Bioinformatics Scientist Degree Apprenticeship MSc

The apprentice gains core principle knowledge in statistical methods and the practical skills to apply and perform data analytics. This is essential to data scientists in the bioinformatics field. They gain a solid foundation in statistical concepts and the different data analysis approaches and pipelines used in bioinformatics studies. Commercial software packages, such as SPSS are introduced for data analysis and visualisation.

The apprentice is provided with a framework to support completing the vocational competence evaluation log component of the Bioinformatics Scientist Degree Apprenticeship (ST0649) assessment plan. They receive preparatory sessions and an introductory session from the module leader. This module aligns to the work-based vocational competencies defined within the assessment plan. It normally runs over 14 weeks and is delivered in the workplace during the third semester of year one. Progress is monitored via monthly meetings and the completion of a zero-credit module where the apprentice produces a reflective report of 3000 words. This clearly articulates and evaluates their achievements related to the KSBs defined in the apprenticeship standard. The emphasis is on the apprentice’s ability to reflect on their experiential learning journey and support the completion of the vocational competence evaluation log prior to the EPA.

The apprentice explores ‘omics’ technologies focused on the translational and post-translational mechanisms. They learn about most recent technologies, next generation sequencing, proteomics, phospho-proteomics, metabolomics (such as mass spectrometry) bioinformatics analyses, applications for disease treatment and prevention (pharmacogenomics), personalised medicine and ethical challenges in this field.

The apprentice is introduced to the popular programming language of Python, a powerful scripting language in bioscience which is ubiquitous throughout bioinformatics and scientific computing. The first half of the module introduces basic principles of coding and interface interaction. In the second half the apprentice learns a script language for statistical computing and graphics and more advanced practices. Integrated development environments (eSpyder and Jupyter) are learnt for writing, running, and debugging codes for Python. To turn analyses into high quality documents, reports, and presentations, JupyterLab is applied as a popular library.

The apprentice develops advanced bioinformatics knowledge and practical data analysis skills. Delivered via a series of lectures which introduce them to the core knowledge and computational practices used in the bioinformatics field. Emphasis is on the application of state-of-the-art bioinformatics and computational biology approaches to problem solving in relation to high throughput microarray, next generation sequencing and functional genomics datasets derived from real or simulated research scenarios. IT Laboratories are coupled to all the lectures where the apprentice practices and further develops their core computing and problem-solving skills.

The apprentice explores ‘omics’ technologies and bioinformatics. They learn about most recent technologies, next generation sequencing (such as Nanostring and Illumina platforms) CRISPR-Cas system and genome editing, genomic and bioinformatics analyses, genomic application for disease treatment and prevention (pharmacogenomics), personalised medicine and ethical challenges in this field.

The apprentice undertakes a major independent practical research project in their discipline. They complete a hypothesis-driven work-based project using appropriate discipline-specific laboratory, database or computational research methodologies to interrogate a hypothesis in a specialised area of the life sciences.

The apprentice is introduced to the popular programming language of R, a widely used, powerful scripting language in bioscience which is ubiquitous throughout bioinformatics and scientific computing. R especially shines where a variety of statistical tools are required (such as RNA-Seq population genomics) and in the generation of publication-quality graphs and figures. In the first half of the module, basic principles of coding and interface interaction are introduced. In the second half the apprentice learns a script language for statistical computing and graphics and more advanced practices. Integrated development environments (such as RStudio) are introduced for writing, running, and debugging codes for R. To turn analyses into high quality documents, reports, and presentations, R Markdown is applied as a popular library.