TRM5006 · Clinical and translational bioinformatics

Overview

This unit equips you with the knowledge and practical skills to apply bioinformatics in key areas such as genomics and transcriptomics, with a strong focus on translational research. You will build foundational coding skills and learn how to work on high-performance computing (HPC) platforms. Core topics include bioinformatics, biostatistics, experimental design and the use of AI in bioinformatics research. Through these, you will develop the ability to identify, select, and critically evaluate appropriate bioinformatic tools for use across medical research disciplines. The unit also explores how bioinformatics is applied in practice: from identifying disease biomarkers to support diagnosis and uncovering molecular mechanisms of disease. You will learn from specialist researchers and clinicians actively applying bioinformatics across different fields, with opportunities to review methodological and scientific literature in bioinformatics and disease with a focus on clinical and translational medicine. You will develop an in-depth knowledge of how bioinformatics can be applied to address cutting-edge research questions. By the end of this unit, you’ll gain the skills and confidence to use coding tools and high-performance computing (HPC) environments, apply bioinformatic methods to real-world clinical datasets, evaluate research and methodologies in bioinformatics, and combine bioinformatics approaches to tackle questions in translational medicine.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Apply knowledge of bioinformatics data exploration and storage using public databases and training resources.
2. Apply basic programming skills to process genomic or transcriptomic data on a remote UNIX server.
3. Analyse and visualise research data using the R open-source programming language.
4. Analyse, interpret and appraise bioinformatics methods for storing and analysing complex genomic and transcriptomic data.
5. Exploit generative artificial intelligence tools to solve common bioinformatic challenges.
6. Evaluate the principles of ethics and privacy to all bioinformatics data analysis and reporting.

Workload

12 hours study per week distributed as: • Teacher-directed learning, delivered online lectures, workshops and discussions, and pre and post-learning modules (6 hours per week) Self-directed learning including private study, reading published studies, reference material, and textbooks (6 hours per week)

Student experience, not official rules. Nothing here changes what the Handbook says.