Winter school 2026

January 2026- Advanced methods in microbial community analysis

Microbiome scientists can use a variety of sequencing methods to explore the composition, diversity, and function of a microbial community. There is no standard process for working raw data into insights about how microbiomes function. The choices a researcher makes in tailoring a bioinformatic pipeline to a dataset, or in performing a statistical analysis, depend greatly on the system, the characteristics of the dataset, and the scientific question.

In this 2-week course, we will discuss study design, compare sequencing methods, and examine statistical methods for interpreting sequencing data.

Target Audience

This course is intended for students who already have some experience with analysing microbiome data, and who wish to gain a deeper understanding of the methods that will help them answer their research questions. We will examine alternative approaches and address common pitfalls. 

  • During week 1, students will learn about the theory and application of 16S rRNA amplicon, metagenomics, and metatranscriptomic sequencing, as well as functional annotation and machine learning, through lectures and hands-on exercises. 
  • During week 2, students will put these methods to use by re-using published sequencing datasets in small-group projects. 

This course is designed for students and postdocs: 

  • Whose research includes microbiomes 
  • Who understand the basics of DNA or RNA sequencing analysis
  • Who have experience with scripting in R and RStudio

Location and Dates

Location: Lausanne, UNIL Campus

Dates 12-23 January

Cost: The course itself is free; participants are responsible for covering their accommodation and meal expenses.

Course Details

Purpose of the course: To teach methods in analysing and re-using microbiome sequencing data, with emphasis on:

  • Choosing the appropriate experimental design and data analysis methods for a particular system and question
  • Anchoring analytical outputs in microbiology
  • Performing downstream statistical methods for comparing microbial communities or characterising changes to a microbiome
  • Identifying challenges and opportunities in re-using data

Outcomes. By the end, students will be able to…

  • Understand the principles of designing experiments to characterise microbial communities
  • For 16S and whole-genome metagenomic, as well as transcriptomic data:
    1. Understand how steps in a bioinformatic pipeline affect the resulting taxa and gene counts
    2. Use statistical methods to look for differences/changes in microbiomes composition or activity
  • Formulate research question that are suitable for data re-use
  • Contribute to the development of data re-use practices in the microbiomes field.

Program

  • Week 1, 12-16 January: Lecture and hands-on exercises to develop a deep understanding of microbiome sequencing data
    • Experimental design, power analysis
    • 16S and whole-genome metagenomics
    • Functional annotation
    • Machine learning
    • Metatranscriptomics
  • Weekend, 17-18 January: Day trips to socialise and explore the outdoors
  • Week 2, 19-23 January: Student projects to reinforce the methods learned during Week 1
    • Reproduce a figure from a published study
    • Formulate a new research question and conduct an original analysis of published data
    • Assess the re-usability of published datasets

 

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