Bachelor of Science Honours in Bioinformatics and Computational Biology

Purpose

Bachelor of Science Honours in Bioinformatics and Computational Biology is composed of two components: lectures/tutorials and practical research, assigned respectively 58% and 42% of the credits (notional learning hours)).

Rationale

The qualification in Bioinformatics and Computational Biology is aimed at learners with either a computer science or a biology background, who are qualitatively talented and keen to learn statistical and computational skills to advance an understanding of biology.

The qualification is thus inter-disciplinary in nature, and provides training that integrates knowledge from the computer science, biochemical and genetics fields. These inter-disciplinary skills are required to analyse the datasets routinely generated in modern biological sciences.

The demand for Bioinformaticians in South Africa, Africa and the international community remains exceptionally high due to the need for Bioinformatics skills to engage with big data in modern biology and life sciences. It is estimated that the number of positions available in Bioinformatics grows at a rate of approximately 3% per annum, and that in some states in the United States of America (USA), vacancies for bioinformaticians grows at the fastest rate of any career (www.recruiter.com/careers/bioinformatics-scientists/outlook/, accessed 27 November 2017). The institution plans to contribute to meet this high demand for learners in the new inter-disciplinary field of bioinformatics.

The demand for this inter-disciplinary qualification justified the existence of the qualification.

Recognition of Prior Learning (RPL)

Learners who do not meet the minimum entry requirements can apply for admission to the qualification by the RPL route.

This selection will be following the CAT and RPL rules, and according to the regulations of the faculties of Science, AgriSciences or Medicine and Human Health. The procedures for RPL and CAT the qualification is as follows:

• Submit all applications to the Faculty Administrators.
• Review of all applications by the Postgraduate Committee of the Centre for Bioinformatics and Computational Biology. The assessment of the formal (CAT) and non-formal or informal learning (RPL) against the knowledge required to complete the requirements of the Bachelor of Science Honours qualification.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor's Degree Level 7 with majors in the following fields: Bioinformatics, Genetics, Biochemistry, Molecular Biology, Computer Science or Mathematics.

This qualification consists of the following compulsory and elective modules at National Qualifications Framework (NQF) Level 8 totalling 120 Credits.

Compulsory Modules at Level 8,110 Credits

• Advanced Bioinformatics, 40 Credits.
• Algorithms in Bioinformatics, 5 Credits.
• Current Topics in Bioinformatics, 5 Credits.
• Machine Learning in Bioinformatics, 5 Credits.
• Project, 50 Credits.
• Seminar in Bioinformatics, 5 Credits.

Elective Modules at Level 8, 10 Credits (Choose one)

• Cell Biology in Bioinformatics, 10 Credits.
• Scientific Computation in Bioinformatics, 10 Credits.

1. Demonstrate an in-depth understanding of modern programming language (rules and construction) and the application thereof to Bioinformatics problems.
2. Demonstrate an understanding of the theories, research methodologies, methods and techniques relevant to the field, and how to apply such knowledge through engagement in the Bioinformatics and Computational Biology field.
3. Demonstrate the ability to produce and communicate information effectively.

Associated Assessment Criteria for Exit Level Outcome 1

• Construct a solution to a problem as a series of programmatic steps.
• Display sufficient knowledge of the Linux operating system to use it effectively as a platform on which to qualification.
• Display a working knowledge of effective programming in Python or a related programming language.
• Develop and apply new programs to bioinformatics problems.
• Discuss the relevance and applicability of computer programming in the broader Bioinformatics field.
• Apply new methods to bioinformatics problems.
• Effectively apply knowledge and understanding of methods to address new defined problems in bioinformatics.
• Display a knowledge of and explain the use of relevant algorithms in bioinformatics.
• Apply relevant algorithms in a bioinformatics programme or to a problem.

>Analyse the efficacy of the algorithms as well as their applicability to specific types of problem sets.

Associated Assessment Criteria for Exit Level Outcome 2

• Discuss various, field-specific, fundamental biological topics in terms of the different biological components of cells and the functions thereof, including important metabolic pathways, macromolecule synthesis and regulatory mechanisms.
• Discuss bioinformatics in the context of the current state of the field.
• Search the literature, identify relevant publications, and critically assess the data.
• Compare different bodies of information and synthesise a coherent conclusion from the bodies of information.
• Identify current methods and applications, show how they can be applied and apply the most important aspects of these to their own research project (with or without the help of others).
• Display a deeper knowledge of different machine learning approaches.
• Display a deeper knowledge of the use of machine learning methods and approaches in computer programmes.
• Apply machine learning methods to new bioinformatics problems.
• Identify the major current developments at the forefront of bioinformatics and explain the applicability and application thereof.
• Incorporate aspects of the major current developments at the forefront of bioinformatics into own research.
• Unambiguously and very clearly define a problem, analyse the problem, identify appropriate methods to use and apply these methods to the problem in order to resolve it and arrive at an answer.
• Apply skills to solve bioinformatics problems.
• Effectively use multiple sources of information.
• Review data from literature critically.
• Apply learning strategies and take responsibility for work.

Associated Assessment Criteria for Exit Level Outcome 3

• Access, read and critically review bioinformatics publications and scientifically assess the statements therein.
• Be sufficiently familiar with the methods used in bioinformatics research to be able to explain the reasons for their selection as well as their relevance and applicability.
• Communicate scientific concepts effectively.
• Give a broad account of current developments at the cutting edge of the field of machine learning.

Integrated Assessment

The qualification in bioinformatics is composed of seven modules for which assessment is done through class tests, assignments, presentations, discussions and examinations:

The research project will be performed under close supervision with continual discussion and feedback during the course of the research, to assess progress and to train the learner and develop the skill in the learner to critically assess results, identify problems and consider different approaches to solve the problem and achieve the aim of the research project. This insight and skill is achieved via formative testing, including numerous discussions and meetings. The success of the learner to achieve the skill set, and successfully complete the project is also assessed through a summative test in the form of a Research Report at the end of the project.

In the research project, it is expected that the learner will demonstrate a close familiarity with the seminal papers directly relevant to the research project and be able to interrelate and combine knowledge from different publications in the literature. The learners should also display a clear understanding of the importance of the research question in the context of the available literature, and an understanding of a suitable work plan to address the research question. The learner should display familiarity with methods and techniques can be applied to the research question, and demonstrate success in applying these, or provide insight into why the applied methods and techniques did not yield expected results. The learner must be able to assess the data produced by applying a method or technique to the research question, including the statistical relevance of the data. The learner must display an understanding of the context of the results in the literature, and make meaningful conclusions from the results.