Module Information

Module Identifier
CS31420
Module Title
Computational Bioinformatics
Academic Year
2025/2026
Co-ordinator
Dr Otar Akanyeti
Semester
Semester 1
Pre-Requisite
CC21120, CC24420, CS21120, CS24420, MA25220 or MT25220 Students must have taken one of these modules or be able to demonstrate equivalent learning.
Reading List
View example reading list on Aspire
Other Staff
 

Course Delivery

 

Assessment

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