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
Having successfully completed this module you will be able to:
- Write basic scripts and pipelines for automating and repeating analyses
- Take the raw outputs of these techniques and perform basic data processing and analysis.
- Explain how genomic, transcriptomic and proteomic techniques work, and discuss their strengths and limitations.
- Navigate, and organise and manipulate files within the UNIX/LINUX environment.
- Interpret the results of biological studies that make use of these techniques.
- Use the R language to perform basic statistical and graphical analyses
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.
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.
|Practical classes and workshops||18|
|Total study time||150|
Resources & Reading list
Arthur M. Lesk (2014). Introduction to Bioinformatics. Oxford University Press.
Beginning R: the statistical programming language.
Arthur M. Lesk (2012). Introduction to Genomics. Oxford University Press.
Keith Bradnam & Ian Korf (2012). UNIX and Perl to the Rescue!: A Field Guide for the Life Sciences (and Other Data-rich Pursuits)..
This is how we’ll formally assess what you have learned in this module.
|Integrative Omics assignment||25%|
This is how we’ll assess you if you don’t meet the criteria to pass this module.