Applied Bioinformatics (MSc)

How will I be taught?

You will be taught through a dynamic, research-led approach that emphasises active learning, collaboration, and real-world application. The programme consists of six taught modules, delivered entirely in person, with sessions held in classroom environments rather than traditional lecture theatres.

Teaching is structured around problem-based and active learning, where you will apply theoretical concepts directly to practical problems in biological data science. For example, you may work individually or in small groups to extend and troubleshoot bioinformatics pipelines, gaining experience that mirrors real research scenarios.

Each session is supported by semi-structured post-session tutorials, giving you the opportunity to consolidate your learning, work independently or collaboratively, and bring questions to the teaching team for guidance. These tutorials are designed to extend classroom activity and promote self-directed learning.

Teaching is delivered by internationally recognised experts in bioinformatics, genomics, genetic epidemiology, protein design and systems biology. You will work with real datasets from leading research groups, giving you early access to current challenges and approaches in the field. This ensures that your learning is research-informed and immediately relevant to both academic and applied career paths.

Throughout the programme, you will develop practical skills in tools and environments widely used in both academia and industry. Assessments are varied and designed to reflect authentic outputs from professional practice, including projects, presentations, coursework, and portfolios.

How will I be assessed?

Assessment in this programme is designed to reflect the diverse and applied nature of bioinformatics and data science, with tasks that mirror real-world outputs and professional expectations. Each module includes two summative assessments, chosen to evaluate students' ability to apply theoretical understanding in practical and authentic contexts.

Assessments include a range of formats such as technical reports, portfolios, presentations, and applied coursework. These are carefully selected to align with the skills and knowledge required in research and industry, including data analysis, scientific communication, coding, and pipeline development.

The assessment strategy is built on constructive alignment, ensuring that each task maps clearly to both module and programme learning outcomes. Across the programme, students have opportunities to demonstrate a broad range of competencies — from critical analysis and problem-solving to collaborative working and communication — ensuring all learning outcomes are robustly assessed.

Formative assessment is embedded throughout the teaching to help students build skills incrementally. These tasks are often embedded into classroom activities or supported through post-session tutorials, giving students the chance to gain feedback and develop confidence before completing summative assessments.

Assessment diversity not only supports inclusive learning but also prepares students for the varied demands of academic, clinical, and commercial careers in bioinformatics and data science.

How will I be supported?

You will be supported through a combination of personalised academic guidance, structured feedback, and a wide range of skills development resources throughout your time on the programme.

Each student is assigned a personal tutor, who provides academic and pastoral support through regular check-ins across the academic year. Tutors are your first point of contact for advice on your progress, wellbeing, and personal development planning.

You will receive detailed feedback on both formative and summative assessments, delivered through a mix of written and verbal formats. Formative tasks are embedded into teaching to build confidence and skills progressively, helping you prepare for final summative assessments. Class-wide feedback is also used to reinforce common areas of strength and improvement.

The programme includes a structured series of academic skills sessions, aligned to marking rubrics and assessment expectations. These sessions cover key skills such as technical report writing, presenting data in slide decks and posters, and communicating complex analyses clearly and concisely. These are further supported by additional resources and workshops from the School, University, and Library Services.

To ensure a smooth transition into the programme, students attend both programme-level and module-level induction sessions, which introduce key systems, expectations, and opportunities for development. Ongoing support is available throughout the year to help you succeed academically and prepare for your future career.

What skills will I practise and develop?

Knowledge & Understanding:

On successful completion of the Programme you will be able to demonstrate:

• Critically apply advanced computational and statistical methods to analyse and interpret large-scale biological datasets, including genomic, transcriptomic, and proteomic data.
• Integrate knowledge from biology, computing, and statistics to design and implement innovative, reproducible workflows for interpreting genomic and multi-omics data.
• Critically evaluate methodologies in contemporary biological data analysis and explain how these approaches address complex biological and clinical questions, including disease mechanisms, molecular functions, and network-level interactions. 
• Students will develop domain-specific expertise based on their chosen learning pathway.
• Demonstrate proficiency in polyglot programming; justify choices based on analytical goals, data types, and performance considerations, aligned with best practices in scientific computing.
• Demonstrate the ability to critically evaluate primary research literature in bioinformatics, assessing the strengths, limitations, and validity of methodologies, results, and interpretations in the context of current scientific knowledge.

Intellectual Skills:

• On successful completion of the Programme you will be able to demonstrate:
• Critically analyse and interpret complex biological data using advanced computational and statistical methods applied to large-scale datasets, including genomic, transcriptomic, and proteomic data.
• Synthesise and apply interdisciplinary knowledge to solve complex biological problems by evaluating analytical strategies, justifying methodological choices, and generating novel insights in bioinformatics and computational biology.
• Demonstrate collaborative problem-solving by contributing to shared research activities and engaging constructively with peers and experts to refine scientific approaches.
• Engage in critical reflection and self-directed learning to evaluate personal development and identify areas for continued growth in scientific practice.
• Demonstrate independence in applying critical thinking and initiative to address complex bioinformatics challenges.
• Design, execute, and critically evaluate an independent, original research project in bioinformatics or computational biology, demonstrating autonomy in identifying research questions, selecting appropriate methodologies, interpreting complex data, and situating findings within the broader scientific context.

Professional Practical Skills:

On successful completion of the Programme you will be able to demonstrate:

• Design and implement reproducible, scalable bioinformatics workflows using appropriate programming languages and tools, with attention to data integrity and computational efficiency.
• Apply advanced statistical and machine learning techniques to analyse complex biological datasets and draw conclusions aligned with specific research aims.
• Integrate multi-omics data to construct comprehensive models of biological systems and elucidate underlying molecular mechanisms.
• Collaborate effectively within interdisciplinary research teams, contributing bioinformatics expertise to drive innovation in diverse scientific contexts.
• Communicate scientific findings clearly through written, oral, and visual formats, adapting content for both specialist and non-specialist audiences.

Transferable/Key Skills:

On successful completion of the Programme you will be able to demonstrate:

• Demonstrate autonomy and self-direction in learning by identifying personal development needs, setting goals, and evaluating progress to support continuous professional growth.
• Apply critical thinking and problem-solving skills to analyse complex biological and computational challenges, developing evidence-based, reasoned solutions.
• Plan, manage, and deliver bioinformatics projects efficiently, balancing resources, timelines, and deliverables to meet defined objectives.
• Adapt to emerging technologies and methodologies by critically evaluating and integrating new tools and approaches into bioinformatics practice.
• Systematically identify, evaluate, and synthesise complex information to support evidence-based decision-making. Critically appraise research methodologies, assess the validity and relevance of findings, and communicate data-driven insights to inform real-world solutions.