Syllabuses - PG

CS828 - Introduction to Bioinformatics

TIMETABLETEACHING MATERIAL
Credits10
Level5
SemesterSemester 1
Availability

Mandatory

Prerequisites

None

Learning Activities Breakdown

Lectures: 10 hours  |  Tutorials: 5 hours  |  Labs: 10 hours  |  Self-directed study: 75 hours

Items of Assessment2
Assessment
  1. First coursework assignment (50%): Students will be asked to conduct a piece of research using techniques and approaches introduced in the first half of the module, submitting their work in Jupyter notebook format. The focus will be on the ability to identify and integrate data from multiple sources, as well as reproducibility, understanding and presentation of results.
  2. Second coursework assignment (50%): Students will be asked to conduct a piece of research using techniques and approaches from across the entire module, submitting their work as a report in pdf format. The focus will be on identifying and integrating data from multiple sources, appropriateness of analysis, as well as understanding and presentation of results.
Resit

Single coursework assignment (100%): Students will be asked to conduct a piece of research using techniques and approaches from across the entire module, submitting their work as a report in pdf format. The focus will be on identifying and integrating data from multiple sources, appropriateness of analysis, as well as understanding and presentation of results.

LecturerDidier Devaurs

Aims and Objectives

This module provides key concepts, knowledge and understanding of bioinformatics and computational biology. It enables students to understand how biological data is represented computationally, and how it can be integrated and analysed to inform research and practices in industry. It is a highly practical module, which emphasises direct application of techniques; it involves the same tools and datasets that bioinformatics practitioners and researchers use in their day-to-day roles.

Learning Outcomes

  • Computational representation and management of real-world biological data
  • Integration and analysis of large-scale datasets
  • Programming in Python
  • Reproducible research and computational practice
  • Critical appraisal of data and evidence to inform research

Syllabus

There will be a series of ten lectures delivered in a partially flipped format (as students will be expected to read relevant material ahead of time) with time for questions and discussion. Students will attend five on-campus practical computational laboratory sessions, which will be run using Python in Jupyter notebooks. Students will also attend five tutorials before practical labs, which will act as a bridge between theory and practice. Students will learn what programming features, libraries and platforms are there for bioinformatics tools and how that can be linked with theory covered in lectures.

Learning objectives:
1. Understand and communicate the fundamentals of human genetics and genomics.
2. Understand different DNA sequencing methods and commonly used bioinformatics tools along with their limitations and advantages.
3. Critically assess the quality of sequencing reads and use appropriate bioinformatics tools to deal with poor quality raw sequencing.
4. Understand, process and analyse real-world biological data (from multiple open-source databases and in various formats).
5. Develop computational techniques, methods and tools by utilising existing bioinformatics software.

Recommended Reading

This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.

Various items of reading material will be suggested on the MyPlace page of this module. 

Last updated: 2026-01-28 09:29:20