Mark Robinson: Catalogue data in Autumn Semester 2014 |
Name | Prof. Dr. Mark Robinson (Professor Universität Zürich (UZH)) |
Address | Universität Zürich Winterthurerstrasse 190 Inst. Molecular Life Sciences 8057 Zürich SWITZERLAND |
Telephone | 044 635 48 48 |
mark.robinson@math.ethz.ch | |
Department | Mathematics |
Relationship | Lecturer |
Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|
401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, P. L. Bühlmann, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer | |
Abstract | About 5 talks on applied statistics. | ||||
Objective | See how statistical methods are applied in practice. | ||||
Content | There will be about 5 talks on how statistical methods are applied in practice. | ||||
Prerequisites / Notice | This is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web: http://stat.ethz.ch/events/zukost Course language is English or German and may depend on the speaker. | ||||
401-6282-00L | Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data | 5 credits | 3G | H. Rehrauer, M. Robinson | |
Abstract | The lecture discusses the complete analysis of microarray and short-read sequencing data and covers the dedicated methods of data preprocessing, data exploration, inference, classification, and functional analysis. It treats especially the application of statistical methods in the situation where many variables are measured for few subjects and where many hypothesis tests are run on the same data. | ||||
Objective | The students learn the characteristics of microarray and short-read sequencing data. They learn how to process, inspect and analyze the data with R. They understand the statistical principles underlying the various processing algorithms. | ||||
Content | Microarrays and the latest Short-Read Sequencing technologies are the main workhorses to gain insight in the RNA and DNA world of cells and tissues. The main characteristic of both technologies is that they do not only measure single genes or genomic regions but can provide genome-wide measurements in a single experiment. They achieve this by a measurement process that is massively parallelized and can thus interrogate millions of sequences at the same time. The main application of microarrays is to measure the gene expression or gene activity which is frequently used to identify the changes of the gene activity in cells or tissues induced during development or external stimuli like drug treatments or environmental changes. Many other applications like, e.g. genotyping, do exist but are less frequent. For Short-Read Sequencing there is not yet a main application, it is equally well suited to • Measure gene expression • Identify transcript variants • Identify genome-wide transcription factor binding sites • De novo sequencing of new organisms • Resequencing of organisms • ... This lecture covers the statistical methods that are used to preprocess and analyze both types of data. All methods will be exemplified in the exercises using real-world data. The exercises will be conducted using the R programming language. Basic knowledge of the R programming language is required! The topics of the lecture are • Data preprocessing: Conversion of raw measurement data • Exploratory data analysis: Identification of the major data characteristics • Differential expression: Use hypothesis tests to identify changes in gene expression • Transcript variation: Identification of alternative usage of the same genomic locus • ChIP-chip or ChIP-seq: Identify genomic regions that are enriched in samples • RNA-seq: Analysing digital expression counts and determining expression of transcript variants • Classification: Using expression data to build predictive models • Functional analysis: Mapping genes or genomic regions to biological annotation like functional categories or pathways The lecture is relevant for everybody who has an interest in the areas of applied statistics, bioinformatics or molecular life science. | ||||
551-1295-00L | Introduction to Bioinformatics: Concepts and Applications | 6 credits | 4G | W. Gruissem, K. Bärenfaller, A. Caflisch, G. Capitani, J. Fütterer, M. Robinson, A. Wagner | |
Abstract | Storage, handling and analysis of large datasets have become essential in biological research. The course will introduce students to a number of applications of bioinformatics in biology. Freely accessible software tools and databases will be explained and explored in theory and praxis. | ||||
Objective | Introduction to Bioinformatics I: Concepts and Applications (formerly Bioinformatics I) will provide students with the theoretical background of approaches to store and retrieve information from large databases. Concepts will be developed how DNA sequence information can be used to understand phylogentic relationships, how RNA sequence relates to structure, and how protein sequence information can be used for genome annotation and to predict protein folding and structure. Students will be introduced to quantitative methods for measuring gene expression and how this information can be used to model gene networks. Methods will be discussed to construct protein interaction maps and how this information can be used to simulate dynamic molecular networks. In addition to the theoretical background, the students will develop hands-on experiences with the bioinformatics methods through guided exercises. The course provides students from different backgrounds with basic training in bioinformatics approaches that have impact on biological, chemical and physics experimentation. Bioinformatics approaches draw significant expertise from mathematics, statistics and computational science. Although "Intoduction to Bioinformatics I" will focus on theory and praxis of bioinformatics approaches, the course provides an important foundation for the course "Introduction to Bioinformatics II: Fundamentals of computer science, modeling and algorithms" that will be offered in the following semester. | ||||
Content | Bioinformatics I will cover the following topics: From genes to databases and information BLAST searches Prediction of gene function and regulation RNA structure prediction Gene expression analysis using microarrays Protein sequence and structure databases WWW for bioinformatics Protein sequence comparisons Proteomics and de novo protein sequencing Protein structure prediction Cellular and protein interaction networks Molecular dynamics simulation |