Applied Bioinformatics and Genetic Epidemiology

The aim of this Applied Bioinformatics and Genetic Epidemiology program fromCardiff University is to provide individuals with a platform to explore, analyse and interpret contemporary biological data. This course offers Masters level instruction in Bioinformatics and Genetic Epidemiology with a focus in genetic epidemiology.

With a focus on genetic epidemiology, this program is ideal for graduates from a life sciences, mathematics, or computer sciences discipline. Applied Bioinformatics and Genetic Epidemiology is delivered by Cardiff University

Key facts

It will provide you with the skills and knowledge of computational and statistical biosciences to prepare you for a challenging career in academic research, biotechnology, or the pharmaceutical and healthcare industries.

Bioinformatics is the field of study that utilises computational tools to understand biology. Genetic Epidemiology is the study of how genetic factors play a role in determining health and disease, and their interplay with the environment. As well as developing core skills in computational and statistical biosciences, you will focus gene discovery approaches including GWAS, explore copy-number variation (CNV) analysis, and post-GWAS approached such as pathway/network, gene-set and polygenic epidemiological methods.

This program has been designed to meet the growing demand from academic research, biotechnology and the pharmaceutical and health care industries for capable informaticians with bioinformatics skills. We will provide instruction in computational and statistical biosciences and you will foster the additional complementary skills required to enable you to work effectively within a multidisciplinary bioinformatics arena.

Aims

• To introduce the commonly exploited computational, statistical and analytical approaches to post genomic biology and genetics
• To develop skills to understand and critically evaluate research methodologies and conclusions that allow you to make sound judgements about the applicability of these techniques to your own research
• To develop competency in both the design and analysis of studies and the effective extraction of information in genetics, genomics and other biosciences coupled with the ability to communicate the information, results, issues and ideas to audiences of both a specialist and non-specialist background
• To prepare and provide guidance to perform an original piece of research within the specialist area in which you wish to pursue your career

Distinctive features

This course was first established over a decade ago in response to the completion of the first drafts of the human genome project and the subsequent informatics needs of the genetics and genomics communities. Ongoing advances in genomic technologies and analytic approaches have dictated the continuing evolution of this program to provide contemporary instruction in new essential skills.

Our course is accessible to students with primary degrees in mathematics, life sciences or computing. Modules in core complementary areas such as in computation/scripting, statistics and molecular biology provide the fundamental building blocks necessary to succeed in bioinformatic analysis and interpretation.

In the Spring Semester, you will undertake a 20-credit case-study. This will include taught elements in research skills and involve working directly with a client using real data. You will be embedded in one of the many research centres across campus and gain valuable experience in delivering bioinformatics projects for research programs. The resulting data will also be presented alongside your peers at our case-study poster sessions.

You will be taught essential organisation and coding skills and given extended instruction in statistics. If you are not from a life sciences background, we will introduce you to the biology behind the data and help you make informed decisions around data choice and interpretation.

Courses include:

• Dissertation: Genetic Epidemiology

• Computing for Bioinformatics and Genetic Epidemiology
• Bioinformatics

• Case Studies in Bioinformatics and Biostatistics