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Transcriptomics Technologies and Applications: Insights from Research Studies

Explore the study of transcriptional products, gene structures, and mRNA levels in transcriptomics. Learn about technologies like DNA arrays, ESTs, and tiling arrays in gene expression profiling. Discover how to analyze expression profiles for biological and disease mechanisms. Delve into research on experimental design, alternative splicing, and coexpression in transcriptomics.

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Transcriptomics Technologies and Applications: Insights from Research Studies

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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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