3mm Blue : 5hr 3-11 mmPurple : 48hr 0-3mmGreen : 48 hr 3-11mm AT5G08300 ; AT2G05710; AT2G05710; AT4G35830; AT2G05710; AT2G42790 AT5G43330; AT3G47520 ; AT5G09660; AT3G15020 AT1G65930; AT5G14590; AT2G47510 AT5G08300 AT3G55410; AT3G55410; AT3G55410 AT5G55070

  28. Red : 5hr 0-3mm Blue : 5hr 3-11 mmPurple : 48hr 0-3mmGreen : 48 hr 3-11mm AT1G72330 AT4G24830 AT3G47340 AT5G65010

  29. Examples • Microarray Data • GeneSpring • Functional Analysis • Pathway Analysis

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