Module Information

Module Identifier
Module Title
Computational Bioinformatics
Academic Year
Semester 1
Reading List
Other Staff

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment 1 Hours   Mid Semester Test  In-class Blackboard test  40%
Semester Assessment 80 Hours   Analysis of biological data  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 underpins biological data

Define a computational question aiming for knowledge discovery in biology

Read and write different types of 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

Discuss the social and ethical issues in biological data mining

Brief description

This is an interdisciplinary module introducing state-of-the-art computational methods used to analyse biological data. The module focuses on string analysis which has broad applications in genomics, text mining and natural language processing. The content will be delivered within the context of DNA sequence analysis (e.g., predicting gene functions ) and health informatics (e.g., information retrieval from electronic medical records), and the module will cover a wide range of algorithms for efficient string storage, search, comparison, annotation, compression, semantics analysis and prediction. 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 in life sciences.


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

Applications of computational bioinformatics from association mapping and biomarker discovery to disease diagnosis and prevention

Computational methods for DNA sequence alignment, genome assembly, gene annotation and protein structure prediction

Computational methods for word tokenisation, vectorisation, semantics and sentiment analysis

Symbolic time series analysis where data from wearable sensors (e.g., smartphone or continuous glucose monitoring sensor) are transformed into strings and analysed for anomaly detection (e.g., fall detection or high blood sugar)

Shell scripting for creating data processing pipelines in Unix environment

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.


This module is at CQFW Level 6