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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

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 are self-taught in bioinformatics and often lack basic skills in handling genomic data. This course bridges this gap by providing the core knowledge and practical experience needed to effectively process genetic data, with a strong emphasis on reproducible research practices.

This introductory bioinformatics course provides students with the essential skills and concepts needed to work with genomic data. It begins with basic Linux training, where students learn to navigate both local and remote terminals. The course then introduces the fundamentals of bioinformatics, covering data analysis in R and the use of high performance computing (HPC) clusters for large-scale bioinformatics tasks.

A major focus is on massively parallel sequencing (MPS) techniques, including quality control (QC) and quality filtering (QF) to ensure data integrity. Students will explore genome analysis, sequence alignment, data preparation and interpretation for RNA-Seq, SNPs, RADseq, AmpSeq and metagenome projects. In addition, the course includes critical discussions of recent research papers to help students develop analytical skills to evaluate findings in the field.

Course Objectives

This course trains participants in the handling and analysis of genomic data, with a focus on high-throughput sequencing. Emphasis is placed on reproducibility, report writing and re-evaluation of common methods. A self-study component with clear learning objectives allows students to apply theoretical concepts. This course is not a copy-paste exercise - active participation, questions and discussion are essential.

Our Guiding Principle

We encourage students to be active thinkers rather than passive recipients of information. This means engaging deeply with the material, questioning established approaches and not simply following the path of others. Rather than simply repeating steps, students should focus on understanding the purpose behind each method and consider whether it fits with their specific research questions and data. This approach encourages critical thinking, independent decision-making and adaptability - key skills for navigating the complexities of genetic data analysis.

Course Format

The course runs for two weeks, giving students time to absorb and apply what they've learned at their own pace. Our aim is for students to understand and engage with the applications in a thoughtful way.

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)