This module will equip the learner with a fundamental understanding of the structure, organisation, and function of the genome: knowledge necessary to generate, organise, analyse, and interpret high-volume genomic data. This will include: 1) fundamental structure of DNA, genes, RNA, inheritance, and the genetic code; 2) genomic organisation; 3) transcription and translation and their regulation; 4) epigenomics; and 5) metagenomics.

1. Appraise the evidence for the mechanisms of genetic inheritance, evolution, and how variation arises in individual genome sequences.
2. Differentiate among the genomic organisation of eukaryotes, prokaryotes, and viruses.
3. Differentiate between transcription and translation: their molecular bases and regulation.
4. Critique the role of epigenetics in the regulation of gene expression.
5. Appraise the role of metagenomics in human biology and environmental science.

This module will equip the learner with an understanding of the current next-generation DNA sequencing technology used to generate modern large scale genomic sequence data and introduce the learner to the databanks and archives (public databases) into which sequence data are deposited, accessed, and searched/mined. This will include: 1) the basic principles of DNA sequencing and their development to the current next-generation high-throughput sequencing technology; 2) key reactions in used in sequencing technology; 3) genome assembly; 4) use of genomic databases; and 5) ethical and legal considerations surrounding the use of genomic data repositories.

1. Catalogue the development of DNA sequencing from early-stage techniques to the current next generation sequencing technologies- incl. implications for cost, logistics, and technical difficulty.
2. Relate the key reactions and technologies harnessed by sequencing technologies.
3. Organise a sequencing project- genome assembly from fragments.
4. Analyse public genomic databases to perform core bioinformatics tasks.
5. Evaluate the legal, ethical, and social concerns associated with using public genomic sequence databases.

This project-based module will equip the learner with the skills required to perform an array of core bioinformatics operations using a web-based graphical user interface software platform (e.g. GALAXY) while establishing a solid understanding of their theoretical underpinnings. Platforms like GALAXY offer web-based bioinformatics software with a graphical user interface that enables completion of complex bioinformatics problems without the use of a computer programming language. Learnings in this module will enable the student to plan, design, organise, perform, and interpret the results of core bioinformatics analysis routines without the added complexities of learning code. Students will accrue a comprehensive understanding of the foundational methodologies of bioinformatics by performing complete (end-to-end) analyses and solving a real-world problem in a project. Students will gain the confidence to appraise common bioinformatics tasks, select appropriate analyses, and interpret the results. This foundation will serve as a springboard to further abilities to perform bioinformatics analyses using command line code in a future module (see Bioinformatics Analysis on the Command Line). Skills taught will include: 1) genomic data retrieval from public NCBI databases; 2) quality control and mapping reads to genomes (global alignment, reassembly, and annotation); 3) phylogenetics and evolutionary signatures; 4) gene regulatory modules and transcriptomics; 5) epigenetic feature inspection using ChIP-seq; 6) single nucleotide variant calling.

1. Design & build a workflow to fetch genomic sequences, perform quality assessments, map to a reference genome, and identify coding regions.
2. Construct (assemble) a microbial genome using sequences from the short read archive.
3. Design & build a workflow to perform variant calling.
4. Design & build a workflow to perform a phylogenetic analysis.
5. Investigate the presence of epigenetic sites in a genomic sequence.

This module will equip the learner with a fundamental understanding of computer syntax and programming essentials applicable across multiple programming languages and coding environments. This module is designed for individuals with no prior programming knowledge and is designed to build programming confidence using the universally accessible and widely used Python or R language. Practical programming skills will be emphasised. This module will begin with a one-day programming practical session held in-person and on campus. The module will then offer a further in-person instruction session mid- semester to complement the online content.

1. Evaluate the utility of different Integrated Development Environments (IDEs).
2. Select appropriate data types, operations, and expressions based on plain language problems.
3. Differentiate and apply different pre-processing techniques to a dataset.
4. Appraise correct scripting syntax, best practices, and create scripts appropriate to given scenarios.
5. Critique the effectiveness of error handling and debugging techniques.

This module will provide the learner with a fundamental understanding of the common statistical approaches used in bioinformatics while establishing a solid foundation in the use of the statistical programming language 'R' (commonly used in bioinformatics data analysis and beyond). This module will focus on the application of statistical approaches to the analysis of real-world data from public datasets, including, but not limited, to biological data. This module will begin with a one-day R programming practical session held in-person and on campus. The module will then offer a further in-person instruction session mid- semester to complement the online content.

1. Design and organise workflows for data hygiene, wrangling, analysis, and modelling using a statistical programming language.
2. Critically evaluate the choice of data analysis and data modelling techniques and tools for a particular scenario with consideration of regulatory constraints.
3. Evaluate study design, data structure, type, distribution, and implement relevant statistical tests.
4. Critique and analyse statistical test output using relevant metrics.
5. Critique and analyse output from predictive models and evaluate their performance.

This module will prepare the learner for bioinformatics analysis using the Linux command line (BASH) for large scale bioinformatics programming and scripting. The command line interface offers a versatile and powerful environment for processing DNA sequence data and undertaking bioinformatics analyses and enables shell scripting which is import for both the reproducibility and scaling-up of analyses. Most of the software tools used in bioinformatics can be implemented on the Linux command line and its use is often preferred in both industrial and academic settings. Hence, ability to use this interface is a crucial skill for bioinformaticians. Learnings in this module will enable to student to plan, design, organise, perform, and interpret an array of real-world bioinformatics tasks as would be conducted in a workplace setting. Learnings will also equip the student with an understanding of the theoretical bases of all analyses conducted. This will include use of the BASH command line for: 1) core bioinformatics operations; 2) viral genomic analysis 3); 4) transcriptomics- analysis and visualisation; and 5) microbiome analysis. The module will offer several in-person computer lab-based instruction sessions to support the online content delivery.

1. Create code on the command line to perform core bioinformatics operations.
2. Develop command line code to investigate genomic sequence data from a viral outbreak.
3. Create command line code to quantify differences in gene expression and interpret results.
4. Produce command line code to perform a microbiome analysis, including metagenome assembly.
5. Design and organize a command line script to perform a complete bioinformatics workflow in the investigation of a biological problem.

To provide the student with the theoretical and applied skills to use contemporary bioinformatics techniques to address a biological science problem. The dissertation represents the capstone work on a topic related to their chosen programme of study.

1. Develop appropriate data collection and acquisition instruments for bioinformatics research and to evaluate each for their appropriateness to the research question.
2. Engage in a sustained piece of individual, academic research on a chosen topic within the field(s) specifically relevant to the course.
3. Read widely and critically reflect on multiple pieces of written research in an appropriate and thorough manner.
4. Evaluate varying methodological approaches and to adopt the necessary approaches suitable to the topic being researched.
5. Produce a dissertation that evaluates and synthesises quantitative (statistical) approaches such as empirical tests on a hypothesis.
6. Produce and defend a dissertation that evaluates and synthesises research methods such as the literature review and dissertation proposal and displays evidence of independent research skills.