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Transcriptomics

Area Chair: Martin Vingron Max-Planck-Institute for Molecular Genetics, Berlin, Germany Presentation: Thomas Lengauer Max-Planck-Institute for Informatics, Saarbücken, Germany. Transcriptomics. Transcriptomics = Study of transcriptional products .

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Transcriptomics

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  1. Area Chair: Martin VingronMax-Planck-Institute for Molecular Genetics, Berlin, Germany Presentation: Thomas Lengauer Max-Planck-Institute for Informatics, Saarbücken, Germany Transcriptomics

  2. Transcriptomics = Study of transcriptional products • Determination of mRNA levels, i.e. expression profiling • Gene structure, alternative splicing • Utilization of expression profiles for study of biological mechanisms, disease mechanisms • Application of DNA arrays in chromatin immuno precipitation – gene regulation

  3. Technologies I • Tagging the mRNA: ESTs, SAGE • Quantitative PCR

  4. Technologies II: Array based • cDNA arrays, long oligo arrays: immobilize a piece of DNA per gene. These are (usually) 2-color arrays, i.e. two samples are labeled with different dyes and hybridized • Short oligo arrays (Affymetrix): immobilize several short oligonucleotides per gene. These are 1-color arrays, i.e. one sample is hybridized at a time • Tiling arrays: spots do not correspond to genes. Instead representative sequences for whole genomic regions are spotted

  5. Questions I • Experimental design: How to get the most information out of the least number of hybridizations? - Paper by Woo et al: Experimental Design for Three-Color and Four-Color Gene Expression Microarrays

  6. Questions II • What is the product of transcription? • Gene structure and alternative splicing: Paper by Cline et al: A Statistical Method for Detecting Splice Variants from Expression Data • Tiling arrays: Originally used for unbiased detection of transcription. Now being used for identifying transcription factor binding sites, see paper by Li et al: A Hidden Markov Model for Analyzing ChIP-chip Experiments on Genome Tiling Arrays and its Application to p53 Binding Sequences

  7. Questions III • Use expression profiles to characterize, e.g., • Developmental states • Disease states • Leads to classification problem: Paper by Soukup et al: Robust Classification Modeling on Microarray Data Using Misclassification Penalized Posterior

  8. Questions IV • Common change – common regulation? • Clustering, coexpression: Paper by by Dueck et al: Multi-way clustering of Microarray Data using Probabilistic Sparse Matrix Factorization • Is coexpression mediated by the same transcription factor? Compare also paper on regulation by Li et al

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