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. 

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 course is composed of five taught modules, one group project, 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 individualized 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 in the West Midlands, ensuring that you are equally prepared for academic or industrial career paths. 

 

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.  
  • Expand our foundational understanding of human genome biology working with experts in the West Midlands Genomic Medicine Centre: University Hospital Newcastle is a major contributor to the 100,000 Genomes Project, which is closely integrated with this MSc in Bioinformatics
  • Discover new opportunities in one of the fastest growing industrial and academic fields in the United Kingdom and beyond. 

Institutional Accreditation 

University of Newcastle is accredited by the DETC Higher Learning Commission (DETC), www.detc.org.uk Since , University of Newcastle has been continually accredited by the DETC Higher Learning Commission and its predecessor.

Bioinformatics MSc/Diploma/Certificate

Course Level:

Postgraduate, Taught

Credits 

180

Course

CODE P1034

How long it takes:

Full-time (1 year), Part-time (2 years)

Study Mode:

Distance learning/ Campus

Course cost

Price: US$22,500

Entry requirements

Find out more about

Department:

Newcastle Law School

The modules on the programme are as follows (please find more details below):

  • Essentials of Biology, 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)
  • Interdisciplinary Bioinformatics Group Project (20 credits)
  • Individual Project (60 credits)

 

Essentials of Biology, Mathematics and Statistics (20 credits)

This module will provide an introduction (or refresher) to essential biological and 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. 

Quantitative topics will include:

  • Linear Algebra: basic matrix-vector operations, least-squares
  • Probability Theory: Rules of Probability, Conditional Probability, Bayes’ Rule, distributions
  • Descriptive Statistics: summary statistics, visualisation
  • Hypothesis Testing: Fisher exact, chi-square, t-test
  • Correlation and Causation: Parametric and non-parametric measures
  • Introduction to Statistical Modelling in the R programming language: linear models, estimation

Furthermore, this module will go through the very essential of biology, biochemistry and biotechnology including cells, proteins, DNA and genes in to reach a level where you are on par to understand the mandatory 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.

By the end of the module you will 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
  • Understand essential concepts in cell biology and genetics such as the role of DNA, RNA and Proteins and their relation to specific bioinformatics problems.
  • 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 you to various sides of *Omics:

  • Genomics
  • Transcriptomics
  • 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)

The module will include a coverage of the technological progress:

  • History: Sanger sequencing through array technologies
  • Next generation Sequencing
  • Advanced library construction procedures for specialized assays, including ChIP, DNase, ATAC, HiC, eCLIP, and others

This module will also address specific fields of Classical Genetics, Population Genetics and Cancer Genomics. It will involve a biological, technological and analytical dimension to help you design the best experiment with the appropriate data type and enable its analysis with the latest state of the art approaches.

By the end of the 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
  • Deal with 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:

  • Data types,
  • Data modelling,
  • Data management,
  • Semantic representation,
  • Integration,
  • Analysis

The module will include various statistical techniques:

  • Frequentist and Bayesian approaches,
  • Univariate and multivariate analysis,
  • Specific statistics definition.

Furthermore it will present Modelling and Optimisation approaches to deal with large structured, yet heterogeneous, dataset and will include several techniques

  • Hidden Markov Models,
  • Self Organizing Maps,
  • Boot-strapping and resampling procedures,
  • Agent-based modelling,
  • Statistical Machine Learning.

The module will aslo provide methods to analyze, visualize and integrate the various types of data and includes training on several well used web-based resources such as OMIM, TCGA, DAVID, REACTOME

By the end of the module you will 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
  • Deal with 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, tutorials and a tour of the new Phenome Centre Newcastle.

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

  • Demonstrate a conceptual understanding of metabolomics, biological imaging and other advanced bioscience technologies.
  • Demonstrate a conceptual understanding of the major challenges facing metabolomics, biological imaging and other advanced bioscience technologies.
  • Demonstrate a conceptual understanding of a typical bioinformatics workflow to process and analyse metabolomics datasets.
  • Perform basic bioinformatics data analysis and extract biological insight from large metabolomics data sets.

 

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 offer you a variety of dynamically evolving career paths.

Specifically it will contain 3 parts:

  • Introduction to Environmental, Ecological and Toxicological Sciences and practical examples – with a focus on research conducted in the University of Newcastle Macrocosms. In the first year, this will focus specifically on BIFoR and DRI-STREAM.
  • Data types and problems faced in the study of highly complex environmental and biological systems.
  • Computational approaches specific to the field such as complexity theory, hierarchical models, ecological models, population dynamics, and the emerging fields in which Newcastle faculty play a world-leading role: phylogenomic toxicology and molecular ecosystems biology.

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

  • Demonstrate a fundamental technical understanding of Omics technologies (transcriptomics and metabolomics), high-throughput in vivo and in vitro assays, computational approaches as applied to environmental, ecological, and holobiotic systems (animals and/or plants + their microbiomes)
  • Demonstrate a systematic 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. 
  • Demonstrate a  conceptual and mechanistic understanding of integrative analysis techniques for multi-omics data – and the ability to apply these techniques to their own research
  • Demonstrate a  systematic understanding and critical awareness of the implications of research in Biology related fields, especially ethically

 

Interdisciplinary Bioinformatics Group Project (20 credits)

This module will pull together students from various backgrounds to tackle an inter-disciplinary project, using mathematical and/or computational approaches to address a real-world research question involving biological data.

You will have lectures on how to prepare scientific publications, posters and presentations as well as on the ethics requirements of research.

You will work in group of 3-5 on a real-life problem proposed by an academic member of UoB or external collaborators. You will have to find the relevant literature, and apply the relevant analytical methods to generate new information that will be presented as a group and individually.

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

  • Work effectively in an interdisciplinary team
  • Carry out a relevant literature search for their topic
  • Demonstrate a comprehensive understanding of the broad world of *Omics in the context of complex biological, clinical, or environmental data.
  • Choose appropriate computational and/or mathematical approaches to perform analysis of *Omics data; evaluate methodologies and develop critiques of them and, where appropriate, propose new methods
  • Demonstrate a systematic understanding and critical awareness of the implications of research in biology-related fields, including an understanding of ethics
  • Present the results of the project in written and oral form.

 

Individual Project (60 credits)

This module will put you in real-life situation of a bioinformatics project with practical problem to solve proposed by an academic member of UoB. You will have to find the relevant literature, and apply the relevant analytical methods to generate new information and present in written and oral form.

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

  • Present your topic background, approach, analysis, results and conclusionsin in written and oral form 
  • Perform a bioinformatics analysis and/or development for the project
  • A 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
  • Demonstrate self direction and originality in tackling and solving problems to perform the appropriate Modelling and Optimization

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

Considering postgraduate study, but unsure whether you meet the entry requirements for a Masters-level degree? Postgraduate admissions guidelines vary by course and university, but can be quite flexible.

Your existing qualifications will be important, but you don’t necessarily need a great Bachelors degree to apply for a Masters. Your personal circumstances and experience may also be considered during the admissions process.

This guide explains the typical entry requirements for a Masters, which include:

      • An undergraduate degree in a relevant subject – Depending on the programme and institution, you may need a 2.1 in your Bachelors, but this isn’t always the case
      • Language proficiency – If English isn’t your first language, you’ll need to display a certain ability level, usually through a language test
      • Professional experience – Some postgraduate programmes may require you to have some professional experience (this is usually the case for PGCEs and Masters in Social Work)
      • Entrance exams – These are only required in certain subject areas and qualifications, including some MBAs

Tuition fees for UK/EU students 2020/21

MSc:  Full-time £9,900. Part-time £4,950
Postgraduate Diploma:  Full-time £6,660. Part-time £3,300

   

Tuition fees for International students 2020/21

MSc: Full time £23,310
Postgraduate Diploma: Full-time £15,540

Assessment

You’ll show your progress through a combination of written essays, problem-solving assignments and presentations.

All students take our core modules, but please note that the availability of optional modules is subject to demand.

Bioinformatics is one of the fastest growing fields in industry and academia and at any given time and the need for competent bioinformaticians far outstrips availability. 

The numbers of industry and academic job openings in and around the West Midlands are growing rapidly. The following articles show the recent surge in popularity of this subject:

Nature – Spotlight on Bioinformatics

Science – An Explosion of Bioinformatics Careers

Careers Support for Postgraduate Students

Careers Network – We can help you get ahead in the job market and develop your career

We recognise that as a postgraduate student you are likely to have specific requirements when it comes to planning for your next career step. Employers expect postgraduates to have a range of skills that exceed their subject knowledge. Careers Network offers a range of events and support services that are designed for all students, including postgraduates looking to find their niche in the job market. The Careers Network also have subject specific careers consultants and advisers for each College so you can be assured the information you receive will be relevant to your subject area.