This full-day tutorial introduces participants to the principles of impactful data visualisation and equips them with the skills to create publication-ready visualisations for -omics data. Designed for beginners with basic knowledge of the R programming language, the tutorial will guide attendees through creating and interpreting key visualisations such as volcano plots, box plots, heatmaps, dot plots, and network diagrams.

Participants will work with real biological datasets in Quarto notebooks and learn how to use tools like ggplot2, ComplexHeatmap, igraph, ggraph, and ClusterProfiler. Through hands-on coding exercises and interactive lectures, attendees will develop an intuition for ggplot2’s grammar of graphics, best practices in data visualisation, and the application of functional enrichment methods to contextualise results.

Whether you are a student or researcher wanting to improve your visualisation skills, or a computational biologist looking to enhance the presentation of your results, this tutorial will provide the tools and knowledge to produce professional-quality figures.

Snakemake is a powerful, Python-based workflow management system that revolutionises how computational tasks are designed, executed and reproduced. By allowing researchers to define workflows as a series of interconnected rules, Snakemake simplifies complex computational pipelines and ensures reproducibility across diverse scientific domains. This intensive workshop introduces participants to the full potential of Snakemake, moving beyond traditional bioinformatics applications to demonstrate its versatility in machine learning and data analysis.

Designed for researchers dealing with complex computational challenges, this tutorial will explore Snakemake’s capabilities through practical, hands-on examples that span multiple disciplines. Participants will learn how to create robust, scalable, and efficient workflows that can adapt to various research challenges. By the end of the workshop, attendees will have a comprehensive understanding of workflow management principles and the skills to implement sophisticated pipelines using Snakemake.

With the significant advancements in genomic profiling technologies and the emergence of selective molecular targeted therapies, biomarkers have played an increasingly pivotal role in both the prognosis and treatment of various diseases, most notably cancer. This workshop is designed to begin with an introductory overview of basic concepts of biomarkers, the diverse categories of biomarkers, commonly employed biotechnologies for biomarker detection, with a special focus on gene mutation and gene expression using DNA-seq, RNA-seq, and scRNA-seq data. Furthermore, we will discuss processes of biomarker discovery and development, and outlining the key steps involved and the current analytical methodologies utilized. Following this, we will discuss the identification of driver gene mutations and altered gene expression, using The Cancer Genome Atlas (TCGA) lung cancer data and PBMC scRNA-seq data as illustrative examples with using R code as practical demonstrations to enhance understanding. In the latter part of this workshop, we will discuss commonly utilized biostatistics and bioinformatics tools, including data visualization, survival analysis and machine learning methods, which are employed to predict disease progression and patient survival outcomes based on these critical biomarkers. By the conclusion of this course, participants will have acquired a broad and fundamental understanding of biomarker discovery, particularly in cancer. This encompasses key concepts, data sources, data analysis techniques, and interpretation strategies. Such expertise will equip participants with the knowledge necessary in contributing to the development of precision medicine in cancer patient treatment.

Single-cell perturbation modelling is revolutionising how we understand the effects of genetic interventions, drugs, and cellular stimulants on molecular and cellular physiology. This half-day virtual tutorial will introduce participants to highly performant Generative AI tools—scGEN and scPRAM—designed to simulate perturbations on single-cell datasets and extrapolate to unseen conditions. Through concise presentations and hands-on exercises, attendees will explore the theoretical underpinnings of these tools, preprocess single-cell data, train generative models, and interpret results using advanced metrics such as R², E-distance, and Maximum Mean Discrepancy as well as dimensionality reduction techniques. Special focus will be placed on challenging scenarios like extrapolating to unseen patient responses and cross-species predictions, leveraging benchmarking insights from the EU BH 2024 Perturb-Bench initiative. Participants will leave with actionable knowledge to implement, evaluate, and benchmark generative perturbation models, supported by practical resources hosted on Google Colab, Jupyter Book, and GitHub.