Workshops
Hands-on bioinformatics training using Software Carpentries methodology.
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Introduction to R - Data Analysis
Master R fundamentals including data structures, manipulation with dplyr and tidyr, and exploratory data analysis for genomics research.
Duration: ~7 hours | Level: Beginner
Introduction to R - Visualisation
Create publication-quality plots and figures using ggplot2. Learn to visualise genomic data, expression patterns, and statistical results.
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Introduction to Python for Bioinformatics
Learn Python programming essentials including Pandas, NumPy, and Biopython for genomic data analysis and automation.
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Introduction to Unix/Linux Command Line
Essential command-line skills for bioinformatics pipelines, file management, and high-performance computing environments.
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Version Control with Git
Track analysis scripts, collaborate on pipelines, and ensure reproducible research using Git and GitHub.
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NGS Data Pre-processing
Quality control, trimming, alignment, and variant calling workflows for next-generation sequencing data using command-line tools.
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Whole Genome Sequencing QC
Comprehensive quality assessment using FastQC, MultiQC, contamination screening, and coverage analysis for WGS data.
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RNA-Seq Analysis
Differential expression analysis, pathway enrichment, and visualisation using DESeq2, edgeR, and related tools.
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Variant Calling and Annotation
SNP and indel detection, filtering strategies, and functional annotation using GATK, VEP, and SnpEff.
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High-Performance Computing for Genomics
Using SLURM job schedulers, optimising computational resources, and managing large-scale genomic analyses.
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Statistical Analysis for Genomics
Experimental design, multiple testing correction, power analysis, and statistical methods for genomic data.
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Genome Assembly and Annotation
De novo genome assembly, quality assessment, scaffolding, and gene prediction for newly sequenced genomes.
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Metagenomics Analysis
16S rRNA and shotgun metagenomic sequencing analysis, taxonomic profiling, and diversity assessment.
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Workflow Management (Nextflow/Snakemake)
Build reproducible, scalable bioinformatics pipelines using modern workflow management systems.
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Machine Learning for Genomics
Classification, feature selection, and model validation applied to genomic datasets using scikit-learn and TensorFlow.
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Single-Cell RNA-Seq Analysis
Normalisation, clustering, cell type identification, and trajectory analysis using Seurat and Scanpy.
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Long-Read Sequencing Analysis
PacBio and Oxford Nanopore data processing, structural variant detection, and phasing applications.
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Phylogenetics and Evolution
Phylogenetic tree construction, molecular evolution analysis, and comparative genomics methods.
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Population Genetics and GWAS
Linkage analysis, genome-wide association studies, ancestry analysis, and population structure assessment.
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Genomic Data Visualisation
IGV, UCSC Genome Browser, circos plots, Manhattan plots, and custom visualisation for genomic data.
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Agricultural Genomics
Crop breeding analysis, quantitative trait loci mapping, and genomic selection for plant improvement.
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Veterinary Genomics
Animal health genetics, breed analysis, disease variant detection, and companion animal genomics.
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Conservation Genomics
Population diversity assessment, inbreeding analysis, and demographic history for endangered species.
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Clinical Genomics & Diagnostics
Variant interpretation, ACMG guidelines, clinical reporting standards, and precision medicine applications.
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