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
Telephone044 635 48 48
E-mailmark.robinson@math.ethz.ch
DepartmentMathematics
RelationshipLecturer

NumberTitleECTSHoursLecturers
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. 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
AbstractAbout 5 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 5 talks on how statistical methods are applied in practice.
Prerequisites / NoticeThis 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.
401-6282-00LStatistical Methods for the Analysis of Microarray and Short-Read Sequencing Data5 credits3GH. Rehrauer, M. Robinson
AbstractThe 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.
ObjectiveThe 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.
ContentMicroarrays 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.
551-1295-00LIntroduction to Bioinformatics: Concepts and Applications Information 6 credits4GW. Gruissem, K. Bärenfaller, A. Caflisch, G. Capitani, J. Fütterer, M. Robinson, A. Wagner
AbstractStorage, 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.
ObjectiveIntroduction 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.
ContentBioinformatics 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