Bioinformatics (with Advanced Practice) MSc

You complete advanced practice (AP) when you study our two-year postgraduate courses. Completed over one semester (12 weeks), you gain additional experience and enhance your employability and academic learning.
You normally complete one of the below AP internship types:
• a research or development internship developed by Teesside University staff
• an employer-led internship developed by the employer partner
• a virtual internship working on projects for an external organisation
• a vocational internship with an external organisation.
All internships are located at our Middlesbrough or Darlington campus (including NHC), apart from the vocational internship which is located at the external organisation.
The AP internship is related to your course. You receive preparatory sessions, normally in the semester before (delivered by our Student Futures team) and an introductory session from the module leader.

You are expected to complete at least 400 hours of AP work, including preparation for AP, induction, training and any personal or professional development completed.

You receive 12 hours of supervision (normally 1 hour per week) and are assigned an AP supervisor (a member of University staff) to support and assess your work. Your supervisor’s role varies depending upon the internship type (explained in the learning strategy). As a minimum, they meet with you at the start, mid-point and end of the internship, coordinate meetings and are the point of contact for internship providers. You are also allocated a work-based mentor who functions as your supervisor when completing specific internship types.

Develop advanced bioinformatics knowledge and practical data analysis skills. You are introduced to the core knowledge and computational practices used in the bioinformatics field. Study the application of state-of-the-art bioinformatics and computational biology approaches to problem solving relating to high-throughput microarray, next generation sequencing and functional genomics datasets derived from real or simulated research scenarios. IT laboratories are paired with lectures, allowing you to practice and further develop your core computing and problem-solving skills.

Gain core knowledge in statistical methods and the practical skills to apply and perform data analytics. This is essential to data scientists in the bioinformatics field. Develop 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. IT laboratories are coupled to the lectures and enable you to test out your new skills, tackle exercises, and work on project code to manipulate, process and interpret biological data.

Explore ‘omics’ technologies and bioinformatics and 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 as well as ethical challenges in this field.

You undertake a major independent practical research project in your discipline while fully integrated within a research team. Reflecting staff expertise, you pursue many discipline-related topics, including medical, industrial and environmental microbiology, molecular, cell and system biology, recombinant DNA technology, protein biochemistry, structural biology, fermentation, bioengineering and many other areas, using the state-of-the-art analytical and digital infrastructure at the National Horizons Centre. You complete a hypothesis-driven project using appropriate discipline-specific laboratory, database or computational research methodologies to interrogate a hypothesis in a specialised area of the life sciences. You are expected to work at a level recognised to be at the forefront of the discipline. Key skills in research and knowledge creation are developed. You must demonstrate the capacity for comprehensive and objective analysis, and for developing innovative and constructive proposals for the solution to the project topic. Supervisors provide guidance to support you, but a high degree of autonomy is required. You are supported by working within a research team, and by technical and learning support staff.

You explore ‘omics’ technologies focused on the translational and post-translational mechanisms. You 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. One of two modules dedicated to the exploring ‘omics’ technologies. Taught through a combination of interactive lecture and seminars.

You are introduced to the popular programming language of Python, a powerful scripting language in bioscience and found throughout bioinformatics and scientific computing. The first half of the module introduces basic principles of coding and interface interaction. In the second half you learn a script language for statistical computing and graphics and more advanced practices. Integrated development environments (Spyder and Jupyter) are learnt for writing, running, and debugging codes for Python. To turn analyses into high quality documents, reports, and presentations, JupyterLab will be applied as a popular library. IT Laboratories are coupled to the lectures where you test out your new skills, tackle exercises, and work on project code to manipulate, process and interpret biological data.

You are introduced to the popular programming language of R, which is widely used and powerful in bioscience, bioinformatics and scientific computing. R especially shines where a variety of statistical tools are required (such as RNA-Seq and 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 you learn 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 will be applied as a popular library. IT Laboratories are coupled to the lectures where you test out your new skills, tackle exercises, and work on project code to manipulate, process and interpret biological data.