Workshops & courses

Topics covered

• —Linux command line and HPC basics
• —FASTQ format, quality scores, and QC with FastQC/MultiQC
• —Short-read alignment: BWA-MEM2 and STAR
• —Variant calling concepts and VCF format
• —Introduction to R for genomic data: ggplot2, basic Bioconductor
• —Hands-on practicals with provided datasets throughout

Topics covered

• —Read QC and adapter trimming (fastp, Trimmomatic)
• —Genome alignment and post-processing (BWA-MEM2, samtools, Picard)
• —Germline variant calling: GATK4 HaplotypeCaller, BQSR, VQSR
• —Somatic variant calling: Mutect2 tumour–normal mode
• —Functional annotation: VEP, SnpEff
• —RNA-seq from FASTQ to differential expression: STAR + DESeq2

Topics covered

• —Nextflow DSL2 syntax: channels, processes, and workflows
• —Container integration: Docker and Singularity
• —nf-core architecture and using community pipelines
• —Running on HPC (Slurm executor) and AWS Batch
• —Configuration profiles, parameter schemas, and CI testing
• —Building and publishing a minimal nf-core-compatible pipeline

Topics covered

• —Droplet-based scRNA-seq: Cell Ranger, STARsolo, and kallisto|bustools
• —Quality control: doublet detection, ambient RNA (SoupX, DoubletFinder)
• —Normalisation, feature selection, and dimensionality reduction (scanpy / Seurat)
• —Clustering, UMAP, and cell-type annotation
• —Differential expression and trajectory analysis (Monocle3 / PAGA)
• —Introduction to spatial transcriptomics (Visium)

Topics covered

• —Curriculum designed around your institution's data and pipelines
• —Delivered online or on-site at your facility
• —Pre-training assessment of participant skill levels
• —Practical sessions use participants' own (anonymised) data
• —Post-training support period included