Program Overview

Advanced Computational Biology (BIOL0050)

Key Information

The module is part of the Faculty of Life Sciences, taught by the Division of Biosciences, and carries a credit value of 15. It is compulsory for the Computational Biology streams of the MSc Genetics of Human Disease and MSci/BSc Biological Sciences. Remaining places can be offered to students with relevant backgrounds, including good numeracy skills and knowledge of fundamental concepts in probability theory/statistics and genetics/biology.

Alternative Credit Options

There are no alternative credit options available for this module.

Description

The module provides an introduction to statistical and computational methods for analyzing and interpreting genetics/genomics data, with an emphasis on statistical model application and interpretation. Students will learn how to implement various statistical methods, analyze and visualize genetic data through programming in R and command line tools.

• Content: The module covers the analysis of DNA sequencing data, gene expression variation, analysis of epigenetic data, population genetics, and cancer genomics.
• Teaching Delivery: The module is taught in three weekly sessions of 3 hours each, consisting of brief lectures followed by practicals in R.
• Indicative Topics:
  • Analysis of DNA sequencing data
  • Gene expression variation (differential expression analysis of RNA-Seq data)
  • Analysis of epigenetic data
  • Population genetics: genetic drift, Hardy-Weinberg equilibrium, coalescent model, linkage disequilibrium
  • Population demographics and species tree estimation using the multispecies coalescent: Bayesian computation and Markov Chain Monte Carlo (MCMC) methods applied to comparative genomics
  • Cancer genomics: phylogenetic tree reconstruction (tracing histories of cancer evolution through analysis of somatic mutations)

Module Aims and Objectives

After taking this module, students should be able to:

1. Use computational and statistical techniques to analyze genetic and genomic data.
2. Develop their own scripts in R to read, process, and analyze a variety of datasets.
3. Interpret the results of statistical/computational analyses.
4. Understand the rationale underlying standard computational statistics procedures and the situations in which different procedures are applicable.
5. Understand the key concepts behind models employed in population genetics and convey these key points effectively.
6. Understand differential expression analysis as a method to identify changes in expression between groups.
7. Understand cancer evolution principles, including after treatment, and the different models and statistical principles/frameworks employed to study them.
8. Reason about diverse DNA and RNA sequencing and analytical techniques to solve complex real-life problems.
9. Troubleshoot errors in code or scenarios where results are different from expectations.

Pre-requisites

This is an advanced, fast-paced course with extensive programming assignments. Some previous knowledge of programming is recommended, although not mandatory. Students without previous programming experience are advised to undertake an R crash course before the start of the module.

• Essential Skills:
  • Good numeracy skills
  • Interest in developing programming skills
  • In-depth knowledge of fundamental concepts in probability theory and statistics
  • Good understanding of biological concepts: DNA, gene, protein, chromosome, species, phylogenetic tree, Mendelian genetics, basics of human genetics, DNA/RNA sequencing

Recommended Readings

For students without a biological background, recommended books include:

• Principles of Genetics by D.P. Snustad and M.J. Simmons
• Human Molecular Genetics 4 by T. Strachan and A. Read
• Human Genes and Genomes: Science, Health, Society by L.E. Rosenberg and D.D. Rosenberg

For students who wish to refresh their statistical knowledge, recommended books include:

• Introduction to Statistics and Data Analysis by C. Heumann, M. Schomaker, and Shalabh
• Modern Statistics for Modern Biology by S. Holmes and W. Huber

Module Deliveries for 2026/27 Academic Year

Intended Teaching Term: Term 2, Postgraduate (FHEQ Level 7)

Teaching and Assessment

• Mode of Study: In person
• Methods of Assessment:
  • 60% Exam
  • 40% Coursework (2 assessments)
• Mark Scheme: Numeric Marks

Other Information

• Number of Students on Module in Previous Year: 7
• Module Leader: Dr. Maria Secrier

Intended Teaching Term: Term 2, Undergraduate (FHEQ Level 7)

Teaching and Assessment

• Mode of Study: In person
• Methods of Assessment:
  • 60% Exam
  • 40% Coursework (2 assessments)
• Mark Scheme: Numeric Marks

Other Information

• Number of Students on Module in Previous Year: 15
• Module Leader: Dr. Maria Secrier