From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers via e-mail.

Andre Kahles: Catalogue data in Autumn Semester 2019

Name Dr. Andre Kahles
Professur für Biomedizininformatik
ETH Zürich, CAB F 52.2
Universitätstrasse 6
8092 Zürich
DepartmentComputer Science

261-5112-00LAdvanced Approaches for Population Scale Compressive Genomics Restricted registration - show details
Number of participants limited to 30.
3 credits2GA. Kahles
AbstractResearch in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherentcomplexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions.
ObjectiveThis interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it.
ContentOver the duration of the semester, the course will cover three main topics. Each of the topics will consist of 70-80% lecture content and 20-30% seminar content.
1) Algorithms and data structures for text and graph compression. Motivated through applications in compressive genomics, the course will cover succinct indexing schemes for strings, trees and general graphs, compression schemes for binary matrices as well as the efficient representation of haplotypes and genomic variants.
2) Stochastic data structures and algorithms for approximate representation of strings and graphs as well as sets in general. This includes winnowing schemes and minimizers, sketching techniques, (minimal perfect) hashing and approximate membership query data structures.
3) Data structures supporting encryption and data privacy. As an extension to data structures discussed in the earlier topics, this will include secure indexing using homomorphic encryption as well as design for secure storage and distribution of data.
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.
Case studies to learn about applying ethical principles in human genomics research

Genetic variant calling
Analysis and critical evaluation of genome wide association studies

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

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.