Genetic Diversity: Analysis
| Course Catalogue | 701-1425-00L |
| Credit Points | 2 ECTS |
| Dates | 15.06.26 to 26.06.26 (WK25 & WK26) |
| Organizer | GDC, D-USYS, ETH Zurich |
| Location | CHN F42 |
| Timetable | View schedule |
Handouts
Will be added once the course has started.
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 and analysing data, and through this course we aim to share that expertise with students.
Overview
Many biologists enter the field of bioinformatics without formal training, lacking the foundational skills needed to work with genomic data. This course is designed to bridge that gap, equipping students with essential knowledge and hands-on experience while placing a strong emphasis on reproducibility and good research practices.
The course begins with Linux training, covering both local and remote computing environments. It then moves into core bioinformatics topics, including data analysis in R and job submission on high-performance computing (HPC) clusters. The course places particular emphasis on massively parallel sequencing (MPS) methods, covering quality control and filtering, genome assembly, sequence alignment, and data workflows for RNA-Seq, SNPs, RADseq, AmpSeq and metagenomics. Throughout, students engage with current research literature to sharpen their critical thinking and learn to evaluate bioinformatic findings in a scientific context.
Course Objectives
This course equips students with the practical skills needed to handle and analyse genomic data from high-throughput sequencing experiments. The emphasis is on reproducibility, critical evaluation of common workflows, and clear scientific communication. A structured self-study component with defined learning objectives reinforces key concepts and encourages independent thinking.
Success in this course depends on active engagement, open discussion and genuine curiosity. Not copying and pasting.
Our Guiding Principle
We believe that students should be active thinkers, not passive followers. This means questioning established methods, understanding the rationale behind each step, and evaluating whether a given approach is appropriate for the research context. Rather than blindly repeating workflows, we encourage students to make informed decisions and adapt methods to their own data and scientific goals. These are the skills needed to navigate the real complexities of genetic data analysis.
Important Note: On-Site Only
On-Site Attendance Required
This is an on-site course with a 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 written report.
Instructors
- Jean-Claude Walser (jean-claude.walser[🙈]usys.ethz.ch)
- Niklaus Zemp (niklaus.zemp[🙉]usys.ethz.ch)