551-1299-00L Introduction to Bioinformatics
| Semester | Autumn Semester 2019 |
| Lecturers | S. Sunagawa, M. Gstaiger, A. Kahles, G. Rätsch, B. Snijder, E. Vayena, C. von Mering, N. Zamboni |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 551-1299-00 G | Introduction to Bioinformatics Lecture: Mo 15-17 Exercises: Mo 17-19 | 4 hrs |
| S. Sunagawa, M. Gstaiger, A. Kahles, G. Rätsch, B. Snijder, E. Vayena, C. von Mering, N. Zamboni |
Catalogue data
| Abstract | This course introduces principle concepts, the state-of-the-art and methods used in some major fields of Bioinformatics. Topics include: genomics, metagenomics, network bioinformatics, and imaging. Lectures are accompanied by practical exercises that involve the use of common bioinformatic methods and basic programming. |
| Learning objective | The course will provide students with theoretical background in the area of genomics, metagenomics, network bioinformatics and imaging. In addition, students will acquire basic skills in applying modern methods that are used in these sub-disciplines of Bioinformatics. Students will be able to access and analyse DNA sequence information, construct and interpret networks that emerge though interactions of e.g. genes/proteins, and extract information based on computer-assisted image data analysis. Students will also be able to assess the ethical implications of access to and generation of new and large amounts of information as they relate to the identifiability of a person and the ownership of data. |
| Content | Ethics: Case studies to learn about applying ethical principles in human genomics research Genomics: Genetic variant calling Analysis and critical evaluation of genome wide association studies Metagenomics: Reconstruction of microbial genomes Microbial community compositional analysis Quantitative metagenomics Network bioinformatics: Inference of molecular networks Use of networks for interpretation of (gen)omics data Imaging: High throughput single cell imaging Image segmentation Automatic analysis of drug effects on single cell suspension (chemotyping) |
| Prerequisites / Notice | Course participants have already acquired basic programming skills in Python and R. Students will bring and work on their own laptop computers, preferentially running the latest versions of Windows or MacOSX. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 6 credits |
| Examiners | S. Sunagawa, M. Gstaiger, A. Kahles, G. Rätsch, B. Snijder, E. Vayena, C. von Mering, N. Zamboni |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit. |
| Mode of examination | written 150 minutes |
| Additional information on mode of examination | The exam will take place on a computer. Lectures will be accompanied by exercises. |
| Written aids | None |
| Digital exam | The exam takes place on devices provided by ETH Zurich. |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| Priority | Registration for the course unit is only possible for the primary target group |
| Primary target group | Interdisciplinary Sciences MSc (507000)
Interdisciplinary Sciences (Phys. Chem.) BSc (531000) Interdisciplinary Sciences (Biochem. Phys.) BSc (531100) Biology BSc (552300) Biology MSc (562000) Doctorate Biology (564002) Doctorate Biology ETH-UZH (565000) |
