Teaching

1. Analysis of High-Throughput Sequencing RNA-Seq Data (PhD course)

Role: Co-instructor

Lecture: Interactive teaching focusing on biological interpretation (Q&A format) of RNA‑Seq bioinformatic results.

Lab:

  • HPC environment setup
  • Running Nextflow-based nf-core/rnaseq pipelines
  • Interpretation of QC metrics, alignment statistics, and expression matrices
  • Downstream analysis in R: DESeq2, GO & KO enrichment, and biological result validation

2. Genome-wide Predictions in Breeding: Genotype–Phenotype Associations and Genomic Selection (PhD course)

Role: Co-instructor / Bioinformatics mentor

Lecture: Introduction to Linux, R & RStudio

Lab:

  • Hands on Linux & R introduction
  • Setup for the GWAS analysis: installation of bioinformatics tools and R packages/functions

Topics covered:

  • Statistical genomics foundations
  • SNP calling and variant quality assessment
  • GWAS workflows and visualization
  • Genomic prediction using mixed models and machine‑learning approaches
  • Interpretation of genotype–phenotype associations
  • Best practices for reproducible breeding informatics pipelines

3. Data Handling and High-Quality Illustrations for Publications (PhD course)

Role: Instructor

Topics covered:

  • Data management strategies in R
  • Automated dataset merging, cleaning, and version control
  • Creating publication‑quality multi-panel figures with ggplot2
  • Reproducible analysis: project structure, DOIs, and open‑science workflows
  • Hands-on sessions with tidyverse and #tidytuesday datasets

4. Multi-omics Analyses of the Microbial Community (PhD course)

Role: Co-instructor

Topics covered:

  • Overview of multi-omics: metagenomics, metatranscriptomics, metaproteomics, metabolomics
  • Quality control and assembly strategies for microbial communities
  • Functional annotation, pathway reconstruction, and comparative metagenomics
  • Integration of multi-omics layers using statistical and network-based approaches
  • Reproducible workflows with Nextflow and nf-core pipelines

5. SLUBI SIDA Training Course — Reproducible Bioinformatics, Best Practices, Nextflow & nf-core (Researcher course)

Role: Instructor

Topics covered:

  • FAIR data principles and reproducible scientific workflows
  • Introduction to workflow managers (Nextflow)
  • Building and running nf-core pipelines
  • Containerization (Docker, Singularity/Apptainer)
  • Version control with GitHub
  • Designing transparent and reproducible bioinformatics analyses in collaborative environments

6. MedBioInfo — Swedish National Graduate School in Medical Bioinformatics (PhD course)

Role: Teaching assistant / Guest instructor

Topics covered:

  • Medical bioinformatics fundamentals
  • Genome-scale analysis pipelines
  • Statistical modeling of biological data
  • Reproducibility and data stewardship
  • Hands‑on R and bash training targeted at medical datasets

Details of One Course

3. Data Handling and High‑Quality Illustrations for Publications (PhD course)

Learning Outcomes

  • Assess data structure and select appropriate graphical representations in R
  • Create multi‑panel publication‑ready figures using ggplot2
  • Merge and update datasets programmatically without altering raw data
  • Prepare for reproducible code and data submission using DOIs

Content

  • A practical overview of handling data in R, including merging datasets directly from the original data files within R. This enables automatic updates to illustrations and maps.
  • Data handling strategies to avoid multiple dataset versions and preserve raw data integrity, especially in projects with continuous updates.
  • Guidance on selecting suitable figures to convey dataset characteristics and what to avoid.
  • An introduction to open science with emphasis on reproducible data, scripts, and DOI-based sharing.

The course uses free software within the R environment, including packages such as tidyverse, dplyr, tidyr, ggplot2, and patchwork. GitHub is used for DOI and data sharing. #tidytuesday datasets are used extensively for exercises. Teaching includes lectures, presentations, hands-on workshops, group work, and a final individual project using students’ own data or #tidytuesday datasets. The course is delivered as a one‑week on‑campus class followed by an independent project presented via Zoom.

Formats and Requirements for Examination

  • The course objectives are examined through independent project presentations and individual written reports on the techniques used.