Bioinformatics MSc

Dubai Campus

Start date
1 year full-time, 2 years part-time
Course Type
Postgraduate, Taught

Annual tuition fee for 2024/25: AED 138,456 (full-time)

Scholarships available 


MSc Bioinformatics Alumna Hanan Ahmed Al Mullah shares her experience at the University of Birmingham.

Designed to prepare you to interact with the world’s most advanced biological and clinical datasets - this programme will prepare you for careers, or further graduate work, in the omics-enabled biosciences.

MSc Bioinformatics is accredited by the United Arab Emirates Ministry of Education

The future of biology is bioinformatics – computational analysis procedures that leverage state-of-the-art statistics and machine learning to gain insight into systems of exquisite complexity. We have entered an era of unprecedented expansion in the biological sciences, and our data now grows exponentially faster than Moore’s law.

The biological sciences have been transformed by the advent of omics. Enabled by revolutionary advances in molecular sequencing and mass spectrometry, it is now possible to sequence a genome in six hours, simultaneously assess the expression level of every gene in a genome, quantify the abundance of proteins and metabolites, and determine the epigenetic and regulatory landscape of individual cells. Hypotheses are generated through the integrative analysis of enormous datasets, and tested in high-throughput with third-generation genome-engineering technologies, including CRISPR.

Biology is now driven by data.

This MSc is composed of seven taught modules and one independent project. The taught modules provide you with foundational knowledge and skills in statistics, computer programming, and molecular biology, and then exploit this skillset to help you to understand and participate in the ongoing revolution in biological data science.

The course begins with a fast-paced introduction to essential capabilities. Through individualised and student-centered teaching, as well as heterogeneous group work, it will prepare wet-bench biologists and clinicians interested in data analysis, as well as statisticians or computer scientists wishing to work in biology for studies in modern bioinformatics.

You will then be introduced to the sequencing modalities that have reduced the cost of obtaining the human genome from over a billion pounds to less than a thousand dollars, and the many applications in the biomedical and environmental sciences enabled by these technologies. Beyond DNA and RNA sequencing, you will learn how mass spectrometry and nuclear magnetic resonance spectroscopy have opened protein sequencing and metabolomics – the study of all small molecules, or metabolites present in a system. You will learn how to integrate these datatypes with high-content imaging to map molecular changes onto four-dimensional representations of complex systems, including the human nervous system and fresh-water ecologies.

Closely supervised individual projects will prepare you to tackle real-world research or industrial problems at scale, and highly interactive group projects will enable teams to tackle challenges too complex for individuals. Projects are developed in collaborations with our world-leading faculty and our many industrial partners, ensuring that you are equally prepared for academic or industrial career paths.  

Programme lead

Dr Mohamed El-Hadidi

Dr Mohamed El-Hadidi

Dr Mohamed El-Hadidi is an Associate Professor in Bioinformatics at the Institute of Cancer and Genomic Sciences. Dr El-Hadidi's expertise extends to his involvement in human genome projects, proving invaluable for a deeper understanding of complex biological systems. Furthermore, he has a keen interest in multi-OMICS data integration, with a specific emphasis on microbiome-host Multi-OMICS data integration.

View Mohamed's staff profile

Why study this course?

  • Learn how to analyse each major omics data-type – beyond next generation sequencing, understand mass spectrometry, emerging single molecule techniques, genome engineering, and integrative analysis toolsets that reveal synergies between these and other distinct modalities such as Health Partners
  • Gain a foundation in statistical machine learning to prepare for a career in the information sciences
  • Explore the frontiers of biosciences, from precision medicine, to precision agriculture, and emerging fields including molecular ecosystems biology and topological data analysis.  
  • Discover new opportunities in one of the fastest growing industrial and academic fields 


  • Introduction to Biology and Programming (10 credits)
  • Essentials of Mathematics and Statistics (20 credits)
  • Genomics & Next Generation Sequencing (20 credits)
  • Data Analytics & Statistical Machine Learning (20 credits)
  • Metabolomics and Advanced Omics Technologies (20 credits)
  • Computational Biology for Complex Systems (20 credits)
  • Scientific communication (10 credits)
  • Individual Project (60 credits)

Introduction to Biology and Programming (10 credits)

This module will provide an introduction to biology and programming for students from any disciplinary background. On the biology side, we will introduce students to foundational topics and concepts, including the fundamentals of carrying out good science and key processes in molecular biology. These include an exploration of the nature of the genetic information, how it is stored, transmitted and expressed and the mechanisms through which genetic information, and hence organisms, can change. Topics also include a variety of different methodologies and technologies that can be  used  to  measure  processes  in  biology,  from  the  sequencing  of  DNA  to  quantifying  the spectrum of proteins present inside cells (genomic, epigenomic, transcriptomic, metabolomic and proteomic techniques). 

On the computational side, this module will introduce the computing systems students will need to work with in bioinformatics (including Linux) and the associated languages including bash script. Students will learn how to write simple computer programs in a range of the most popular languages, including R and Python, and good practice in how to manage and maintain code, as well as debug it.  Finally, students will learn the fundamentals of how to visualise and explore their data, providing essential preparation for future research projects. s

By the end of the module you will be able to:

  • Describe the steps involved in measuring biological processes using a variety of omics techniques spanning genomics, epigenomics, transcriptomics, metabolomics and proteomics. 
  • Identify appropriate omics techniques and explain/compare their relative merits and limitations for addressing specific questions in biology. 
  • Apply knowledge of basic biological processes including molecular genetics to solve simple problems in transmission genetics and interpret biological datasets. 
  • Apply knowledge of computing systems and communications to remotely connect to and use the University compute systems. 
  • Combine simple Shell commands (bash) in a Linux system to build a pipeline to manipulate data files and extract basic information. 
  • Apply the principles of good practice in computer programming to write code (in R or Python) to reproducibly analyse a biological dataset to answer defined questions (including data import, and manipulation). 
  • Choose appropriate methods for data exploration and create simple graphics to visualise the patterns shown by a given biological dataset. 

Essentials of Mathematics and Statistics (20 credits)

This course will provide an introduction (or refresher) to essential quantitative theory that underpins modern bioinformatics. Concepts will be introduced via a series of core problems whose details will be explored in greater depth in later modules. The module contains a variety of integrated learning environments, including interactive lectures as well as tutorials to explain and give feedback on aspects of assessment.  

On successful completion of this module, you should be able to: 

  • Understand essential mathematical and statistical concepts and apply the correct techniques to solve elementary data analysis problems 
  • Correctly apply techniques for the graphical representation and visualisation of data 
  • Perform essential statistical data analysis in a computer programming language, specifically R 
  • Solve quantitative problems inspired by real world bioinformatics that require an understanding of the underlying biology and the application of the correct mathematical and statistical techniques 
  • Demonstrate the qualities and transferable skills necessary for employment requiring the exercise of initiative and personal responsibility, decision making in complex and unpredictable situations, and the independent learning ability required for continuing professional development 

Genomics & Next Generation Sequencing (20 credits)

This module will introduce the students to various sides of -Omics, primarily genomics and transcriptomics. Furthermore, The module will include a coverage of the technological progress, as well as specific fields of, Classical Genetics, Population Genetics and Cancer Genomics. In particular, the module covers the following topics:

  • Foundations of Genomics 
  • Foundations of Transcriptomics 
  • Introduction to Methylation  
  • Transcription factors analysis 
  • RNA binding protein analysis 
  • Chromatin accessibility analysis (e.g. DNase-seq, ATAC-seq) 
  • Chromatin structure analysis (e.g. HiC, ChIA-PET) 

It will include a coverage of the technological progress: 

  • History: Sanger sequencing through array technologies  
  • Next generation Sequencing 
  • Advanced library construction procedures for specialized assays (e.g. ChIP, DNase, ATAC, HiC, eCLIP) 

On successful completion of this module, you should be able to: 

  • Understand the biological interpretation of the various *omics fields, especially DNA, RNA and Methylation based. 
  • Understand the various technologies available to measure the various type of information from Sanger sequencing, micro-array, Mass-Spectrometry to Next Generation sequencing 
  • Analyse the various types of data generated in the field both with command line and web interface such as Galaxy 
  • Integrate the various type of data to understand the biological implication of the results 
  • Understand the complexity of information available to enable the integration of diverse data types 

Data Analytics & Statistical Machine Learning (20 credits)

The aim of the module is to provide an in-depth understanding of the state of the art in data integration, mining and analysis with applications in biology and biomedicine. The module covers topics related to data. It will include various statistical techniques.  Furthermore, the module will present modelling and optimisation approaches to deal with large structured, yet heterogeneous, dataset and will include several techniques. It will as well provide methods to analyze, visualize and integrate the various types of data. It includes as well the training on several well used web-based resources (e.g. OMIM, TCGA, DAVID, REACTOME). 

On successful completion of this module, you should be able to: 

  • Demonstrate a good understanding of complexity of omics and clinical data and their management including their semantic representation  
  • Demonstrate an in-depth understanding and ability to perform data integration, mining and analysis  
  • Demonstrate conceptual understanding of Computing, Algorithmic and Programming that enables the student to evaluate methodologies and develop critiques of them and, where appropriate, propose new methods  
  • Understand the complexity of information available to enable the integration of diverse data types  
  • Demonstrate self-direction and originality in tackling and solving problems to perform the appropriate Modelling and Optimization 

Metabolomics and Advanced Omics Technologies (20 credits)

This module will introduce you to metabolomics, and you will learn about the data processing and data analysis approaches (e.g. biostatistics and metabolite identification) that are used to interpret data and extract biological insight from the large metabolomics data sets. You will also be introduced to the analytical approaches (e.g. mass spectrometry and NMR spectroscopy) that are used in metabolomics, so that you can appreciate the challenges involved in producing robust and reproducible data sets.   Additionally, this module will introduce you to other emerging and advanced (omics) techniques, including bioimaging and spectroscopy.  The course will include a combination of interactive seminars, hands-on computer workshops and tutorials.  

On successful completion of this module, you should be able to: 

  • Describe and explain/compare the fundamental concepts and essential technical aspects of metabolomics, biological imaging and other advanced bioscience technologies.  
  • Critically evaluate the major challenges facing metabolomics, biological imaging and other advanced bioscience technologies.  
  • Analyse and explain the concepts underlying a typical bioinformatics workflow to process and analyse metabolomics datasets.  
  • Develop a basic bioinformatics data analysis workflow and derive biological insight from large metabolomics data sets.  
  • Analyse emerging data types and extract biologically meaningful conclusions  

Computational Biology for Complex Systems (20 credits)

This module focuses on big data-driven science leveraging diverse omics modalities in the environmental, ecological and toxicological areas. This module will draw from the fields of molecular biology, genomics, genetics, evolutionary biology, computational biology, toxicology, and risk assessment –though these are not prerequisites for enrolment. Theory and concepts will be highlighted by real world applications drawn from the scientific literature. By involving instructions from industry, government agency and NGO scientists, it means to offers a variety of dynamically evolving career paths to students. 

On successful completion of this module, you should be able to:

  • Describe and explain/compare the fundamental technical aspects of Omics technologies (transcriptomics and metabolomics), high-throughput in vivo and in vitro assays, and computational approaches as applied to environmental, ecological, and holobiotic systems (animals and/or plants + their microbiomes). 
  • Synthesize a critical understanding of the emerging field of Molecular Ecosystems Biology, including an emphasis on biotic-abiotic interactions, and the role of the microbiome in establishing the health and resiliency of organisms.   
  • Apply conceptual and mechanistic understanding of integrative analysis techniques for multi-omics data analysis, including original research. 
  • Develop a systematic understanding and critical appraisal of the implications of research in Biology related fields, especially ethical implications. 
  • Write a persuasive and compelling abstract and prepare an oral presentation on original research in the ecological sciences.

Scientific Communication (10 credits)

This module will teach students how to communicate effectively between the many fields relevant to Bioinformatics such as Biology, Health, Statistics or Computer Sciences. Furthermore, they will be trained to efficiently and accurately communicate complex scientific concepts, whether biological or statistical, to a general audience through various means of communication (poster, abstract, news piece). An important part of this training will involve students from various backgrounds working together to tackle the presentation of an inter-disciplinary project, using mathematical and/or computational approaches to address a research question involving biological and/or clinical data. 

On successful completion of this module, you should be able to: 

  • Develop effective strategies for working in an interdisciplinary team.  
  • Understand how to communicate effectively with colleagues and other stakeholders from different backgrounds and expertise.  
  • Demonstrate the ability to compile interdisciplinary information including biological data and statistical techniques.  
  • Evaluate and select appropriate methods to visualise and communicate information.  
  • Create outputs for different stakeholders from the general public to experts in different fields. 

Individual Project (60 credits)

This module will put student in real-life situation of a bioinformatics project with practical problem to solve proposed by an academic member of University of Birmingham. They will have to find the relevant literature, and apply the relevant analytical methods to generate new information that will be presented in a written report and orally.

On successful completion of this module, you should be able to: 

  • Present in written and oral form their topic background, approach, analysis, results and conclusions. 
  • Perform a bioinformatics analysis and/or development for the project.  
  • Effective application of Computing, Algorithmic and Programming that enables the student to develop critiques of them and, where appropriate, propose new methods.  
  • Demonstrate self-direction and originality in tackling and solving problems to perform the appropriate Modelling and Optimization.  
  • Demonstrate the use and application of the qualities and transferable skills necessary for employment requiring the exercise of initiative and personal responsibility, decision making in complex and unpredictable situations, and the independent learning ability required for continuing professional development.  


We charge an annual tuition fee for 2024 entry.

  • AED 138,456 (1 year full-time)
  • AED 69,228 (2 years part-time)


We have a number of scholarships available.

Scholarship information

How To Apply

To find out more about our application process please visit our How to Apply page before starting your application. 

Our Standard Requirements

2:1 or equivalent in Biology, Mathematics, Computer Science or other relevant subjects

International Requirements

International Students

English language requirement
You can satisfy our English language requirements in two ways: 

English to IELTS 6.5 (with no less than 6.0 in any band). 

If you need help with your English language skills then support is available. 

The English for Academic Purposes Presessional course is for international students who have a conditional offer to study at the University, but who do not currently meet the English language requirements. The course is tailored to your level of English and allows you to meet the English language requirements for your programme without retaking IELTS. The EAP programme runs throughout the year and offers different programme lengths ranging from 42 weeks to 6 weeks. The length of course you need depends on your future course, your existing IELTS score and the English level you need for your university degree.

Find out more about the English for Academic Purposes Presessional course.

As a University of Birmingham student, you will be joining the academic elite and will have the privilege of learning from world-leading experts, as well as your peers. From the outset you will be encouraged to become an independent and self-motivated learner. We want you to be challenged and will encourage you to think for yourself.

Course delivery

We offer a combination of evening and weekend classes which are designed to accommodate working professionals and those who choose to study full-time.

Our draft timetable for full-time and part-time students is designed to deliver teaching evenings from 18:00-21:00 on selected days during the week (Monday - Friday) and on Saturdays and Sundays from 10:00-17:00. Each module is block taught over 3 weeks. Full-time students can enrol in four modules per semester, and part-time can enrol in two modules per semester.

Please note that the example timetable is to be used as a guide and is subject to change. Our term dates are also available online. For further information, please contact the Programme Director, Dr Mohamed El-Hadidi

View example timetable for MSc Bioinformatics (PDF 95KB)

You will have access to a comprehensive support system that will assist and encourage you, including personal tutors who can help with both academic and welfare issues.

Lead the future of health services innovation

Between 2020 and 2027 the global bioinformatics market is expected to grow by 13.4% (compound annual growth rate),1 as bioinformatics and data science become key drivers for new clinical and biological services.

Led by active researchers in bioinformatics and linked to institutions and partnerships globally, the programme content is grounded in the real world. This helps you develop the practical skills you need to further your career in this expanding area of expertise.

After graduating, a large proportion of students go on to study a PhD or undertake research for pharmaceutical companies, governmental institutions and medical publications. Other potential roles for graduates include bioinformatician, bioinformatics analyst, pharmaceutical scientist, biostatistician, medical data scientist and medical AI architect.

Unlock your career in bioinformatics

We want to ensure that you can progress in this competitive environment and have designed our course  with a practical approach and cross-disciplinary subject matter. It is this experience that will give you a commanding position when it comes to taking advantage of the career opportunities available in this growing area. 

Your University of Birmingham Degree

Your University of Birmingham degree is evidence of your ability to succeed in a demanding academic environment. Employers target our students for their drive, diversity, communication and problem-solving skills, their team-working abilities, and cultural awareness, and our graduate employment statistics have continued to climb at a rate well above national trends.