Online MSc Bioinformatics: curriculum

Suitable for students from any disciplinary background, this module introduces foundational topics and concepts in biology and bioinformatics, including the basics of programming in R and Python.

Understand how to efficiently and accurately communicate complex scientific concepts to different audiences, across a variety of bioinformatic-based fields, such as biology, health, statistics and computer sciences.

Introducing the concepts of probabilities and distributions, this module provides the foundation for more advanced techniques and a deeper understanding of their application and limitations. Topics include probability theory, descriptive statistics and hypothesis testing.

Building on the concepts of probabilities, you will explore the application of statistical modelling to understand biological and health data. Topics include linear algebra, correlation and causation, PCA and regression analysis.

This module provides an overview of the bioinformatic analysis of omic data, covering Sanger sequencing, array technologies and Next Generation Sequencing analysis, as well as introducing Genomics and Transcriptomics, Methylation and Chromatic accessibility analyses.

Focusing on the analytical dimensions of omics data, you will understand how to apply appropriate data types and analyse high-throughput data sets using the latest state-of-the-art approaches.

This module introduces concepts from statistical machine learning (ML), providing an in-depth understanding of classical supervised and unsupervised learning models, helping you applying appropriate ML tools and techniques to solve problems on given tasks and data types.

Building on the statistical machine learning module, this will develop your hands-on experience of health and biomedical data and its analysis. You will explore data quality control, techniques for evaluating performance, data visualisation and tools for dealing with missing data.

Introducing you to the analytical approaches of metabolomics, this module will help you appreciate the challenges involved in producing robust and reproducible data sets. You will also examine emerging and advanced (omics) techniques, such as bioimaging and spectroscopy.

Learn how different data processing and analytical methods are used to extract biological insights from large metabolomics data sets, helping you perform your own analyses using the latest (omics) techniques.

Drawing on scientific literature, explore real-world complex systems used in biological and environmental settings, covering statistical modelling for competition and evolution, numerical ecology, multi-view/multi-omics data integration, network biology, explainable machine learning, and other advanced models.

Enriching your understanding of complex systems, machine learning, data integration and data analysis, this module will teach you how to apply these models to real data. This will help you to make appropriate interpretations and avoid potential pitfalls when reviewing your results.

Completing the dissertation fulfils the requirements for the award of the MSc Bioinformatics degree. The subject of the dissertation can be related to your work environment or to an area of interest to your employer, which may encourage employers to support your study time or provide financial assistance.