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Genetic Diversity: Analysis

  • Course Catalogue Number: 701-1425-00L
  • Credit Points: 2 ECTS
  • Date: 16.06.25 - 27.06.25 (WK25 & WK26)
  • Organizer: GDC, D-USYS, ETH Zurich
  • Location: CHN E42 / F42

Handouts

The Genetic Diversity: Analysis course is organised by the Genetic Diversity Centre (GDC), a knowledge and technology platform within the D-USYS department at ETH Zurich. The GDC offers two courses per year: Genetic Diversity: Techniques, focusing on molecular laboratory techniques, and Genetic Diversity: Analysis, dedicated to the analysis of sequencing data.

For over a decade, the GDC has supported researchers in designing experiments, acquiring data and analysing it. Through this course, we aim to share our expertise with young scientists.

Overview

Many biologists enter the field of bioinformatics without any formal training, often lacking the fundamental skills required to work with genomic data. This course is designed to address this issue by equipping students with the essential knowledge and hands-on experience they need, while placing a strong emphasis on reproducibility and good research practices.

Students start with basic Linux training, learning to navigate local and remote computing environments. Building on this, the course covers core bioinformatics concepts, such as data analysis in R and using high-performance computing (HPC) clusters for large-scale tasks.

There is a particular focus on massively parallel sequencing (MPS) methods. Topics covered include quality control and filtering, genome analysis, sequence alignment and data workflows for RNA-Seq, SNPs, RADseq, AmpSeq and metagenomics. Throughout the course, students engage with current research literature to develop their critical thinking skills and learn to evaluate bioinformatic findings in a scientific context.

Course Objectives

This course provides participants with the practical skills needed to handle and analyse genomic data, focusing on high-throughput sequencing techniques. The course emphasises reproducibility, the critical evaluation of common workflows and clear scientific communication through reporting. A structured self-study element with defined learning objectives reinforces key concepts and encourages independent learning.

Active engagement, thoughtful questions and open discussion are essential for success in this course, which is not based on copying and pasting.

Our Guiding Principle

We believe that students should be active thinkers, not passive followers. This involves questioning established methods, understanding the rationale behind each step and evaluating whether a given approach is appropriate for the research context. Instead of blindly repeating workflows, we encourage students to think critically, make informed decisions and adapt methods to their own data and scientific goals. These are the essential skills needed to navigate the complexities of analysing genetic data in the real world.

Course Format

The course runs over two weeks, giving students time to absorb the material and apply what they’ve learned at their own pace. The focus is on thoughtful engagement with the methods and understanding the reasoning behind each step, rather than simply following instructions.

Important Note: On-Site Only

This is an on-site course. Due to its highly interactive format — including hands-on exercises, in-person discussions, and collaborative work — remote participation is not possible. Students are expected to attend in person for the full duration of the course.

Guests are welcome to join on individual days, space permitting, but this does not count toward ECTS credit.

Credit Points

Successful completion is worth 2 ECTS and requires a data analysis project with a detailed report.

Instructors

  • Jean-Claude Walser (jean-claude.walser[🙈]usys.ethz.ch)
  • Niklaus Zemp (niklaus.zemp[🙉]usys.ethz.ch)