Shinichi Sunagawa: Catalogue data in Autumn Semester 2021

Name Prof. Dr. Shinichi Sunagawa
FieldMicrobiome 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-mailssunagawa@ethz.ch
URLhttp://www.micro.biol.ethz.ch/research/sunagawa.html
DepartmentBiology
RelationshipAssociate Professor

NumberTitleECTSHoursLecturers
551-1109-00LSeminars in Microbiology0 credits2KS. Sunagawa, W.‑D. Hardt, M. Künzler, J. Piel, J. Vorholt-Zambelli
AbstractSeminars by invited speakers covering selected microbiology themes.
ObjectiveDiscussion of selected microbiology themes presented by invited speakers.
551-1119-00LMicrobial Community Genomics Restricted registration - show details
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 credits7PS. Sunagawa
AbstractIntroduction 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.
ObjectiveGain 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 / NoticeBasic knowledge in [R] (e.g. indroductory course) and/or UNIX is required. Participants should bring their own laptop computer.
551-1299-00LIntroduction to Bioinformatics Restricted registration - show details 6 credits4GS. Sunagawa, M. Gstaiger, A. Kahles, G. Rätsch, B. Snijder, E. Vayena, C. von Mering, N. Zamboni
AbstractThis 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.
ObjectiveThe 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.
ContentEthics:
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 / NoticeCourse 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.