Study Applied Bioinformatics at Cranfield

Over the past few years, Bioinformatics has become the most exciting field in biology. This Bioinformatics MSc course provides a unique hands-on learning experience in bioinformatics skills, by combining the latest advances in analysing high-throughput genomic, transcriptomic and metabolomics data.

Cranfield’s Bioinformatics MSc is the first of its kind in the UK. With more than 200 alumni over the past 10 years, it became the most popular postgraduate course in Bioinformatics in Europe. Because Cranfield is a solely postgraduate university it means that every single taught module of the Applied Bioinformatics MSc is uniquely tailored to be a Masters-level. That’s why it is the award-winner of the BBSRC’s Masters Training Grant (MTG) for best course in life sciences. Our taught modules cover in great depth a plethora of programming languages typically applied in the bioinformatics, such as Perl, Java, R and SQL; as well as modern Web technologies such as JavaEE, NoSQL and JavaScript. Furthermore, we have two dedicated taught modules focusing on established bioinformatics protocols for the latest Next Generation Sequencing (NGS) and 3rd Generation Sequencing (3GS) technologies.


Overview

  • Start dateFull-time: October. Part-time: October
  • DurationOne year full-time; two-three years part-time
  • DeliveryTaught Modules 40%, Group Project 20%, Individual Research Project 40%
  • QualificationMSc
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

This course aims to equip graduate scientists with the computational skills and awareness needed to process, analyse and interpret the vast amounts of biological data now becoming available. This course is equally suitable for candidates from life sciences disciplines who aim to gain the programming and computational skills through this course, and graduates with IT/computer science background who want to gain the molecular biology understanding to become bioinformaticians.

On completion of this course, you will be able to apply information technology and computational techniques to process genomic and genetic data, as well as developing novel drug discovery and diagnostic tools.

Additionally, you will gain the skills to design and implement software tools and databases using the latest advances in standalone and web-based technologies to fulfil the need of the research community.

Your career

Industry, alumni and current students talk about Bioinformatics at Cranfield.

Bioinformatics is a fast-growing field that offers progressive career opportunities for forward-thinking people who are ready to grasp the challenge; people who understand both the biological and computing aspects of this science.

Our MSc opens doors to careers in industry, public research establishments and university research.The multidisciplinary nature of our course has allowed our students to follow diverse career paths in various medical-related sectors.

Successful graduates have been able to pursue or enhance careers in a variety of key areas such as:

Pharmaceutical and Biotech companies, plant research institutes,  food sector,  public Institutions, bioinformatics,  IT companies.

Cranfield Careers Service
Our Careers Service can help you find the job you want after leaving Cranfield. We will work with you to identify suitable opportunities and support you in the job application process for up to three years after graduation.We have been providing Masters level training for over 20 years. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. The increasing interest in sustainability and corporate and social responsibility has also enhanced the career prospects of our graduates.

Previous students have gone on to jobs within prestigious institutions including: The Sanger InstituteThe European Bioinformatics InstituteInstitut National du Cancer (France), Centro de Investigación Prínciple Felipe Ií (Spain), GlaxoSmithKlinePubGeneTessellaUCBWellcome Trust, InpharmaticaInvitrogen, Oxford Gene TechnologyCancer Research.

Others have chosen to continue their research training by undertaking a PhD either at Cranfield or elsewhere.

Cranfield graduates are very successful in achieving relevant work. For professionals already in industry, Cranfield qualifications enhance their careers, benefiting both the candidate and their employer.




Marek Piatek

Consider the MSc in Applied Bioinformatics at Cranfield as an investment in your future. That is what I did when choosing the programme. I was attracted by the diverse portfolio of courses offered. All were directly supporting timely and relevant market skills. I was delighted with this investment as it directly empowered me in the course of my employment and career.

Marek Piatek, Director of Bioinformatics at Ardigen, Applied Bioinformatics MSc, 2009

Why this course?

1. The only Bioinformatics MSc in the UK offering a truly bespoke postgraduate experience:

Cranfield University is the only solely postgraduate university in the UK, which means that every single lecture and practical session within the Applied Bioinformatics MSc is tailored at M-level. Unlike other Bioinformatics MSc courses you may come across, you won’t be sharing any modules or lectures with other undergraduate students or MSc courses. This gives our Applied Bioinformatics MSc a truly tailored postgraduate experience.

2. A Variety of programming languages:

Experience taught us that there is no such thing as a single preferred programming language in the field of bioinformatics. Every programming language has its strength and advantages depending on the task in hand. For example, Java can be quite powerful if you are developing a visualization and/or standalone application, while R and Python are excellent choices for machine learning and statistical analysis. Perl on the other hand is a very easy programming language to learn by biologist, and forms the foundation of most of the legacy tools and frameworks developed for the human genome project, and still being used to date. That’s why the Applied Bioinformatics MSc is the only course in the UK that offers three dedicated programming modules as part of the taught component; covering R, Java, and Perl. Furthermore, other programming languages such as Bash, JavaEE, SQL, JavaScript and Python are also comprehensively covered. This means that upon the completion of this course, you won’t only have the skills and expertise to develop optimized bioinformatics tools for various tasks, but you will also find it relatively much easier to learn new programming languages that were not covered during the course; as you will have the foundation in interpreted, object-oriented, and statistically-focused languages.

3. A truly NGS and 3GS-focused course:

Analysing sequencing data from the latest sequencing platforms such as Illumina® Pacific Biosciences ® and 10x Chromium® is nowadays a standard skill required for most bioinformatics jobs (a quick search on LinkedIn for bioinformatics jobs should prove this!). That’s why in this course we have two dedicated modules on analyzing sequencing data. The first module, “Next Generation Sequencing Informatics” focuses on preprocessing and analyzing Illumina® short-reads sequences for performing sequence alignment, gene expression profiling using RNA-Seq, and genotyping for variant discovery. The second module, “Advanced Sequencing Informatics and Genome Assembly”, provides hands-on experience in performing de-novo sequence assembly using short and long-reads sequencing data, as well as providing  computer practical sessions in developing and optimizing your own assembler using the overlap-layout-consensus (OLC) and de-bruijn-graph (DBG) algorithms.

4. Industrial and research applications in:

Drug discovery: Applications of bioinformatics in drug discovery is not only covered in the Applied Bioinformatics MSc course, but is actually an integral part of the delivery of the course. Leading experts from multinational pharmaceutical industry such as Enrico Ferrero (GSK) and Lee Larcombe (nexaSTEM) are core members of the teaching team of this course and are actively delivering several lectures and hands-on computational practicals. These industrial partners also offer a number of thesis projects for our students each academic year.

Plant genetics: The Bioinformatics Team at Cranfield University works very closely with the AgriFood theme, which includes the Crop Water Use and the Plant Science Laboratory Groups. The Course Director has an established track record as PI or CoI several plant genetics-focused research projects. If plant genetics and food security is your research of interest, you will be given the opportunity to join one of our research team for your thesis project.

Infectious diseases: We have a long-term ongoing collaboration with the London School of Hygiene and Tropical Medicine (LSHTM). Together, we work closely in establishing a molecular understanding for several infectious and tropical diseases such as tuberculosis, malaria and Chagas disease. Each year, we offer 3-4 research MSc thesis project placements for our Bioinformatics students to work closely with LSHTM’s world-renowned leading experts on one of their ongoing research projects. Over the past few years, we published a number of research articles based on such thesis projects. Our MSc students were either the leading or co-authors on these. Example 1. Example 2.

Applied Bioinformatics MSc student Ewa Marek

The course is kept up-to-date, and thus we're developing skills that are nowadays valued in the labour market. I'm sure that it will increase my chances of finding an interesting job. Furthermore, the fact that some lectures are given by external experts gives a better insight of what a bioinformatician's work looks like, which helps to choose one of several possible career paths.

Ewa Marek, Applied Bioinformatics MSc

Informed by Industry

Cranfield University benefits from the input of a group of world-renowned experts in a range of applied sciences including bioinformatics. We lead and collaborate in diverse research and consultancy projects, both nationally and internationally.

Our collaborators include:

GlaxoSmithKline
London School of Hygiene and Tropical Medicine
Queen Mary University of London
Unilever
Sanofi Aventis
Rothamsted Research
The European Bioinformatics Institute
London School of Hygiene and Tropical Medicine
University of Athens
Cambridge University.

Course details

The taught programme is generally delivered from October until March and is comprised of eight compulsory taught modules, a group project and an individual thesis project. Students on the part-time programme will complete all of the compulsory modules based on a flexible schedule that will be agreed with the Course Director.

Course delivery

Taught Modules 40%, Group Project 20%, Individual Research Project 40%

Group project

Watch our Applied Bioinformatics group project video.

Real-life experience

Working in project teams is part of everyday working life. It requires not only your individual expertise but also an appreciation of the skills of the other members of the team. This part of the course gives you the opportunity of working as part of a team on a group project. This is an invaluable experience that will help you to recognise and implement the differing contributions that colleagues bring to team work, and the different roles that we can choose to play within a team. 

Individual project

Industry related projects

A four-month thesis project carried out either at Cranfield or an external research establishment or commercial organisation within the UK or Europe. This gives you the chance to concentrate on a subject area of particular interest to you, perhaps in collaboration with the type of organisation that you are hoping to find employment with.

Real-life-problems solving thesis projects

Our MSc students finalise their hands-on study practice with individual thesis projects that solve problems in multidisciplinary areas whilst working under academic supervision. Some recent projects include:

  • Development of a Web-based resource for tuberculosis genotyping and diagnosis from whole genome sequencing data: PhyTB.

This project by Ernest Diez (2013-2014) is focused on creating PhyTB - an application for the interactive study of variation in M.tuberculosis using data from the PhyloTrack library.

Visit project page

Further reading

  • Applications of data science and machine learning in detection of meat adulteration.

This project by MSc student Rafal Kural (2014-2015) is focused on the application of machine learning methods to unravel hidden patterns of meat samples using Fourier Transform Spectrometry, Gas Chromatography Mass Spectrometry, High Performance Liquid Chromatography and VideometerLab. Over the course of this work it has been proven that it is certainly possible to obtain very accurate detection of meat adulteration, reaching sample adulteration level prediction accuracy of 100% for GCMS and 90-97% for FTIR and VM data.

Modules

Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory modules and (where applicable) some elective modules affiliated with this programme which ran in the academic year 2018–2019. There is no guarantee that these modules will run for 2019 entry. All modules are subject to change depending on your year of entry.


Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course

Introduction to Bioinformatics using Python

Module Leader
  • Dr Fady Mohareb
Aim

    This module provides an introduction to bioinformatics (what it is, why it is needed and what it can deliver) as well as the required skills to browse, query various relevant resources. The module covers the programming basics required by students in order to program in Python, which is nowadays becoming one of the most popular programming languages in the bioinformatics community; and its application in retrieving, parsing and visualising biological sequence data.


Syllabus
    • Fundamentals of Python programming
    • Introduction to Object Oriented Programming (OOP)
    • Simple mathematical operations
    • Modules in Python
    • Various data types and Objects
    • Control Statements
    • Lists, Tuples and Dictionaries
    • Functions
    • File IO

    • Programming for biology using BioPython 
      • DNA sequence manipulation using Perl
      • Reading protein files
      • Performing Multiple Sequence Alignment
      • BLAST
      • Data Visualisation
    • Biological data format.



Intended learning outcomes

On successful completion of this module a student should be able to:

  • Identify the most important programming structures
  • Retrieve relevant nucleotide, protein sequences and their corresponding metadata from online public data resources
  • Develop custom Python scripts for sequence manipulation
  • Develop Python scripts to automate data handling and curation tasks
  • Develop advanced stand-alone Python programs for the acquisition and consolidation of data from remote databases.

Exploratory Data Analysis and Essential Statistics using R

Module Leader
  • Dr Maria Anastasiadi
Aim
    To provide an overview of important concepts in statistics and exploratory data analysis. The module introduces the main concepts in analysing biological datasets using the R environment, as well developing bespoke scripts for multivariate analysis such as principal component analysis and hierarchical clustering.
Syllabus
    • Introductory statistics – averages, variance and significance testing
    • Exploratory data analysis (PCA, HCA)
    • An introduction to R.
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Devise basic R programs to meet given specifications
  • Apply different statistical techniques and be able to implement them programmatically in R
  • Effectively integrate and devise statistical methods into experimental protocol design.

Proteome Informatics

Module Leader
  • Dr Fady Mohareb
Aim
    To provide the students with an awareness of the current trends in proteomics and the crucial role that bioinformatics plays within this field.
Syllabus
    • Introduction to practical proteomics (qualitative & quantitative)
    • Proteomics repositories (PRIDE, PeptidAtlas, etc.)
    • Protein/peptide identification algorithms (Mascot, X!Tandem, OMMSA)
    • Tools for quantitative proteomic data (iTRAQ, SILAC, SRM, etc.)
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Explain the mode of operation of the most common analytical techniques used in the acquisition of proteomic data
  • Critically assess current practices and identify the relative strengths and weaknesses of the techniques covered and how these relate to the quality of the data acquired
  • Discover information using bioinformatics tools and effectively apply the information to biological problems.

Next Generation Sequencing Informatics

Module Leader
  • Dr Fady Mohareb
Aim
    To introduce the techniques that have given rise to the genomic data now available, and develop skills and understanding in the bioinformatics approaches that facilitate evaluation and application of these data. Over the past decade, Next-generation DNA Sequencing (NGS) technology has been a huge stimulus for a lot of breakthrough discoveries in biology. This module provides therefore an overview of many core types of NGS projects, including latest protocols in genomic and transcriptomic analyses, genotyping and variant calling as well as detailed hands-on practical sessions of our best practice data-analysis workflows.
Syllabus
    • Gene expression analysis using microarray
    • Introduction to Next Generation Sequencing (NGS) Technology
    • Genome assembly and quality control
    • Transcriptome informatics
    • Sequence data analysis web platforms
    • Geneotyping and variant calling.
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Critically evaluate the operation of the most common analytical techniques used in the acquisition of genomic sequence and expression data
  • Apply various techniques to overcome the challenges of dealing with sequence data and be able to identify and apply appropriate software tools to tackle these challenges
  • Apply appropriate genome assembly software and optimise their outputs
  • Perform gene expression profiling using both first and next generation sequencing data.
  • Critically assess current practices and evaluate the relative strengths and weaknesses of the techniques covered and how these relate to the quality of the biological findings that result
  • Critically contrast a range of NGS tools and related sequence software tools for NGS applications, and interpret the output from those tools.

Machine Learning for Metabolomics

Module Leader
  • Dr Maria Anastasiadi
Aim
    To cover the main aspects related to the analysis of the metabolic profile in living organisms and explore statistical and computational techniques that are central to the field of metabolomics with particular emphasis to machine learning. Machine learning is a rapidly expanding form of artificial intelligence (AI) which has found many applications in the field of metabolomics. Examples include explanatory analysis of complex biological systems, novel biomarker discover and prediction modelling.
Syllabus
    • Metabolomics: overview and workflow
    • Multivariate classification and biomarker discovery
    • Introduction to machine learning
    • Applications of machine learning in metabolomics
    • Advanced topics in machine learning
    • Applications of machine learning in food metabolomics
    • Advanced topics in R .



Intended learning outcomes

On successful completion of this module a student should be able to:

  • Critically assess various metabolomics analytical and spectral platforms
  • Apply state-of-the-art best practices in machine learning to fit the purpose of the analysis
  • Critically understand the basic principles of the most common instrumental techniques used in metabolomics, the technical limitations and the underlying biological and experimental assumptions that impact on data quality
  • In depth knowledge of the current approaches for modelling and warehousing of life science data
  • Develop classification and regression models based on multivariate metabolic data
  • In-depth understand and application of machine learning algorithms and be able to provide examples of specific machine learning algorithms for each task
  • Apply statistical and machine learning procedures covered during the module, to derive biological relevant information from metabolic datasets using R.

Programming Using Java

Aim
    To introduce the concepts of object oriented programming using Java. Java is the pre-eminent programming language for serious application development on the Internet. The module covers Java data objects of primitive and reference data types and introduces students to the basic fundamentals of programming in Java, with hands-on practical sessions on implementing simple programs using calculations, variables, control statements and loops.
Syllabus
    • Fundamental principles of programming in Java
    • Object-oriented programming using Java
    • Variables and calculations
    • Strings
    • Arrays, ArrayLists and HashMaps
    • GUI programming
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Identify the most important programming structures.
  • Develop Java programs to meet given specifications.
  • Implement custom Java classes, interfaces, and packages.
  • Implement standalone application interfaces using Java Swing Components.

Data integration and Interaction Networks

Aim
    Data integration represents a major challenge for bioinformatics research. This module covers the most popular data management, integration and visualisation tools within the bioinformatics community as well as the main concepts of databases design and normalization.
Syllabus
    • Database design and normalisation
    • Development of database access interfaces
    • Design and implementation of data repository Web front-ends
    • Introduction to interaction networks
    • Data Integration and visualisation.
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Utilise systems software for the visualisation of systems and system interactions
  • Critically apply available tools for data integration
  • Design, normalise and implement databases for experimental datasets
  • Critically assess the main data standards protocols for genomics
  • Discover systems relationships between data using bioinformatics tools and approaches.

Advanced Sequencing Informatics and Genome Assembly

Module Leader
  • Dr Fady Mohareb
Aim
    To develop a system-level view of biological systems and their response to various internal and external factors, through the integration of advanced NGS and 3GSsequencing data with functional annotation using established concepts of graph theories widely applied for various assemblers such de-Bruiin and Overlap-layout consensus.
Syllabus
    • Advanced Java programming
    • Application of graph-theory using Java
    • Advanced Next-Generation Sequencing informatics.
    • De-novo genome assembly
    • Gene prediction and functional annotation
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Apply and optimise various algorithms for short and long reads sequence assembly.
  • Successfully develop and optimise de-novo genome assemblies for various species3 Develop in-silico gene prediction models and functional annotation4 Effectively apply graph theory and its application in biological data analysis.

Teaching team

You will be taught by an expert multidisciplinary team both from Cranfield University and externally: Cranfield University: Dr Fady Mohareb - Course Director and lecturer in Bioinformatics Tomasz Kurowski External lecturers: Prof Conrad Bessant – Professor of Bioinformatics QMUL Dr Enrico Ferrero - Scientific Leader at GSK Dr Lee Larcombe - Applied Exomics Ltd Director Dr Robert King – Bioinformatician at Rothamsted Research Dr Luca Bianco Bioinformatics Research Scientist – Fondazione Edmund Mach

How to apply

Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.