Shinichi Sunagawa: Catalogue data in Autumn Semester 2021

Name	Prof. Dr. Shinichi Sunagawa
Field	Microbiome Research
Address	Institut für Mikrobiologie ETH Zürich, HCI F 417 Vladimir-Prelog-Weg 1-5/10 8093 Zürich SWITZERLAND
Telephone	+41 44 633 61 55
E-mail	ssunagawa@ethz.ch
URL	http://www.micro.biol.ethz.ch/research/sunagawa.html
Department	Biology
Relationship	Associate Professor

Number	Title	ECTS	Hours	Lecturers
551-1109-00L	Seminars in Microbiology	0 credits	2K	S. Sunagawa, W.‑D. Hardt, M. Künzler, J. Piel, J. Vorholt-Zambelli
Abstract	Seminars by invited speakers covering selected microbiology themes.
Learning objective	Discussion of selected microbiology themes presented by invited speakers.
551-1119-00L	Microbial Community Genomics Number of participants limited to 10. Prerequisite: Basic knowledge in [R] (e.g. indroductory course) and/or UNIX is required. Participants should bring their own laptop computer. The enrolment is done by the D-BIOL study administration. General safety regulations for all block courses: The COVID certificate is mandatory at ETH Zurich. Only students who have a Covid certificate, i.e. who have been vaccinated, have recovered or have bee -Whenever possible the distance rules have to be respected -All students have to wear masks throughout the course. Please keep reserve masks ready. Surgical masks (IIR) or medical grade masks (FFP2) without a valve are permitted. Community masks (fabric masks) are not allowed. -The installation and activation of the Swiss Covid-App is highly encouraged -Any additional rules for individual courses have to be respected -Students showing any COVID-19 symptoms are not allowed to enter ETH buildings and have to inform the course responsible.	6 credits	7P	S. Sunagawa
Abstract	Introduction to current research methods in the analysis of microbial communities using Next Generation Sequencing approaches - metagenomics. Practical experience of work in a computational laboratory and an introduction to scientific programming.
Learning objective	Gain skills in data analysis and presentation for oral and written reports. Lectures introducing state-of-the-art in respective research areas and community microbiology, which is the target of ongoing research. Start to assess current literature.
Prerequisites / Notice	Basic knowledge in [R] (e.g. indroductory course) and/or UNIX is required. Participants should bring their own laptop computer.
551-1299-00L	Introduction to Bioinformatics	6 credits	4G	S. Sunagawa, M. Gstaiger, A. Kahles, G. Rätsch, B. Snijder, E. Vayena, C. von Mering, N. Zamboni
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.