Module Information

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

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment .66 Hours   Mid Semester Test  Multiple choice questions run in Blackboard  30%
Semester Assessment 80 Hours   Genome Analysis  Essay with accompanying source code/data 3000 words plus code, data and graphs  70%
Supplementary Assessment 80 Hours   Genome Analysis Supplementary Assessment  Essay with accompanying source code/data Resubmission of failed assignment  100%

Learning Outcomes

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

Describe the current algorithms and data structures used in bioinformatic sequence analysis.

Perform an analysis of a set of DNA sequences and interpret the results.

Demonstrate an understanding of the potential sources of error in this type of data and subsequent analyses.

Brief description

This module introduces students to computational bioinformatics. The module will cover the basics of DNA sequence analysis, including sequencing, assembly, similarity/searching, and annotation. The emphasis will be on the computational algorithms that allow us to discover information from biological sequence. This is a fast-moving field, and the content of the course will cover classic algorithms and newer developments.


The basics of DNA, RNA and protein sequences.

Sequencing. Short read and long read sequencing.

Sequence quality. Inspecting the results of sequencing. Analysis of the types of error.

Assembly. Reference genomes.

Sequence similarity, alignment and k-mer algorithms.

Burrows Wheeler transformations and short read alignment.

Genome annotation. ORF-finding. Codons, frames, introns/exons, non-coding regions.

Ethics and security when working with genomic data.

Module Skills

Skills Type Skills details
Adaptability and resilience Inherent in subject.
Co-ordinating with others Will be used for some workshops/pracs.
Creative Problem Solving Data analysis skills, algorithm and data structure skills.
Critical and analytical thinking Inherent in subject matter.
Digital capability Automatic feedback from the process of using and coding the computational tools.
Professional communication Documenting code, essay writing.
Real world sense Understanding the impact of personal genomics in society
Reflection Discussion on the ethics of personal genomics
Subject Specific Skills Using a computer and online tools. Readings from current scientific literature.


This module is at CQFW Level 6