Mark Robinson: Katalogdaten im Herbstsemester 2016 |
| Name | Herr Prof. Dr. Mark Robinson (Professor Universität Zürich (UZH)) |
| Adresse | Universität Zürich Winterthurerstrasse 190 Inst. Molecular Life Sciences 8057 Zürich SWITZERLAND |
| Telefon | 044 635 48 48 |
| mark.robinson@math.ethz.ch | |
| Departement | Mathematik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics  | 0 KP | 1K | M. Kalisch, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl | |
| Kurzbeschreibung | Etwa 5 Vorträge zur angewandten Statistik. | ||||
| Lernziel | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten. | ||||
| Inhalt | In etwa 5 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen. | ||||
| Voraussetzungen / Besonderes | Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm: http://stat.ethz.ch/events/zukost Lehrsprache ist Englisch oder Deutsch je nach ReferentIn. | ||||
| 401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: STA426 Beachten Sie die Einschreibungstermine an der UZH: http://www.uzh.ch/studies/application/mobilitaet.html | 5 KP | 3G | H. Rehrauer, M. Robinson | |
| Kurzbeschreibung | A range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments. | ||||
| Lernziel | -Understand the fundamental "scientific process" in the field of Statistical Bioinformatics -Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave) -Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data -Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data -Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project -Gain the ability to critical assess the statistical bioinformatics literature -Write a coherent summary of a bioinformatics problem and its solution in statistical terms | ||||
| Inhalt | Lectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification | ||||
| Skript | Lecture notes, published manuscripts | ||||
| Voraussetzungen / Besonderes | Prerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data | ||||
| 551-1295-00L | Introduction to Bioinformatics: Concepts and Applications | 6 KP | 4G | W. Gruissem, K. Bärenfaller, A. Caflisch, G. Capitani, J. Fütterer, M. Robinson, A. Wagner | |
| Kurzbeschreibung | 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. | ||||
| Lernziel | 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. | ||||
| Inhalt | 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 | ||||