Bioinformatics teaching in undergraduate degrees is crucial if we want to better prepare the next generation of graduate students to tackle the challenges of modern science.
We contribute to a number of NST subjects throughout the year and are the organisers of the NST Part II BBS Bioinformatics Minor Subject.
NST Part II BBS Bioinformatics minor (128)
Bioinformatics is an interdisciplinary field that uses computational approaches to process biological data. With the biological and biomedical sciences becoming more data-driven than ever before, bioinformatics is central to research and work in these areas. The NST Part II BBS Bioinformatics minor subject introduces the fundamental bioinformatic concepts and methodologies used to analyse biological data. It is structured around 2 main blocks; data science, omics and approaches to analysis of biological data.
The Data Foundation sessions provide introductory sessions in biological databases, command line, R programming, data manipulation and data visualisation. These sessions provide skills for data exploration, relevant not only in the bioinformatics field but also in other data-driven subjects. With the large number of data available, different areas of research are becoming increasingly reliant on these skills. The course makes use and builds upon the data skills acquired through the Data Foundation sessions to perform analysis on biological data.
In the Data Science for Bioinformatics block we will introduce statistics and machine learning topics that are popular in the bioinformatics field. These topics are fundamental to the analysis of data which are currently in high-demand due to the data-driven approach of answering research questions, driven by the increasing amount of data becoming available. Knowledge gained from this set of lectures and practicals can be applied and transferred to different research domains.
The Bioinformatics Approaches to Omics and Analysis of Biological Data block introduces bioinformatics workflows used to process omics datasets with hands-on practice on genomic data. This follows with genome-wide association studies (GWASs), a popular approach in population studies which allows the identification of associations between single-nucleotide polymorphism (SNPs) loci and traits. We will then intro- duce gene set enrichment analysis to link results obtained from the previous analyses back to biology and identify classes of genes or proteins that are over-represented in our results which may have an association with disease phenotypes.
The course is specially designed for students coming from the biomedical sciences who have had little exposure to computational biology fundamentals. As such it provides data foundation sessions that go over programming, data visualisation and manipulation and basic statistics, all of which will be used throughout the course. Topics throughout the course are introduced through a set of lectures that introduce theoretical concepts, and practicals which provide hands-on practice using real biological datasets.
More information about the Part II Biological and Biomedical Sciences can be found here.
For further information on the course, please contact the Course Administrator Ms Cathy Hemmings.
