Masters Degrees (Bioinformatics)

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 with a focus on genomic bioinformatics. You will develop key skills for the analyses of omics data including genomics data from next generation sequencing technologies. Additional skills around emerging omics including metabolomics and proteomics will also be developed.

This programme has been designed with the needs of academic research, biotechnology and the pharmaceutical and health care industries in mind. We will provide instruction in computational and statistical biosciences and students will foster these additional complementary skills required to enable individuals to work effectively within a multidisciplinary bioinformatics arena.

Distinctive features

• This course was first established over a decade ago in response to the emerging informatics needs of the genetics and genomics communities following the completion of the first drafts of the human genome project. Subsequent advances in research technologies and analytic approaches have dictated the continuing evolution of this programme to provide contemporary instruction in these new essential skills.

• Providing a strong platform for students entering from the biological, mathematical or computational sciences, this course provides modules in core complementary areas such as in computation/scripting, statistics and molecular biology; the fundamental building blocks necessary to succeed in bioinformatic analysis and interpretation.

• As an introduction – you will be taught essential organisational and coding skills required for effective bioinformatics and biostatistical analysis.

• One of the unique components of this course is the extended instruction in statistics provided by the Statistics for Bioinformatics and Genetic Epidemiology module.

• You will also be introduced to the molecular and cellular biology behind the data. This is invaluable if you are entering from a non-life sciences background to make informed decisions around data interpretation.

• You will extend your bioinformatics studies by focusing on next generation sequencing technologies and other developing omics platforms such as proteomics and metabolomics.

We are committed to developing transferable skills and to improving graduate employability. We want highly capable graduate informaticians who can fulfil the growing bioinformatics needs of local, national and international employers.

Structure

The course can be completed in one year with full-time study or in three years by part-time study.

Both full-time and part-time students register initially for the MSc Bioinformatics and Genetic Epidemiology

A Postgraduate Certificate exit point is available for students successfully completing 60 credits of the taught element (module restrictions apply).

A Postgraduate Diploma exit point is available for students successfully completing 120 credits of the taught element (module restrictions apply).

Core modules:

Computing for Bioinformatics and Genetic Epidemiology
Statistics for Bioinformatics and Genetic Epidemiology
Introduction to Bioinformatics
Case Studies in Bioinformatics and Biostatistics
Next Generation Sequencing
Protein Biology and Omics
Dissertation in Bioinformatics

Teaching

The programme is delivered as face-2-face learning. You will find course materials, links to related materials and assessments via Cardiff University’s Virtual Learning Environment (VLE) ‘Learning Central'

Career Prospects

This programme has been designed with the needs of academic research, the biotechnology, pharmaceutical and health care industries in mind. Instruction in computational and statistical biosciences will enable individuals to work effectively within a multidisciplinary bioinformatics arena.

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The aim of this programme is to provide individuals with the skills to explore, analyse and interpret contemporary biological data. This course offers Masters level instruction in Bioinformatics and Genetic Epidemiology.

You will develop key skills necessary to analyse genomics data for gene discovery, including genomewide association studies (GWAS) and post-GWAS applications such as gene-set and polygenic genetic epidemiology analysis.

This programme has been designed for biomedical scientists and informaticians looking to undertake a career in academic research, the biotechnology, pharmaceutical or health care industries.

Distinctive features

• This course was first established over a decade ago as a response to the emerging informatics needs of the genetics and genomics communities following the completion of the first drafts of the human genome project. Subsequent advances in research technologies and analytic approaches have dictated the continuing evolution of this programme to provide contemporary instruction in these new essential skills

• Providing a strong platform for students entering from the biological, mathematical or computational sciences, this course provides modules in core complementary areas such as in computation/scripting, statistics and molecular biology; the fundamental building blocks necessary to succeed in bioinformatic analysis and interpretation

• As an introduction – you will be taught essential organisational and coding skills required for effective bioinformatics and biostatistical analysis.

• One of the unique components of this course is the extended instruction in statistics provided by the Statistics for Bioinformatics and Genetic Epidemiology module.

• You will also be introduced to the molecular and cellular biology behind the data within the Introduction to Bioinformatics Module. This is invaluable if you are entering from non-life sciences backgrounds to make informed decisions around data interpretation.

• You will extend your bioinformatics and biostatistics studies by focusing on the genetic epidemiology and gene discovery approaches including GWAS and copy-number variation (CNV) analysis, and post-GWAS approached such as pathway/network, gene-set and polygenic epidemiological methods.

• We are committed to developing transferable skills and to improving graduate employability. We want highly capable graduate informaticians who can fulfil the growing bioinformatics needs of local, national and international employers.   

Structure

The course can be completed in one year with full-time study or in three years by part-time study.

Both full-time and part-time students register initially for the MSc Bioinformatics and Genetic Epidemiology.

A Postgraduate Certificate exit point is available for students successfully completing 60 credits of the taught element (module restrictions apply).

A Postgraduate Diploma exit point is available for students successfully completing 120 credits of the taught element (module restrictions apply).

Core modules:

Computing for Bioinformatics and Genetic Epidemiology
Statistics for Bioinformatics and Genetic Epidemiology
Introduction to Bioinformatics
Case Studies in Bioinformatics and Biostatistics
Genetic Epidemiology - Association and Linkage
Post-GWAS Genetic Epidemiology
Dissertation in Genetic Epidemiology

Teaching

The programme is delivered as face-2-face learning. Students will find course materials, links to related materials and assessments via Cardiff University’s Virtual Learning Environment (VLE) ‘Learning Central'

Career Prospects

This programme has been designed with the needs of academic research, the biotechnology, pharmaceutical and health care industries in mind. Instruction in computational and statistical biosciences will enable individuals to work effectively within a multidisciplinary bioinformatics and genetic epidemiology arena.

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Programme description

Bioinformatics is about the application of computer-based approaches to understanding biological processes. Our programme will introduce you to the current methods used to interpret the vast amounts of data generated by modern high-throughput technologies.

The aim of this MSc is to equip you with a strong background in biology, plus the computing skills and knowledge necessary to navigate the vast wealth of modern biological data. On completing this programme you will be able to take up PhD studies or bioinformatics posts in academia or in industry.

The programme covers programming skills, statistical analysis and database science as well as bioinformatics. Option courses allow you to specialise in several aspects of bioinformatics.

Programme structure

The MSc comprises two semesters of taught courses followed by a research project and dissertation. The project is a key element in deciding how your career in bioinformatics should develop further. Teaching is through lectures, tutorials, seminars, computer practicals and lab demonstrations.

Compulsory courses:

Bioinformatics Programming & System Management
Bioinformatics Research Proposal
MSc Dissertation (Bioinformatics)
Statistics & Data Analysis

Optional courses:

Bioinformatics 1
Human–Computer Interaction
Information Processing in Biological Cells
Molecular Modelling and Database Mining
Quantitating Drug Binding
Text Technologies
Bioinformatics Algorithms
Bioinformatics 2
Computational Systems Biology
Data Mining and Exploration
Functional Genomic Technologies
Introduction to Website and Database Design for Drug Discovery
Molecular Phylogenetics
Next Generation Genomics
Software Architecture, Process, and Management
Drug Discovery
Introduction to Java Programming

Research:
The research project is carried out independently, but under the guidance of a supervisor, during the summer, with results presented in a dissertation. A wide range of projects is available through both the School of Biological Sciences and the School of Informatics.

Career opportunities

The programme is good preparation for further academic research or for technical or managerial roles in various commercial sectors, from medical electronics to defence.

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This Masters in Bioinformatics is a new, exciting and innovative programme that has grown out of our well-regarded MRes in Bioinformatics. Bioinformatics is a discipline at the interface between biology and computing and is used in organismal biology, molecular biology and biomedicine. This programme focuses on using computers to glean new insights from DNA, RNA and protein sequence data and related data at the molecular level through data storage, mining, analysis and display - all of which form a core part of modern biology.

Why this programme

◾Our programme emphasises understanding core principles in practical bioinformatics and functional genomics, and then implementing that understanding in a series of practical-based elective courses in Semester 2 and in a summer research project.
◾You will benefit from being taught by scientists at the cutting edge of their field and you will get intensive, hands-on experience in an active research lab during the summer research project.
◾Bioinformatics and the 'Omics' technologies have evolved to play a fundamental role in almost all areas of biology and biomedicine.
◾Advanced biocomputing skills are now deemed essential for many PhD studentships/projects in molecular bioscience and biomedicine, and are of increasing importance for many other such projects.
◾The Semester 2 elective courses are built around real research scenarios, enabling you not only to gain practical experience of working with large molecular datasets, but also to see why each scenario uses the particular approaches it does and how to go about organizing and implementing appropriate analysis pipelines.
◾You will be based in the College of Medical, Veterinary & Life Sciences, an ideal environment in which to train in bioinformatics; our College has carried out internationally-recognised research in functional genomics and systems biology.
◾The new programme reflects the development and activities of 'Glasgow Polyomics'. Glasgow Polyomics is a world-class facility set up in 2012 to provide research services using microarray, proteomics, metabolomics and next-generation DNA sequencing technologies. Its scientists have pioneered the 'polyomics' approach, in which new insights come from the integration of data across different omics levels.
◾In addition, we have several world-renowned research centres at the University, such as the Wellcome Trust Centre for Molecular Parasitology and the Wolfson Wohl Cancer Research Centre, whose scientists do ground-breaking research employing bioinformatic approaches in the study of disease.
◾You will learn computer programming in courses run by staff in the internationally reputed School of Computing Science, in conjunction with their MSc in Information Technology.

Programme structure

Bioinformatics helps biologists gain new insights about genomes (genomics) and genes, about RNA expression products of genes (transcriptomics) and about proteins (proteomics); rapid advances have also been made in the study of cellular metabolites (metabolomics) and in a newer area: systems biology.

‘Polyomics’ involves the integration of data from these ‘functional genomics’ areas - genomics, transcriptomics, proteomics and metabolomics - to derive new insights about how biological systems function.

The programme structure is designed to equip students with understanding and hands-on experience of both computing and biological research practices relating to bioinformatics and functional genomics, to show students how the computing approaches and biological questions they are being used to answer are connected, and to give students an insight into new approaches for integration of data and analysis across the 'omics' domains.

On this programme, you will develop a range of computing and programming skills, as well as skills in data handling, analysis (including statistics) and interpretation, and you will be brought up to date with recent advances in biological science that have been informed by bioinformatics approaches.

The programme has the following overall structure
◾Core material - 60 credits, Semester 1, made up of 10, 15 and 20 credit courses.
◾Elective material - 60 credits, Semester 2, students select 4 courses (two 10 credit courses and two 20 credit courses) from those available.
◾Project - 60 credits, 14 weeks embedded in a research group over the summer.

Core and optional courses

◾Programming (Java)
◾Database Theory and Application
◾Foundations of Bioinformatics
◾Omics and Systems Approaches in Biology
◾These 4 courses are obligatory for those taking the MSc degree and the PgDip; they are also obligatory for those with no prior programming experience taking the PgCert.
◾60-credit summer research project lasting 14 weeks - this is also obligatory for those taking the MSc programme; normally this will be with one of the research laboratories in Glasgow associated with the programme, but there is also the opportunity to study in suitable laboratories in other parts of the world.

Optional courses include:
◾RNA-seq and next generation transcriptomics
◾Metagenomics
◾Pathogen Polyomics
◾Using Chemical Structure Databases in Drug Discovery for Protein Targets
◾Identification of disease-causing genetic variants
◾A range of more general biology and computing biology courses are also available in semester 2.

Career prospects

Most of our graduates embark on a research career path here in the UK or abroad using the skills they've acquired on our programme - these skills are now of primary relevance in many areas of modern biology and biomedicine. Many are successful in getting a PhD studentship. Others are employed as a core bioinformatician (now a career path within academia in its own right) or as a research assistant in a research group in basic biological or medical science. A postgraduate degree in bioinformatics is also valued by many employers in the life sciences sector - e.g. computing biology jobs in biotechnology/biosciences/neuroinformatics/pharma industry. Some of our graduates have entered science-related careers in scientific publishing or education; others have gone into computing-related jobs in non-bioscience industry or the public sector.

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Program overview

The master of science degree in bioinformatics provides students with a strong foundation in biotechnology, computer programming, computational mathematics, statistics, and database management. Graduates of the program are well-prepared for careers in the biotechnology, bioinformatics, pharmaceutical, and vaccine industries. Based on consultation with individuals within the industry nationwide, the job market is rich with opportunities for those who obtain a graduate degree in bioinformatics, particularly when coupled with industry-sponsored research as thesis work. This research provides exposure to real-world problems—and their solutions—not otherwise attainable in an academic setting.

The program provides students with the capability to enter the bioinformatics workforce and become leaders in the field. The curriculum is designed to fulfill the needs of students with diverse educational and professional backgrounds. Individuals entering the program typically have degrees in biology, biotechnology, chemistry, statistics, computer science, information technology, or a related field. The program accommodates this diversity in two ways. First, a comprehensive bridge program exists for students who need to supplement their education before entering the program. Second, the program itself consists of two tracks, one for students with backgrounds in the life sciences and one for those with backgrounds in the computational sciences. Regardless of the track pursued, students are prepared to become professional bioinformaticists upon graduation. The program is offered on a full- or part-time basis to fulfill the needs of traditional students and those currently employed in the field.

Plan of study

A minimum of 30 semester credit hours is required for completion of the program. A number of graduate electives are offered for students to pursue areas of personal or professional interest. In addition, every student is required to complete a research project that addresses a relevant and timely topic in bioinformatics, culminating in a thesis. Graduate electives may be chosen from relevant RIT graduate courses.

Curriculum

Bioinformatics, MS degree, typical course sequence:
First Year
-Bioinformatics Seminar
-Graduate Bioinformatics Algorithms
-Graduate Ethics in Bioinformatics
Choose one of the following
-Database Management for the Sciences
-Cell and Molecular Genetics
-Graduate Elective*
-Graduate Statistical Analysis for Bioinformatics
-Graduate Molecular Modeling and Proteomics
-Graduate Elective*
Second Year
-Thesis

* Any graduate level course deemed related to the field of bioinformatics by the program director. See website for details.

Other admission requirements

-Have an undergraduate GPA of 3.2 or higher (on a 4.0 scale).
-Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
-Submit scores from the Graduate Record Examination (GRE), and complete a graduate application.
-International applicants whose primary language is not English must submit scores from the Test of English as a Foreign Language (TOEFL). A minimum score of 79 (Internet-based) is required. International English Language Testing System (IELTS) scores are accepted in place of the TOEFL exam. Minimum scores will vary; however, the absolute minimum score required for unconditional acceptance is 6.0. For additional information about the IELTS, please visit http://www.ielts.org.

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MSc Bioinformatics

The two-year MSc Bioinformatics concerns a new scientific discipline with roots in computer science, statistics and molecular biology. Bioinformaticians apply information technology to store, retrieve and manipulate these data and employ statistical methods capable of analysing large amounts of biological data to predict gene functions and to demonstrate relationships between genes and proteins.

Programme summary

DNA contains information about life, but how is this information used? Biological data, such as DNA and RNA sequence information produced by next-generation sequencing techniques, is accumulating at an unprecedented rate. Life scientists increasingly use bioinformatics resources to address their specific research questions. Bioinformaticians bridge the gap between complex biological research questions and this complex data. Bioinformaticians use and develop computational tools to predict gene function(s) and to demonstrate and model relationships between genes, proteins and metabolites in biological systems. Bioinformatics is an interdisciplinary field that applies computational and statistical techniques to the classification, interpretation and integration of large-scale biological data sets. If different data types are joined then complex interactions in biological systems can be studied. The use of systems biology methods to study complex biological interactions offers a wealth of possibilities to understand various levels of aggregation and enables control of biological systems on different scales. Systems biology approaches are therefore quickly gaining importance in many disciplines of life sciences, such as in applied biotechnology where these methods are now used to develop strategies for improving production in fermentation. Other examples include bioconversion and enzymatic synthesis, and in the study of human metabolism and its alterations where systems biology methods are applied to understand a variety of complex human diseases, including metabolic syndromes and cancer. The Wageningen Master programme focuses on the practical application of bioinformatics and systems biology approaches in many areas of the Life Sciences. To ensure that students acquire a high level of understanding of modelling and computing principles, the students are trained in the fundamentals of database management, computer programming, structural and functional genomics, proteomics and systems biology methods. This training includes advanced elective courses in molecular biology and biostatistics.

Thesis tracks

Bioinformatics
The bioinformatics track focuses on the practical application of bioinformatics knowledge and skills in molecular life sciences. It aims at creating and using bioinformatics resources to address specific research questions. The knowledge and skills gained can be applied in many life science disciplines such as molecular & cell biology, biotechnology, (human) genetics, health & medicine and environmental & biobased technology.

Systems Biology
The systems biology track focuses on the study of the complex interactions in biological systems and on the emerging properties derived from these. Systems biology approaches to complex biological problems offer a wealth of possibilities to understand various levels of aggregation. It enables control of biological systems on completely different scales, ranging from the molecular cellular level to marine, plant, or animal ecosystems to a desired state. The knowledge and skills gained can be applied in many life science disciplines including molecular & cell biology, applied biotechnology, genetics, medicine and vaccine development, environmental and biobased technology.

Your future career

Bioinformatics and Systems Biology are new fast growing biology based interdisciplinary fields of research poorly served by the traditional curricula of Life Sciences. As demand has outpaced the supply of bioinformaticians, the first job after graduation is often a PhD project at a research institute or university. It is expected that five years after graduation, about one third will stay employed as a scientist at a university or research centre, while the others choose for careers at research-oriented pharmaceutical and biotechnological companies.

Alumnus Tom van den Bergh.
"It is sometimes difficult for doctors to diagnose genetic diseases caused by missense mutations. A missense mutation does not necessarily mean that you have the gene-associated disease and will become ill since not all missense mutations lead to appreciable protein changes." Tom created a database for Fabry’s disease for his final thesis. He wrote a computer programme that reads publications and stores all information about Fabry mutations in its database. Genetic researchers can, in turn, quickly access this database to determine if the mutation they found in a patient has already been addressed in literature and what the effects were.

Related programmes:
MSc Biotechnology
MSc Molecular Life Sciences
MSc Plant Biotechnology

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Bioinformatics is changing as high throughput biological data collection becomes more Systems oriented. This means that employers are looking for people able to work across the traditional disciplines.

The MSc in Bioinformatics and Systems Biology at Manchester reflects these exciting developments, providing an integrated programme taught by researchers at the forefront of fields spanning Bioinformatics, Genomics and Systems Biology.

Bioinformatics has been an identifiable discipline for more than a decade, driven by the computational demands of high volumes of biological data. It incorporates both the development and application of algorithms to decipher biological relationships.

Enormous success has been achieved, for example in defining homologous families of sequences at the DNA, RNA, and protein levels. However, our appreciation of function is changing rapidly as experimental analysis scales up to cellular and organismal viewpoints.

At these levels, we are interested in the properties of a network of interacting components in a system, as well as the components themselves. The concepts or Systems Biology and Bioinformatics complement each other, and both are addressed in this course.
This combination reflects the current skills sought in academic and industrial (eg pharmaceutical) settings. An important feature is the extent to which computational biology is concerned with finding patterns in biological data, and generating hypotheses that feed back into experiments.

Teaching is delivered by more than ten academic staff working in the fields of Bioinformatics, Genomics and Systems Biology, representing the breadth and depth of these areas.

Aims

The Bioinformatics and Systems Biology course provides students with theoretical and practical knowledge of methods to analyse and interpret the data generated by modern biology. This involves the appreciation of biochemistry and molecular biology, together with the techniques of IT and computer science that will prepare students for multidisciplinary careers in research.

To achieve this there are three main objectives:
-Provide biological background to the data types of Genomics, Proteomics and Metabolomics.
-Develop the computational and analytical understanding necessary as a platform for processing biological data.
-Demonstrate applications and worked examples in the fields of Bioinformatics and System Biology, integrating with student involvement through project work.

Coursework and assessment

Research projects provide experience in carrying through a substantive research project including the planning, execution and communication of original scientific research. They are assessed by written report.

Taught units involve lectures, practicals and problem classes and are assessed through both coursework and exam.

Course unit details

The taught part of the course runs from September to April and consists of 60 credits delivered from four 15 credit units.
-Bioinformatics
-Programming Skills
-Computational Systems Biology
-Experimental Design and Statistics

You will undertake two research project, each of 60 credits, in Semester 2 and the summer. Additionally tutorials and the Graduate Training Programme (skills development) will run through the whole programme.

Career opportunities

Graduates acquire a wide range of subject specific and transferable skills and gain extensive research experience. Around half of each class find PhD positions straight after the MSc, whilst others build upon their training to enter careers in biology and IT. The combination of Systems Biology and Bioinformatics addressed in this course reflects the current skills sought in academic and industrial (e.g. pharmaceutical) settings.

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The MSc in Bioinformatics and Computational Biology at UCC is a one-year taught masters course commencing in September. Bioinformatics is a fast-growing field at the intersection of biology, mathematics and computer science. It seeks to create, advance and apply computer/software-based solutions to solve formal and practical problems arising from the management and analysis of very large biological data sets. Applications include genome sequence analysis such as the human genome, the human microbiome, analysis of genetic variation within populations and analysis of gene expression patterns.

As part of the MSc course, you will carry out a three month research project in a research group in UCC or in an external university, research institute or industry. The programming and data handling skills that you will develop, along with your exposure to an interdisciplinary research environment, will be very attractive to employers. Graduates from the MSc will have a variety of career options including working in a research group in a university or research institute, industrial research, or pursuing a PhD.

Visit the website: http://www.ucc.ie/en/ckr33/

Course Detail

This MSc course will provide theoretical education along with practical training to students who already have a BSc in a biological/life science, computer science, mathematics, statistics, engineering or a related degree.

The course has four different streams for biology, mathematics, statistics and computer science graduates. Graduates of related disciplines, such as engineering, physics, medicine, will be enrolled in the most appropriate stream. This allows graduates from different backgrounds to increase their knowledge and skills in areas in which they have not previously studied, with particular emphasis on hands-on expertise relevant to bioinformatics:

- Data analysis: basic statistical concepts, probability, multivariate analysis methods
- Programming/computing: hands-on Linux skills, basic computing skills and databases, computer system organisation, analysis of simple data structures and algorithms, programming concepts and practice, web applications programming
- Bioinformatics: homology searches, sequence alignment, motifs, phylogenetics, protein folding and structure prediction
- Systems biology: genome sequencing projects and genome analysis, functional genomics, metabolome modelling, regulatory networks, interactome, enzymes and pathways
- Mathematical modelling and simulation: use of discrete mathematics for bioinformatics such as graphs and trees, simulation of biosystems
- Research skills: individual research project, involving a placement within the university or in external research institutes, universities or industry.

Format

Full-time students must complete 12 taught modules and undertake a research project. Part-time students complete about six taught modules in each academic year and undertake the project in the second academic year. Each taught module consists of approximately 20 one-hour lectures (roughly two lectures per week over one academic term), as well as approximately 10 hours of practicals or tutorials (roughly one one-hour practical or tutorial per week over one academic term), although the exact amount of lectures, practicals and tutorials varies between individual modules.

Assessment

There are exams for most of the taught modules in May of each of the two academic years, while certain modules may also have a continuous assessment element. The research project starts in June and finishes towards the end of September. Part-time students will carry out their research project during the summer of their second academic year.

Careers

Graduates of this course offer a unique set of interdisciplinary skills making them highly attractive to employers at universities, research centres and in industry. Many research institutes have dedicated bioinformatics groups, while many 'wet biology' research groups employ bioinformaticians to help with data analyses and other bioinformatics problems. Industries employing bioinformaticians include the pharmaceutical industry, agricultural and biotechnology companies. For biology graduates returning to 'wet lab' biology after completing the MSc course, your newly acquired skills will be extremely useful. Non-biology graduates seeking non-biology positions will also find that having acquired interdisciplinary skills is of great benefit in getting a job.

How to apply: http://www.ucc.ie/en/study/postgrad/how/

Funding and Scholarships

Information regarding funding and available scholarships can be found here: https://www.ucc.ie/en/cblgradschool/current/fundingandfinance/fundingscholarships/

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The Bioinformatics MSc combines foundational skills in bioinformatics with specialist skills in computing programming, molecular biology and research methods. Our unique, interdisciplinary course draws together highly-rated teaching and research expertise from across the University, equipping you for a successful career in the bioinformatics industry or academia.

This interdisciplinary course is based in the School of Computing Science and taught jointly with the School of Biology, School of Mathematics and Statistics, Institute of Cell and Molecular Biosciences and the Institute of Genetic Medicine. It is designed for students from both biological science and computational backgrounds. Prior experience with computer programming is not required and we welcome applications from students with mathematical, engineering or other scientific backgrounds.

Our graduates have an excellent record of finding employment (around 90%). Recent examples have included:
-Bioinformatician at the Medical Research Council
-Technical consultant at Accenture
-Bioinformatics technician at Barcelona Supercomputing Centre

Our course structure is highly flexible and you can tailor it to your own skills and interests. Half of the course is taught and the remainder is dedicated to a research project.

As research is a large component of this course, our emphasis is on delivering the research training you will need to meet the demands of industry and academia now and in the future. Our research in bioinformatics, life sciences, computing and mathematics is internationally recognised. We have an active research community, comprising several research groups and three research centres.

You will be taught by academics who are successful researchers in their field and publish regularly in highly-ranked bioinformatics journals. Our experienced and helpful staff will be happy to offer support with all aspects of your course from admissions to graduation and developing your career.

The course is part of a suite of related programmes that include:
-Synthetic Biology MSc
-Computational Neuroscience and Neuroinformatics MSc
-Computational Systems Biology MSc

All four courses share core modules. This creates a tight-knit cohort that has encouraged collaborations on projects undertaking interdisciplinary research.

Delivery

Semester one combines bioinformatics theory and application with the computational and modelling skills necessary to undertake more specialist modules in semester two. We provide training in mathematics and statistics and, for those without a biological first degree, we will also provide molecular biology training. Some of these modules are examined in January at the end of semester one.

Semester two begins with two modules that focus heavily on introducing subject-specific research skills. These two modules run sequentially, in a short but intensive mode that allows you time to focus on a single topic in depth. In the first of the second semester modules you learn how to analyse data arising from post-genomic studies such as microarray analysis, proteomic analysis and RNAseq. All of the semester two modules are examined by in-course assessment - there are no formal examinations in these modules.

Project work

Your five month project gives you an opportunity to develop your knowledge and skills in depth, and to work in a research or development team. You will have one-to-one supervision from an experienced member of staff, supported with supervision from industry partners as required.

The project can be carried out:
-With a research group at Newcastle University
-With an industrial sponsor
-With a research institute
-At your place of work

Accreditation

We have a policy of seeking British Computer Society (BCS) accreditation for all of our degrees, so you can be assured that you will graduate with a degree that meets the standards set out by the IT industry. Studying a BCS-accredited degree provides the foundation for professional membership of the BCS on graduation and is the first step to becoming a chartered IT professional.

The School of Computing Science at Newcastle University is an accredited and a recognised Partner in the Network of Teaching Excellence in Computer Science.

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Bioscience is increasingly data driven, as new bioanalytical 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, a discipline in which there is a serious shortage of trained people. Without assuming any prior informatics experience, this course gets biologists up to speed with essential bioinformatics skills and provides the opportunity to apply these in a cutting edge research project. The course is taught by QMUL academics who are actively engaged in developing bioinformatics tools and applying them in areas such as genome sequencing, proteomics, evolution, ecology, psychology, cancer, diabetes and other diseases. We have an extensive network of academic and industrial collaborators around the UK and in Europe, who will contribute to teaching, co-supervise projects and provide employment opportunities.

Programme highlights:

- New course, covering the very latest tools and techniques.
- Delivered by experts in the development and application of bioinformatics techniques.
- An innovative group project, collaborating with peers to produce new bioinformatics software.
- Providing the skills and experience that employers need.
- A six month individual thesis project tackling a real world bioinformatics challenge.
- Flexible modes of study: full time, part time, campus-based or online.

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This specialist postgraduate degree provides you with high-quality postgraduate training in bioinformatics. It provides a foundation for the development of essential bioinformatics knowledge and skills, as well as an introduction to the emerging field of systems biology. The course is run in parallel with an MRes course that includes a larger research component.

The programme is designed for students from a range of scientific backgrounds, who want to pursue research training in the interdisciplinary field of bioinformatics and systems biology. It is relevant to those seeking a future career in both academia and industry.

On successful completion of this programme, students from all backgrounds should be able to:

- Understand the core concepts and statistical fundamentals that underpin the field of bioinformatics, most notably in the area of sequence analysis.
- Program in Python, and design and query databases using SQL. Experience of more advanced programming practices (such as software testing and application development) will also be gained.
- Explain core biological concepts (such as genes and genomes, protein structure and function) and growth areas such as Next Generation Sequencing and (at least at an introductory level) systems modelling.

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Applications are invited for the MSc in Bioinformatics and Theoretical Systems
Biology. The programme will provide an interdisciplinary training and applications
are invited from students graduating from any biological, physical, computational
or mathematical first degree course. We are keen to encourage graduates from
numerical and physical sciences to join the course.

This programme will provide students with the necessary skills to produce effective
research in Bioinformatics and Systems Biology. The course, which is based at the
South Kensington campus, has been designed and is taught by staff from the Faculties
Natural Sciences, Engineering (Computing) and Medicine.

In the first term, students take the following courses:
• Bioinformatics and Systems Biology - Introduction to biology; advanced tools for the
analysis of biological data; and approaches for modelling biological systems
• Computing - Python, R, & Unix
• Mathematics & statistical inference - high level algorithms & analysis of large datasets

The remainder of the year is devoted to three full-time research projects,
undertaken under the supervision of researchers at Imperial College.

Wellcome Trust 4 year PhD Programme

Please note there is also a separate funded 4 year PhD programme, supported by the Wellcome Trust, which starts with this Master’s course and then progresses to a three year PhD. The closing date for application is Monday 5 December 2016 for admission in October 2017. Details, including how to apply, can be found at

http://www.imperial.ac.uk/wellcome-bioinformatics-phd/

Applicants must have or be expected to obtain at least an upper second honours
degree or an equivalent overseas qualification. Please be aware that we do not
do any 'wet lab' research as part of our courses, it is purely computer based. If you
are not an EU citizen, we do not have any finance for our MSc. For further details and the application procedure
see:

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There is currently a worldwide shortage in graduates qualified in Bioinformatics and the skills to interpret the data that is going to underpin advances in biology and medicine in 21st Century. With the advent of Personalised Medicine, the demand for specialists in Computational Biology and Bioinformatics will further increase. This gives you the opportunity to build your transferable skill set across a range of cutting edge technologies and start building a career in this central facet of modern biology.

Students completing the MSc course in Bioinformatics and Computational Genomics will have the necessary skills and knowledge to undertake research and development in industry (Biotechnology, Pharmaceutical, Diagnostic companies), in medical research centres and in academic institutions worldwide.

Computational, statistical and machine learning methods form an integral part of modern research in Molecular Biology, Cell Biology, Pharmacology, Public Health Care and in Medicine. The past decade has seen enormous progress in the development of molecular and biomedical technologies. Today’s high-throughput array and sequencing techniques produce data in the range of terabytes on a daily basis and new technologies continuously emerge. This will further increase the stream of data available for biomedical research. For this reason analyzing, visualizing and managing this huge amount of data is a challenging task. The Queen’s MSc course in Bioinformatics and Computational Genomics targets these data-driven challenges of modern science. The course is open to graduates in computer science, life sciences, physics or statistics.

The programme will consist of an Introductory short course (two weeks) in Cell Biology, followed by modules in:

• Genomics & Genetics
• Analysis of Gene Expression
• Scientific Programming & Statistical Computing
• Algorithmic Biology
• Statistical Biology
• Bioimaging Informatics
• Research project : MSc dissertation

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Designed to develop the key skills of a bioinformatics in computer science graduates or talented graduates of life science based subjects the MSc Computational Bioinformatics course is the ideal route into a career in this developing area of science.

Bioinformatics is fundamental to the future development of biological science and is a rapidly developing area.

The University of Wolverhampton has a highly successful Brain Tumour UK neuro-oncology research centre and this course is designed to integrate with research in this area to show applications of bioinformatics to medicine.

The course covers key areas of information retrieval, creation and advancement of databases, algorithms, computational and statistical techniques to solve the practical problems of handling experimental data.

We offer an introduction to the genetic basis of oncology and disease to enable you to understand the needs of medical research scientists.

The course has both group and individual research projects to develop professional skills for a future successful career.

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Do you want to become part of a revolution in biology? Advances in genetic sequencing, mass spectroscopy, and other genomic and molecular biology techniques have led to an explosion in the amount of biological information generated by the scientific community. Now the challenge facing scientists is how to make sense of this vast amount of information. Bioinformatics seeks to make sense of biological processes on all scales from the molecular level to full ecosystems. It applies to a number of fields of study and includes sequence analysis, gene regulation and expression studies, protein structure prediction, modelling of ligand/receptor docking, genome annotation, comparative genomics, quantitative structure-activity relations, biological network analysis, analysis of quantitative trait loci, tracking and measurement of biodiversity, and modelling of community formation in ecosystems. The University of Guelph Bioinformatics program offers three graduate degrees (MBinf, MSc, and PhD) that provides students with the necessary theoretical knowledge and practical experiences to excel in this highly relevant and exciting field of research.

Program Features

The Master of Bioinformatics (MBinf) is a course-based master’s program:
-Designed for students with an honours bachelor’s science degree in biological / life sciences.
-Usually takes 3 semesters.
-Students take 6 courses and write a major research project.
-Applicants wishing to be considered in the first round of admissions for this program must submit their documents by March 31st. A limited second round of admissions may be considered for outstanding late applicants up to July 15th each year.

The MSc in Bioinformatics is a thesis-based master’s program:
-Designed for students with an honours bachelor’s degree with a background in mathematics, statistics, and/or computational sciences.
-Usually takes 5-7 semesters.
-Students take 4 courses and conduct substantial research leading to a thesis.
-Applicants must secure a faculty advisor themselves.

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