Christian Beisel: Katalogdaten im Frühjahrssemester 2020
|Herr Dr. Christian Beisel
Genomics Facility Basel
ETH Zürich, BSS D 43.3
|+41 61 387 31 65
|N. Beerenwinkel, C. Beisel, S. Reddy
|This lecture course is an introduction to Systems Genomics. It addresses how fundamental questions in biological systems are studied and how the resulting data is statistically analyzed in order to derive predictive mathematical models. The focus is on viewing biology from a genomic perspective, which requires high-throughput experimental methods (e.g., RNA-seq, genome-scale screening, single-cell
|The goal of this course is to learn how a detailed quantitative description of genome biology can be employed for a better understanding of molecular and cellular processes and function. Students will learn fundamental questions driving the field of Systems Genomics. They will also be introduced to traditional and advanced state-of-the-art technologies (e.g., CRISPR-Cas9 screening, droplet-microfluidic sequencing, cellular genetic barcoding) that are used to obtain quantitative data in Systems Genomics. They will learn how to use these data to develop mathematical models and efficient statistical inference algorithms to recognize patterns, molecular interrelationships, and systems behavior. Finally, students will gain a perspective of how Systems Genomics can be used for applied biological sciences (e.g., drug discovery and screening, bio-production, cell line engineering, biomarker discovery, and diagnostics).
|Lectures in Systems Genomics will alternate between lectures on (i) biological questions, experimental technologies, and applications, and (ii) statistical data analysis and mathematical modeling. Selected complex biological systems and the respective experimental tools for a quantitative analysis will be presented. Some specific examples are the use of RNA-sequencing to do quantitative gene expression profiling, CRISPR-Cas9 genome scale screening to identify genes responsible for drug resistance, single-cell measurements to identify novel cellular phenotypes, and genetic barcoding of cells to dissect development and lineage differentiation.
-- Next-generation sequencing
-- Biological network analysis
-- Functional and perturbation genomics
-- Single-cell biology and analysis
-- Genomic profiling of the immune system
-- Genomic profiling of cancer
-- Evolutionary genomics
-- Genome-wide association studies
Selected genomics datasets will be analyzed by students in the tutorials using the statistical programming language R and dedicated Bioconductor packages.
|The PowerPoint presentations of the lectures as well as other course material relevant for an active participation will be made available online.
|-- Do K-A, Qin ZS & Vannucci M (2013) Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data, Cambridge University Press
-- Klipp E. et al (2009) Systems Biology, Wiley-Blackwell
-- Alon U (2007) An Introduction to Systems Biology, Chapman & Hall
-- Zvelebil M & Baum JO (2008) Understanding Bioinformatics, Garland Science
|Lab Course: Next-Generation Sequencing
|C. Beisel, S. Reddy
|The Lab Course will take place Monday/Tuesday 9-17h, 10 days in total, start of this lab course is on Monday, September 25 2017.
|Students shall obtain a basic understanding in NGS and its application in transcription profiling including theoretical considerations when starting an RNA-seq experiment and the practical hands-on work of library preparation and usage of bioinformatics tools for data analysis.
|Introduction to NGS technologies and applications. Design of an RNA-seq transcription profiling experiment. Specific treatment of cells (+/- signal-induction) and RNA extraction. Handling and quality control of RNA samples. Sequencing library preparation starting with total RNA. Quality control and quantification of the libraries. Setup of an NGS run and sequencing of the prepared RNA-seq libraries using the NextSeq 500 system. Analysis of the generated sequence data: sequence data QC, criteria for run performance and quality of data; pre-processing of the raw data; mapping sequence reads to a reference sequence; quantification of transcript abundance and differential gene expression.
|Material will be provided during the course
|Sara Goodwin, John D. McPherson & W. Richard McCombie. Coming of age: ten years of next-generation sequencing technologies. Nature Reviews Genetics 17, 333-351 (2016)
Zhong Wang, Mark Gerstein & Michael Snyder. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10, 57-63 (January 2009)
Fatih Ozsolak & Patrice M. Milos. RNA sequencing: advances, challenges and opportunities. Nature Reviews Genetics 12, 87-98 (February 2011)
Ana Conesa, Pedro Madrigal, Sonia Tarazona et al. A survey of best practices for RNA-seq data analysis. Genome Biology 2016 17:13.