MSc Applied Bioinformatics and Genetic Epidemiology
The aim of this programme 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 programme is ideal for graduates from a life sciences, mathematics, or computer sciences discipline.
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 programme 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 of the Programme
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 programme 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 programmes. 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.
The aim of this programme 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 programme is ideal for graduates from a life sciences, mathematics, or computer sciences discipline.
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 programme 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 of the Programme
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 programme 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 programmes. 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.
