The University of Southampton
Courses

BIOL3063 Bioinformatics and Systems Biology

Module Overview

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale ‘systems’ biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.

Aims and Objectives

Module Aims

The aims of this module are to introduce students to the latest genomic, transcriptomic and proteomic technologies; illustrate the applications and limitations of these approaches; and provide students with some practical bioinformatics skills to enable them to process, analyse, and interpret the resulting data.

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Explain how genomic, transcriptomic and proteomic techniques work, and discuss their strengths and limitations.
  • Interpret the results of biological studies that make use of these techniques.
  • Take the raw outputs of these techniques and perform basic data processing and analysis.
  • Navigate, and organise and manipulate files within the UNIX/LINUX environment.
  • Use the R language to perform basic statistical and graphical analyses
  • Write basic scripts and pipelines for automating and repeating analyses

Syllabus

Special Features

This module will make extensive use of VDU facilities.

Learning and Teaching

Teaching and learning methods

Study time allocation Contact time: 28 hours. (10 hours of lectures, 18 hours of workshops ) Private study hours: 122 hours. Total study time: 150 hours. Teaching and Learning Methods Formal lectures will introduce the topics each week and give some examples of what can be achieved. These will be combined with practical workshops. Each practical session will be structured around a worksheet and/or online tutorials containing the core skills and some tasks that put these skills into practice. Five assignments will be assessed and count towards the final mark for the module.

TypeHours
Lecture10
Independent Study122
Practical classes and workshops18
Total study time150

Resources & Reading list

Arthur M. Lesk (2014). Introduction to Bioinformatics. 

Arthur M. Lesk (2012). Introduction to Genomics. 

Beginning R: the statistical programming language. 

Keith Bradnam & Ian Korf (2012). UNIX and Perl to the Rescue!: A Field Guide for the Life Sciences (and Other Data-rich Pursuits).. 

Assessment

Summative

MethodPercentage contribution
Genomics assignment 25%
Integrative Omics assignment 25%
Journal 20%
Scripting assignment 15%
Scripting assignment 15%

Referral

MethodPercentage contribution
Coursework 100%
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