Computational Bioinformatics

Module Identifier: CS31420
Module Title: Computational Bioinformatics
Academic Year: 2026/2027
Semester: Semester 1
Co-ordinator: Dr Otar Akanyeti
Reading List: View on Aspire
Pre-Requisite: CC21120 or CS21120 or MA25220 or MT25220
Students must have taken one of these modules or be able to demonstrate equivalent learning.
Other Staff: Dr Yasir Saleem Shaikh; Dr Wayne Aubrey

Assessment

Assessment Type	Assessment details	Proportion
Semester Assessment	Test: 1 Hours In-class Blackboard test	40%
Semester Assessment	Assignment: 80 Hours Essay with accompanying source code/data 3000 words plus code, data and graphs	60%
Supplementary Assessment	Supplementary assessment: 80 Hours Essay with accompanying source code/data 3000 words plus code, data and graphs	60%
Supplementary Exam	Supplementary test: 1 Hours In-class Blackboard test	40%

Learning Outcomes

On successful completion of this module students should be able to:

Explain scientific concepts that underpin biological data
Analyse and interpret biological data using computational methods and algorithms
Draw conclusions from the computational analysis of data
Recognise the strengths and limitations of computational methods when applied to a biological data set

Brief description

This is an interdisciplinary module introducing state-of-the-art computational methods and algorithms used for for biological data analysis. In particular, the module focuses on creation, analysis and interpretation of "omics" data which has broad applications in Health, Biology and Biotechnology. Some examples are biomarker discovery for disease diagnostics and prognostics, plant and animal breeding, environmental monitoring for infectious diseases and production of industrial enzymes. The students will be gently introduced to biological concepts and terminology with no prior knowledge required and will have the opportunity to apply their computing skills to discover new knowledge.

Content

Data sets and knowledge representation in biology (e.g., the basics of DNA, RNA and protein sequences)

Bioinformatics technologies and methods used in generating and analysing "omics" data

Computational methods for DNA sequence alignment, genome assembly, gene detection, gene annotation, genomic variant analysis and protein structure prediction

Applications of computational bioinformatics from association mapping (from genotype to phenotype) and biomarker discovery to disease diagnosis and prevention

Ethical issues surrounding retrieval and use of biological information

Module skills

Skills type	Skills details
Adaptability and resilience	Interdisciplinary skills and knowledge
Co-ordinating with others	Practical sessions and in-class activities
Creative Problem Solving	Data analysis skills, algorithm and data structure skills.
Critical and analytical thinking	Formulating a research question and the application of computational methods to test hypotheses.
Digital capability	Programming and using computational tools.
Professional communication	Documenting code, report writing.
Real world sense	The applications of biological data mining
Reflection	Understanding the impact of biological data mining.
Subject Specific Skills	Using a computer and online tools. Readings from current scientific literature.

Notes

This module is at CQFW Level 6