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As biological sciences have become more data driven, bioinformatics is now central to modern biological research, from genetics, nutrition and epidemiology to ecology, neuroscience and biomedicine. This programme will teach you how to manage and manipulate large datasets to reveal new insights in biological sciences. You will get intensive training in a computer-based approach to biological research, with the opportunity to develop specialist skills in computer programming, data analysis, statistics and computational biology.

New analytical techniques deliver ever more data about genes, proteins, metabolites and the interactions between them. Bioinformatics is the discipline tasked with turning all this data into useful information and new biological knowledge. With applications spanning the breadth of life science disciplines, there is now high demand for trained bioinformaticians.

Prior experience of computer programming is not required as you will be taught the latest tools and techniques in bioinformatics, which you will then apply to your own research project. You will also collaborate with peers to build new bioinformatics solutions to real-world problems as part of an innovative group project. 

Structure

Prior experience of computer programming is not required as you will be taught the latest tools and techniques in bioinformatics, which you will then apply to your own research project. You will also work collaboratively to build new bioinformatics solutions to real-world problems as part of an innovative group project.

If you have any questions about the content or structure, contact the programme director Professor Conrad Bessant

Taught modules

Your taught modules take place in blocks of two weeks of full-time teaching (normally 9am-5pm), followed by weeklong study breaks for independent learning and coursework. This structure allows for an intensive learning experience, giving students the opportunity to immerse themselves in their subject. You will also benefit from small group teaching; for example, seminar groups for 2017-18 were all around 20 students. 

The following intensive taught modules run in the autumn term:

  • Genome Bioinformatics: This module provides an introduction to bioinformatics, focusing specifically on the analysis of DNA sequence data. Lectures cover the bioinformatics methods, algorithms and resources used for tasks such as sequence assembly, gene finding and genome annotation, phylogenetics, analysis of genomic variance among populations, genome wide association studies and prediction of gene structure and function. Practical exercises are used to gain experience with relevant existing bioinformatics tools, data formats and databases.
  • Coding for Scientists: This module provides a hands-on introduction to computer programming (popularly known as coding) using scripting languages popular in the field. The focus is on producing robust software for repeatable data-centric scientific work. Key programming concepts are introduced, and these concepts are then brought together in scientifically relevant applications to analyse data, interact with a database and create dynamic web content. Good coding practice, such as the importance of documentation and version control, is emphasised throughout.
  • Statistics and Bioinformatics: This module is focussed on teaching data analysis using the statistical programming language R. The module covers the basics of using R; drawing publication-standard graphs with R; experimental design; exploratory data analysis; the fundamentals of statistical testing including t-tests and chi-square tests; ANOVA and Regression; fitting and interpreting general linear models; the basics of bioinformatic analysis in R. The module is taught with a mix of theory and practice, with a typical day including roughly two hours of theory instruction in the morning followed by a practical session in the afternoon, often involving hands-on analysis of real experimental data sets.
  • Post-genomic Bioinformatics: This module provides an introduction to bioinformatics, focusing specifically on the management and analysis of data produced by so-called post-genomic methods such as transcriptomics, proteomics and metabolomics. Lectures cover the bioinformatics methods, algorithms and resources used for tasks such as the identification and quantitation of transcripts, proteins and metabolites, and analysis of the interactions between these key biological molecules. Practical exercises are used to gain experience with bioinformatics tools, data formats and databases that have been developed for this field.

Group project

After Christmas you will spend six intensive weeks working on a group project where skills developed in previous modules are applied to develop a novel piece of software to meet a real biological need. Additional skills you can pick up during this module include web development, database design and collaborative working using GitHub.

Research project

You then embark on an individual six month research project, the topic of which we are reasonable flexible about as long as it has bioinformatics at its core. We offer a list of projects supervised by members of Queen Mary, but you can also propose your own projects, which may be carried out elsewhere (e.g. at your employer if you are a part time student).

Learning and teaching

Our Bioinformatics programme combines traditional lectures and hands-on practical computer sessions. Group work, student presentations and open discussion are an integral part of the programme, giving you the chance to develop communication and team-working skills. We take pride in cultivating a close-knit and friendly working relationship between academics and students on this programme. You will benefit from small group teaching, normally typically around 20 students in each seminar, allowing for a more intensive learning experience and increased interaction.

Assessment

You are assessed primarily by coursework, with a small number of in-class assessments. Coursework is usually in the form of a biological problem that for which a bioinformatics must be devised, implemented and written up. You also undertake an individual project assessed by a 10,000-word dissertation and a presentation that is given on the final day of the course.

Fees

Tuition fees for Home and EU students

2019/20 Academic Year

Full time £10,100

Part time £5,050

Tuition fees for International students

2019/20 Academic Year

Full time £21,950

Part time £10,425


Visit the MSc Bioinformatics page on the Queen Mary University of London website for more details!

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