Course Overview

The aim of this programme is to train graduates with backgrounds in the molecular life sciences in genomics relevant to medical applications. With continuing advances in the technologies that are used to sequence DNA, medical applications of genomics are becoming increasingly important. It is now possible to diagnose rare genetic diseases from genomic sequences, while sequencing of tumours has become an important means of refining therapeutic choices in cancer treatment. Graduates of this programme will gain core skills in genomics analysis and practical experience of applying these skills to biological samples and data. 

Download programme brochure here.

Applications and Selections

Applications are made online via The Postgraduate Applications Centre (PAC). PAC application code: GYS31 

Selection is based on the candidate's academic record at an undergraduate level and their aptitude for the course.

Who Teaches this Course

Requirements and Assessment

Students are formally assessed through a variety of both continuous assessment and end-of-semester written examinations. Continuous assessment will include written assignments, programming exercises, genomic analyses, group and individual presentations, and case studies, while assessment of the Research Project includes examination of a written thesis, as well as oral presentations, and participation in a research seminar series.

Key Facts

Entry Requirements

Applicants must have achieved a second class honours degree or better in a discipline relating to the molecular life sciences. Qualifying degrees include, but are not limited to, biochemistry, genetics, biomedical science, and biotechnology.

Additional Requirements

Duration

1 year, full-time

Next start date

September 2018

A Level Grades ()

Average intake

15

Closing Date

Please refer to the review/closing date webpage.

Next start date

September 2018

NFQ level

Mode of study

Taught

ECTS weighting

90

Award

CAO

PAC code

GYS31

Course Outline

This is a 12-month 90-credit course consisting of 60 credits of taught modules and a 30 credit research project. Taught modules will be completed by the end of semester II and will consist of 45 credits of core and 15 credits of optional modules. Both the core modules and the set of optional modules available to the student depend on whether the student has a background in the molecular life sciences or the quantitative or computational sciences. From the end of semester II, the student will focus on a full-time basis on an individual research project.

Optional modules (10 credits from the options below)

  • Applied statistics (5 ECTS)
  • Networks (5 ECTS)
  • Data Visualisation (5 ECTS)
  • Advanced and applied immunology (5 ECTS)
  • Molecular and cellular biology of cancer (10 ECTS)

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Modules for 2017-18

Curriculum information relates to the current academic year (in most cases).
Course and module offerings and details may be subject to change.

Glossary of Terms

Credits
You must earn a defined number of credits (aka ECTS) to complete each year of your course. You do this by taking all of its required modules as well as the correct number of optional modules to obtain that year's total number of credits.
Optional
A module you may choose to study.
Required
A module that you must study if you choose this course (or subject).
Semester
Most courses have 2 semesters (aka terms) per year.

Year 1 (90 Credits)

Required MA5111: Genomics Data Analysis I


Semester 1 | Credits: 5

This module is designed to introduce students to both the linux environment and HPC infrastructure for performing genomic analyses. Students will gain experience in reproducible research using a combination of R/Bioconductor/RStudio and Rmarkdown. Students will then be introduced to NGS technologies and data analysis techniques including read QC and alignment algorithms preparing them for assay-specific analysis in the subsequent semester II module.
(Language of instruction: English)

Learning Outcomes
  1. Access and utilise HPC resources to carry out genomics analysis.
  2. Perform statistical analyses using R and visualise the results appropriately.
  3. Write Rmarkdown documents to generate reproducible research reports.
  4. Analyse gene expression microarrays in order to identify differentially expressed genes.
  5. Describe and compare core NGS technologies/platforms.
  6. Perform QC on raw NGS read data and align reads to a reference genome.
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5111: "Genomics Data Analysis I" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required MA5114: Programming for Biology


Semester 1 | Credits: 5

This module provides postgraduate students with foundation and applied programming skills using the Python ecosystem. Topics include flow control, conditional statements, regular expressions and direct interaction/manipulation of the operating system and other programs within Python. Applied activities will involve use of notebooks, plotting and data analytics packages, including the BioPython framework.
(Language of instruction: English)

Learning Outcomes
  1. Access, interpret and apply programming education resources
  2. Formulate an algorithm to solve a problem using computational data
  3. Implement a given algorithm using Python and its associated libraries
  4. Understand how to represent and process, DNA, proteomic and other data formats using Python
  5. Combine third party programs, mediated by a Python scripts, to create pipelines/workflows
Assessments
  • Continuous Assessment (100%)
Teachers
Reading List
  1. "Python for Biologists: A complete programming course for beginners" by n/a
    ISBN: 978-149234613.
    Publisher: CreateSpace Independent Publishing Platform; 1 edition (September 7, 2013)
The above information outlines module MA5114: "Programming for Biology" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required MA5106: Medical Genomics 1


Semester 1 | Credits: 5

This module is designed to provide students with an understanding of the role of genetic variation in human health, and how modern high-throughput genomics techniques can be used to help understand, diagnose, and treat a variety of common and rare genetic disorders. This will include learning the appropriate analytical and statistical techniques required to detect de novo variation within a given population and to link information regarding genetic variation to other relevant genomic data such as gene expression profiles.
(Language of instruction: English)

Learning Outcomes
  1. Describe different approaches to the design of genomics experiments and apply appropriate statistical methods to their analysis
  2. Critically analyze the outcome of a genomics-based study
  3. Describe modern genomics tools and techniques used to understand and diagnose genetic disorders
  4. Perform a GWAS analysis to detect disease-associated variants
  5. Explain the role of genomics in personalized medicine
  6. Describe the privacy and ethical issues associated with medical and personal genomics
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5106: "Medical Genomics 1" and is valid from 2016 onwards.
Note: Module offerings and details may be subject to change.

Required BI5102: Genomics Techniques 1


Semester 1 | Credits: 5

This module provides a concise introduction to the key principles and features of current next-generation sequencing (NGS) technologies. This is followed by a practical introduction to the techniques used for both study design and preparation of biological samples for sequencing studies across the various applications of NGS technology. The module will be taught using a combination of lectures, student journal club sessions and practical laboratory sessions. This module links with Genomics Data Analysis I and II, where students will learn about analysis techniques for the studies covered in this module.
(Language of instruction: English)

Learning Outcomes
  1. Understand the basic principles that underpin DNA sequencing.
  2. Describe the different types of NGS technologies that are available and detail the differences between them.
  3. Understand how different example applications for DNA sequencing, RNA sequencing and gene function studies work.
  4. Encounter a new NGS application, deconstruct it, detail how the different elements work together and explain how the application functions to achieve its goal.
  5. Prepare sequencing libraries for biological samples.
  6. Communicate effectively and use critical thinking and problem-solving skills in the context of NGS.
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module BI5102: "Genomics Techniques 1" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required MA5108: Statistical Computing with R


Semester 1 | Credits: 5

This module is designed to provide students with an understanding of the role of genetic variation in human health, and how modern high-throughput genomics techniques can be used to help understand, diagnose, and treat a variety of common and rare genetic disorders. This will include learning the appropriate analytical and statistical techniques required to detect de novo variation within a given population and to link information regarding genetic variation to other relevant genomic data such as gene expression profiles.
(Language of instruction: English)

Learning Outcomes
  1. Create and manipulate basic data objects in R
  2. Write basic functions in R
  3. Preprocess genomics data
  4. Perform exploratory analysis of genomics data
  5. Select appropriate statistical methods for the analysis of genomics datasets
  6. Use graphics engines and produce reports on genomics data analysis projects
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5108: "Statistical Computing with R" and is valid from 2016 onwards.
Note: Module offerings and details may be subject to change.

Required MA5113: Medical Genomics II


Semester 2 | Credits: 10

This module will describe contemporary models of carcinogenesis and common pathways of cancer, introduce students to core concepts in machine learning, including supervised/unsupervised learning algorithms and cross validation, and teach them to apply supervised learning methods for biomarker discovery in cancer datasets.
(Language of instruction: English)

Learning Outcomes
  1. Describe contemporary models of carcinogenesis
  2. Explain machine learning concepts, including supervised/unsupervised learning and cross validation
  3. Select appropriate machine learning techniques for cancer class discovery from genomics data
  4. Design a study to identify cancer classes and associated biomarkers
  5. Apply supervised learning methods for biomarker discovery
  6. Describe common cancer pathways
  7. Outline the steps appropriate to identify somatic and driver mutations in cancer sequence data
  8. Describe applications of tumour genome sequencing for targeted therapy
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5113: "Medical Genomics II" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required MA5112: Genomics Data Analysis II


Semester 2 | Credits: 5

This module follows on from Genomics Data Analysis I and introduces students to the theory and practice of data analysis for specific NGS-based assays including: ChIP-seq, RNA-seq, and whole-exome/whole-genome sequencing for variant discovery.
(Language of instruction: English)

Learning Outcomes
  1. Explain the use of the negative binomial model and associated statistical concepts for peak calling in ChIP-seq data.
  2. Perform a ChIP-seq analysis on genome-wide binding data for a specific transcription factor including: peak-calling, de novo motif discovery, and annotation of called peaks.
  3. Critically evaluate different metrics for quantifying read abundances in RNA-seq experiments.
  4. Carry out an RNA-seq analysis including the use of a splice-aware aligner, identification of differentially expressed transcripts, and functional/pathway enrichment analysis.
  5. Explain the GATK best practices for germline and somatic (matched tumour/normal) variant discovery and perform the analyses.
  6. Describe approaches for the integration and visualisation of data from multiple NGS-based assays.
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5112: "Genomics Data Analysis II" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required MA5105: Genomics Project


Trimester 3 | Credits: 30

In this module, the student works either on an experimental genomics project or on the analysis of genomics data, under the supervision of a genomics researcher.

Learning Outcomes
  1. Perform genomics research to a high standard
  2. Carry out genomics experiments and/or perform analysis of high throughput genomics data
  3. Present research findings in written, graphical and oral forms
Assessments
  • Research (100%)
Teachers
The above information outlines module MA5105: "Genomics Project" and is valid from 2016 onwards.
Note: Module offerings and details may be subject to change.

Required MA570: Data Analysis for genomics technologies


Semester 2 | Credits: 5


(Language of instruction: English)

Learning Outcomes
  1. Discuss key historical developments in genomics research.
  2. Access and apply core programming interfaces for bioinformatic analyses.
  3. Discover differential expression in gene transcript sequencing data.
  4. Compare new high-throughput sequencing experiments to other published results.
  5. Evaluate functional genomics experimental datasets.
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA570: "Data Analysis for genomics technologies" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Optional BI5101: Molecular biology for quantitative scientists


Semester 1 | Credits: 5

This module provides a concise introduction to key principles and mechanism in molecular cell biology that underpin genomics. It comprises an overview of cell structure and the cell cycle including mitosis and meiosis, a description of the molecular properties of DNA and chromosomes, and an introduction to the mechanisms of replication, transcription and translation including gene regulation and epigenetics. The module is delivered through introductory lectures, a self-study requirement with supporting tutorials, and weekly tests followed by a final examination.
(Language of instruction: English)

Learning Outcomes
  1. Outline the structure of animal cells and the cell cycle, including mitosis and meiosis
  2. Summarise the structure of DNA and chromosomes and how this enables encoding of genetic information
  3. Explain the mechanisms of DNA replication, gene transcription and translation
  4. Describe the biochemical basis of gene regulation and epigenetics
Assessments
  • Continuous Assessment (40%)
  • Department-based Assessment (60%)
Teachers
Reading List
  1. "Medical genetics at a glance" by Dorian J. Pritchard, Bruce R. Korf.
    ISBN: 9780470656549.
    Publisher: Chichester; John Wiley & Sons
The above information outlines module BI5101: "Molecular biology for quantitative scientists" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Optional MD206: Molecular Medicine


Semester 1 | Credits: 5

24 lectures (3 x 3 lectures from 7 lecturers) covering Signalling pathways; Molecular Diagnosis; Cell Cycle; DNA Repair; Oncogenes & Tumour Suppressors; DNA Damage Response & Cancer; Cell Biology; Cell Death; Future Therapies and, finally, Drug Discovery & Small Molecules. This course will be assessed by a 2 hr written examination of MCQs format questions
(Language of instruction: English)

Learning Outcomes
  1. Describe the role of signal tranduction mechanisms in health and disease
  2. Critically evaluate the current concepts in cancer biology and DNA damage responses
  3. Describe the mechanisms of DNA repair and programmed cell death and their relevance to cancer
  4. Discuss the principles and development of current and potential chemotherapeutic strategies for cancer
  5. Describe RNA interference and gene editing technologies and their potential as future therapeutic strategies
  6. Describe and discuss technological advances that are uncovering novel molecules and intermolecular relationships that are medically relevant
Assessments
  • Written Assessment (100%)
Teachers
Reading List
  1. "Fundamental molecular biology" by Lizabeth A. Allison
    ISBN: 9781405103794.
    Publisher: Blackwell Pub.
  2. "The biology of cancer" by by Robert A. Weinberg
    ISBN: 0815340788.
    Publisher: Garland Science
  3. "Molecular Biology of the the Cell" by n/a
    ISBN: 9780815344537.
    Publisher: Blackwell
The above information outlines module MD206: "Molecular Medicine" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Optional MA461: Probabilistic Models for Molecular Biology


Semester 2 | Credits: 5

This course covers applications of probabilistic models and related techniques in genomics and systems biology. Beginning with a review of stochastic processes, the course will consider the use of Hidden Markov models (HMMs) to predict genes and identify genomic regions with shared epigenetic characteristics; the use of continuous-time Markov processes to model molecular evolution; applications of Gibbs sampling to infer haplotypes from genotype data among other models and applications.
(Language of instruction: English)

Learning Outcomes
  1. derive key results that are applied in the course;
  2. decode sequences of symbols generated from a HMM using the Viterbi algorithm;
  3. calculate hidden state probabilities using forward/backward algorithms;
  4. align a pair of DNA or amino acid sequences using a probabilistic model;
  5. apply probabilistic models to describe sequence evolution over a phylogenetic tree;
  6. infer haplotypes from a set of genotype data by hand;
  7. describe several problems in molecular biology/systems biology and explain the application of probabilistic models to solve these problems;
  8. construct a pair-HMM for sequence alignment.
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Teachers
Reading List
  1. "Biological Sequence Analysis" by n/a
The above information outlines module MA461: "Probabilistic Models for Molecular Biology" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.

Optional ST412: Stochastic Processes


Semester 2 | Credits: 5

The goal of the course is to introduce the main ideas and methods of stochastic processes with the focus on Markov chains (processes with discrete time index and finite state space). Branching processes and Poisson process (continuous time and discrete state space) will also be included in the study.

Learning Outcomes
  1. Use probability and moment generating functions to calculate corresponding distributional properties.
  2. Derive properties of branching processes such as expectation, variance, and probability of extinction.
  3. Calculate relevant probabilities in random walks with and without barriers
  4. Use Markov property to prove various probabilistic statements about Markov chain
  5. Classify states of Markov chains and determine stationarity properties
  6. Calculate limiting and statitonary distributions
  7. Prove and calculate various properties of Poisson process
  8. Build and describe Markov chains to represent simplified real world problems, for example, such as those those used to model credit mobility
Assessments
  • Written Assessment (80%)
  • Continuous Assessment (20%)
Teachers
Reading List
  1. "Introduction to Probability, American Mathematical Society" by C. Grinstead and L. Snell
    Publisher: free online copy
  2. "Introduction to Probability Models," by Sheldon Ross
    Publisher: Academic Press
The above information outlines module ST412: "Stochastic Processes" and is valid from 2016 onwards.
Note: Module offerings and details may be subject to change.

Optional ST417: Introduction to Bayesian Modelling


Semester 2 | Credits: 5

An introductory course to Bayesian statistical modelling and analysis. Covers basic theory and methods of Bayesian model development and focuses on inference which is based on simulations (computations done in R). A prerequisite is a calculus based course in probability (at the level of ST2x3/MA235, for example). Prior experience studying statistics or regression analysis is helpful but not necessary.
(Language of instruction: English)

Learning Outcomes
  1. Determine likelihood and prior distributions as parts of a basic Bayesian model specification.
  2. Apply Bayes theorem to obtain posterior distribution of unknown random variables in the model.
  3. Derive posterior predictive distribution.
  4. Write simple R scripts implementing basic random sampling methods.
  5. Apply the basics of Markov chain theory to implement simulation algorithms for inference.
  6. Implement Gibbs sampler and Metropolis algorithm to obtain samples from posterior distributions.
  7. Compare and contrast basic Bayesian methods with classical statistics and realize advanatges and disadvantages of both.
  8. Develop simple Bayesian models for analysis of real world data sets.
Assessments
  • Written Assessment (80%)
  • Department-based Assessment (20%)
Teachers
Reading List
  1. "Introduction to Bayesian Statistics" by William Bolstad
  2. "A First Course in Bayesian Statistical Methods" by Peter Hoff
  3. "Bayesian Data Analysis" by Gelman, Carlin, Stern and Rubin
    Publisher: Chapman & Hall / CRC
  4. "Introduction to Statistical Thought" by Michael Lavine,
The above information outlines module ST417: "Introduction to Bayesian Modelling" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Optional CT5100: Data Visualisation


Semester 2 | Credits: 5

This module with teach the fundamentals of data visualization. It will cover basic design principles and the principles underlying human perception, color theory and narrative. It will focus on the use of open standards for the presentation of data on the Web such as HTML, CSS, SVG, javascript through the use of libraries such as D3.js, jQuery.js and Dimple.js.
(Language of instruction: English)

Learning Outcomes
  1. understand the basic design principles underlying human perception, color theory and narrative
  2. understand how to use different types of visualisations to illustrate concepts and data types
  3. know how to use the main standards for the presentation of data on the Web such as HTML, CSS, SVG, javascript
  4. know how to manipulate the Document Object Model programmatically
  5. know how to deploy visualisation libraries such as D3.js and dimple.js
Assessments
  • Written Assessment (40%)
  • Continuous Assessment (60%)
Teachers
Reading List
  1. "Interactive Data Visualization for the Web" by Scott Murray
    ISBN: 9781449339739.
    Publisher: O'Reilly Media
The above information outlines module CT5100: "Data Visualisation" and is valid from 2016 onwards.
Note: Module offerings and details may be subject to change.

Optional MA216: Mathematical Molecular Biology II


Semester 2 | Credits: 5

This module is intended to give students an understanding and knowledge of the application of mathematical or algorithmic methods to defined problems in molecular biology. The focus is primarily on problems involving mutation discovery and evolutionary inference.
(Language of instruction: English)

Learning Outcomes
  1. describe how genes and genomes can change between generations;
  2. apply algorithmic methods to infer unknown genotypes in a sample;
  3. understand how genome structure alters mutation discovery power;
  4. use DNA linkage patterns to assess evolutionary neutrality in a population;
  5. infer historical changes in genetic diversity for defined examples;
  6. outline fundamental concepts in molecular evolution;
  7. use population genetic methods to measure mutation at a gene;
  8. outline methods for genome-wide association studies using simple data.
Assessments
  • Written Assessment (60%)
  • Continuous Assessment (40%)
Teachers
Reading List
  1. "A primer of population genetics" by Daniel L Hartl
    Publisher: Sinauer Associates
The above information outlines module MA216: "Mathematical Molecular Biology II" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.

Optional MA324: Introduction to Bioinformatics (Honours)


Semester 2 | Credits: 5

The course will give students an appreciation of the application of computers and algorithms in molecular biology. This includes foundation knowledge of bioinformatics; the ability to perform basic bioinformatic tasks; and to discuss current bioinformatic research with respect to human health.

Learning Outcomes
  1. outline key bioinformatics principles and approaches
  2. discuss the relevance of bioinformatics to medicine
  3. obtain molecular sequence data from public repositories
  4. implement key bioinformatics algorithms by hand on toy datasets
  5. use bioinformatics software tools, including tools for sequence alignment, homology searching, phylogenetic inference and promoter analysis;
  6. describe key high throughput data generation technologies and the steps involved in data pre-processing and basic analysis of these data.
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Teachers
Reading List
  1. "Bioinformatics ; Sequence and Genome Analysis" by David W Mount
    ISBN: 9788123909981.
    Publisher: CBS Publishers & Distributors
  2. "INTRODUCTION TO BIOINFORMATICS." by Arthur M. Lesk
    ISBN: 9780195685251.
    Publisher: OUP
  3. "Bioinformatics" by [edited by] Andreas D. Baxevanis, B. F. Francis Ouellette
    ISBN: 9780471383901.
    Publisher: Wiley-Interscience
The above information outlines module MA324: "Introduction to Bioinformatics (Honours)" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.

Optional CS4423: Networks


Semester 2 | Credits: 5

An Introduction to Network Science.

Learning Outcomes
  1. Describe a network in graph theoretic terms;
  2. Apply graph traversal techniques to networks with additional attributes;
  3. Provide a formal definition of a game;
  4. Identify equilibrium strategies in a given game;
  5. Reason about specific markets when represented as networks;
  6. Reason about document networks such as the web.
Assessments
  • Written Assessment (80%)
  • Continuous Assessment (20%)
Teachers
Reading List
  1. "Networks, Crowds and Markets" by D. Easley & J. Kleinberg
    Publisher: Cambridge University Press
  2. "Networks, An Introduction" by Mark Newman
    Publisher: Oxford University Press
The above information outlines module CS4423: "Networks" and is valid from 2016 onwards.
Note: Module offerings and details may be subject to change.

Optional REM508: Graduate Course in Basic and Advanced Immunology


Semester 2 | Credits: 5

Assessments
  • Continuous Assessment (100%)
Teachers
The above information outlines module REM508: "Graduate Course in Basic and Advanced Immunology" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Optional DER5101: Linked Data


Semester 2 | Credits: 5

This module will teach fundamentals of Linked Data and related standards, including the main principles distinguishing Linked Data from standard database technology. It will focus on designing linked data applications and students will learn how to design ontologies, produce linked data-sets, generate links between data-sets and explain the overall architecture of data integration systems based on Linked Data. It presents techniques for querying and managing Linked Data that is available on today’s Web. A large part of the module is devoted to query processing in different setups. The module will focus on managing large-scale collections of Linked Data. It will present methods to publish relational data as Linked Data and efficient centralised processing. It then addresses advanced topics, such as efficient reasoning, and query optimisation for large-scale linked data-sets.
(Language of instruction: English)

Learning Outcomes
  1. explain the motivation for creating linked data standards
  2. explain how data can be modelled as a graph an how this differs from the relational data model
  3. use RDF and OWL to model graph data
  4. query a RDF data base using the Sparq query language
  5. build a linked data enabled application using best practices in Linked Data application design
Assessments
  • Written Assessment (60%)
  • Continuous Assessment (40%)
Teachers
Reading List
  1. "Linked Data Management" by Andreas Harth,Katja Hose,Ralf Schenkel
    ISBN: 9781466582408.
    Publisher: CRC Press
  2. "Linked Data" by Tom Heath,Christian Bizer
    ISBN: 9781608454303.
    Publisher: Morgan & Claypool Publishers
The above information outlines module DER5101: "Linked Data" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Why Choose This Course?

Career Opportunities

The MSc in Biomedical Genomics will provide the mix of skills required to engage in genomics analysis and research in a variety of settings. As advances in precision medicine take hold, it is anticipated that the need for genomics analysts in health care, the pharmaceutical industry and in academic research will continue to increase, generating opportunities to seek employment in each of these areas.

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Who’s Suited to This Course

Learning Outcomes

 

Work Placement

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€7,015 p.a. 2018/19

Fees: Tuition

€6,791 p.a. 2018/19

Fees: Student levy

€224 p.a. 2018/19

Fees: Non EU

€14,750 p.a. 2018/19
Further information on postgraduate funding opportunities and scholarships can be found here

What the Experts Say

Dr. Colm

Dr. Colm O’Dushlaine |   Manager, Statistical Genetics, Regeneron Genetics Center,USA

Genomics has become a transformative technology for drug discovery. This MSc programme will produce graduates with the range of skills needed to attract employers and build excellent careers in the biopharmaceutical industry.
Dr. John

Dr. John Greally |   Director,Einstein College of Medicine Center for Epigenomics

I am really excited by this programme because it provides the unique combination of molecular and analytical skills that are critical in order to take advantage of the current wave of innovation in genomics-based technologies. I can see NUI Galway establishing itself as a major centre for biomedical genomics training and research in Europe.
Dr. Terri

Dr. Terri McVeigh |   Specialist Registrar in Clinical Genetics, OLCH Crumlin

I work both as a doctor in Clinical Genetics, and also as a cancer researcher. [The genomics data analysis] module was enormously beneficial to me from both clinical and academic perspectives. The material used in the end-of-module assignment could be derived from our own work or from a dataset of our interest, meaning that we could immediately see real-life applications of our new skills and knowledge base. I would highly recommend this module, and indeed this MSc to anybody with a clinical or research interest in genomics.