Bioinformatics
These courses aim to familiarise the participants with advanced data analysis methodologies and provide hands-on training on the latest analytical approaches for specific types of biological data.
The analysis of high-throughput sequencing data is the most popular topic, as reflected by the wide range of courses covering this subject (e.g. RNA-seq, ChIP-seq, variation, DNA methylation, whole exome sequencing analysis) but the spectrum of training courses on offer is much wider.
Recommended starting point
Introduction to the Unix Command Line
Practical introduction to the Linux command line and UNIX environment, including filesystem navigation, text processing and building simple command-line workflows.
Working on HPC Clusters
Introductory overview of high performance computing (HPC), including using shared computing clusters and job scheduling systems for large-scale computational analyses.
Building on from introductory programming
Bulk RNA-seq analysis
Practical introduction to bulk RNA sequencing data analysis, covering quality control, quantification of gene expression, exploratory analysis, differential expression, visualisation, and functional interpretation of results using R and Bioconductor.
Single-cell RNA-seq analysis.
Practical introduction to the analysis of single-cell RNA sequencing data, covering preprocessing, quality control, dimensionality reduction, data integration, clustering, and differential analysis using R/Bioconductor.
Working with Bacterial Genomes
Practical introduction to bacterial genomics analysis using Illumina sequencing data, covering genome assembly, annotation, phylogenetic analysis, strain typing, and antimicrobial resistance detection using standardised workflows and community tools.
Expression proteomics analysis in R
Practical introduction to the analysis of expression proteomics data in R and Bioconductor, covering data import, quality control, visualisation, differential abundance analysis, and biological interpretation of results.
Extracting biological information from gene lists
Practical introduction to gene set enrichment analysis, covering functional annotation resources, statistical methods for enrichment testing, web-based tools, and programmatic analysis of gene lists in R.
Quality Control in Sequencing Experiments
Practical introduction to quality control for short-read sequencing experiments, focusing on the interpretation of quality metrics and reports generated by commonly used quality assessment tools.
Metabolomics data analysis
Practical introduction to metabolomics, covering metabolite extraction, analytical technologies, metabolomics data analysis, metabolite identification, and interpretation of metabolic pathways.
Metagenomics data analysis
Practical introduction to metagenomics approaches for studying complex microbial communities, covering amplicon sequencing, shotgun and Hi-C metagenomics, quality control, assembly, and downstream analysis and interpretation of metagenomic data.
Foundations of Phylogenetic Inference
Practical introduction to molecular phylogenetics, covering sequence alignment, phylogenetic tree reconstruction, maximum likelihood and Bayesian inference methods, and interpretation of evolutionary relationships.
Building Computational Pipelines with Nextflow
Practical introduction to building reproducible and scalable computational pipelines using Nextflow and nf-core, covering workflow development, software management, modular pipeline design, and execution across local and high-performance computing environments.
Managing Bioinformatics Software and Pipelines
Practical introduction to managing bioinformatics software and automating analyses using package managers, software containers, and workflow management systems.
Protein Structure Prediction and Analysis
Practical introduction to computational protein structure prediction, covering structure prediction with AlphaFold, model evaluation, visualisation, multimer prediction, ligand binding site prediction, and molecular docking.
Spatial Transcriptomics Analysis
Practical introduction to the analysis of spatial transcriptomics data using Seurat and related R packages, covering preprocessing, normalisation, integration, clustering, visualisation, and inference of spatial organisation and cell-cell interactions.