Course Catalogue

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Bioinformaticians and researchers in biology need to be able to deploy computational skills to solve problems and help answer biological questions using large and complex datasets. This course provides students with additional computing skills that will enable them to apply basic Python and other core computational skills in tackling biological problems. This includes breaking down a biological problem into logical steps that can be implemented programmatically, understanding common data formats and using Python libraries to manipulate them, and using best computing practice to ensure that an analysis is valid and reproducible. Students will receive extensive
hands-on experience of turning individual computing tasks into a meaningful analytical workflow, which will help prepare them for the work involved in our Semester 2 courses and the project within this MSc programme

This course will take place over a 5-week period during the second half of semester 1 and is made up of lectures and computing practical sessions.

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The course will be assessed entirely by coursework assessment (100%).

The assessment will comprise 2 components:

■ a coding set exercise worth 80% of the course (200 lines of code) [ILOs 1,2,3,4]

■ a report worth 20% of the course (1000 words in total) [ILOs 5,6]

The aims of this course are to foster:

■ the ability to break down a biological problem into a series of logical steps amenable to computational analysis

■ the development of practical computing skills that will enable students to tackle common computational problems in biology, including:

■ the use of programming libraries to solve common problems such as parsing file formats

■ the use of good programming practice in the creation of reusable Python applications that yield reproducible results

Students will learn the underlying concepts during lectures and have the opportunity to put these approaches into practice during extensive computer lab practicals.

By the end of this course students will be able to:

■ deconstruct a biological problem into a series of logical steps and evaluate computational solutions for each step;

■ read an API for a software library and creatively use the API to implement methods from that library;

■ use software libraries creatively to extract and manipulate data from files in a range of common bioinformatic data formats (including fasta, gff, vcf and bam files);

■ design and construct appropriate methods for data checking and error handling;

■ creatively integrate the skills above to implement an application to solve a given biological problem in Python;

■ prepare and appraise documentation for a software application.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.