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SRM UNIVERSITY FACULTY OF ENGINEERING & TECHNOLOGY DEPARTMENT OF BIOINFORMATICS BI0510- MICROARRAY BIOINFORMATICS LECTURE PLAN Class & Sem: M.Tech & II sem Subject code: BI0 510 Lecture Hour 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. Staff Name:Mrs. S.Shobana Subject: MicroarrayBioinformatics Contents INSTRUCTIONAL OBJECTIVES DNA MicroArray: The Technical Foundations Why are MicroArray Important? What is a DNAMicroArray Microarray- Introduction types of Microarray Making microarrays Spotted arrays Insitu synthesized oligonucleotide arrays Steps involved in use of microarray Sample preparation and labeling, Hybridisation Washing, image aquisition Filtering of low complexity sequences cross-hybridization prediction Prediction of melting temperature Secondary structure prediction of probe Feature extraction steps Identifying positions of features Types of segmentation & Background pixel identification Data cleaning and transformation within array normalizationLoess regression Correcting Spatial effects between array normalization steps measuring and quantifying microarray variability Calibration experiments and pilot studies Learning Outcome o Introduction to the student about microarray technology and its uses. o It gives a brief description about the making and using of microarrays. o Also the different databases used for creating probes are taught. o In the method “insitu synthesized oligonucleotides” certain rules have to be followed for preparing probes which has been discussed in this unit. o Also feature extraction methods are discussed o DNA Microarray and its statistical analysis o Analysis of RNA data o Statistical computing and Statistical Genetics o Normalization techniques to correct systemic and spatial effects o Quantify non systemic variability 23. 24. 25. Method for measuring variability Analysis of differentially expressed genes fundamental concepts and hypothesis rules 26. 27. 28. Classic parametric tests- paired and unpaired. Non parametric tests- bootstrap analysis Multiplicity of testing- bonferroni adjustment and ANOVA Similarity analysis of relationships between genes using correlation coefficient, rank coefficient and Euclidean distance Hierarchial clustering & Linkage methods for clustering Machine learning methods of clustering classification of tissues and samples- methods Validation and cross validation Dimensionality reduction- PCA &Individual gene selection Pairwise gene selection,Voting algorithms, genetic algorithms UNIT V-EXPERIMENTAL DESIGN Blocking randomization & blindingchoice of technology using experimental designs Power analysis and confidence data standards- LIMS storage and sharing- MIAME, MAGE, Ontologies Future direction of microarray approach, Pharmacogenomics, Toxicogenomics Data mining 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. o Differentially expressed genes can be obtained from the numerical data and can be done using clustering algorithms. o Classification of tissues and genes using different algorithms and its validation. o Also microarray data is a high dimensional data- thus dimension reduction is discussed o The basic experimental design for a microarray experiment o The ways and means to store large microarray data is discussed. REFERENCES 1. Arun Jogota , Microarray Data Analysis and Visualization, The Bay Press, 2001. 2. Ernst Wit and John McClure, Statistics for Microarrays Design, Analysis and Inference, John Wiley & Sons, 2004. 3. Steen Knudsen, Guide to analysis of DNA Microarray data, John Wiley & Sons, 2004. 4. Dov Stekel , Microarray Bioinformatics, Cambridge University Press, 2003. 5. S. Draghic, Chapman, Data Analysis tools for DNA Microarray, Hall/ CRC Press, 2002. E Mail ID: [email protected] Contact No: 9841988577