636-0101-00L Systems Genomics
| Semester | Spring Semester 2020 |
| Lecturers | N. Beerenwinkel, C. Beisel, S. Reddy |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 636-0101-00 G | Systems Genomics The lecture is being recorded. Lecture: Wednesday 11-13 Tutorial: Wednesday 17-18 | 3 hrs |
| N. Beerenwinkel, C. Beisel, S. Reddy |
Catalogue data
| Abstract | 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 |
| Objective | 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). |
| Content | 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. Main Topics: -- Next-generation sequencing -- Transcriptomics -- 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. |
| Lecture notes | The PowerPoint presentations of the lectures as well as other course material relevant for an active participation will be made available online. |
| Literature | -- 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 |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 4 credits |
| Examiners | N. Beerenwinkel, C. Beisel, S. Reddy |
| Type | end-of-semester examination |
| Language of examination | English |
| Repetition | A repetition date will be offered in the first two weeks of the semester immediately consecutive. |
| Additional information on mode of examination | Written examination, 90 Minutes Examination will take place on Thursday, June 4, 9-11am in Basel |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
Offered in
| Programme | Section | Type | |
|---|---|---|---|
| Biotechnology Master | Core Courses | O |  |
| Computational Biology and Bioinformatics Master | Data Science | W | |