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. 

Scholarships available
Find out about our Postgraduate Scholarships here.

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You may also be interested in one of our other Mathematics, Bioinformatics and Computational Genomics postgraduate programmes.

 

Applications and Selections

Applications are made online via the University of Galway Postgraduate Applications System. Selection is based on the candidate's academic record at an undergraduate level and their aptitude for the course.

Who Teaches this Course

Pilib Ó Broin, PhD

researcher
Dr Derek Morris
BSc, Ph.D
Senior Lecturer
Biochemistry
Room 106
Arts/Science Building
South Campus
View Profile

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 First or strong Second Class Honours degree in a cognate discipline. Qualifying degrees include, but are not limited to: biochemistry, genetics, biomedical science, and biotechnology.

Additional Requirements

Recognition of Prior Learning (RPL)

Duration

1 year, full-time

Next start date

September 2024

A Level Grades ()

Average intake

15

QQI/FET FETAC Entry Routes

Closing Date

Please refer to the review/closing date web-page.

NFQ level

Mode of study

ECTS weighting

90

Award

CAO

Course code

MSC-BMG

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 2 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 2, 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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Curriculum Information

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.
Module
An examinable portion of a subject or course, for which you attend lectures and/or tutorials and carry out assignments. E.g. Algebra and Calculus could be modules within the subject Mathematics. Each module has a unique module code eg. MA140.
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
  • Continuous Assessment (100%)
Teachers
The above information outlines module MA5111: "Genomics Data Analysis I" and is valid from 2023 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 high-throughput genomics techniques are used to identify disease-associated variants in both common and rare genetic disorders - this will include learning the computational and statistical techniques required to perform a GWAS analysis. It will also cover pharmacogenomics and describe its key role in the emerging field of precision medicine.
(Language of instruction: English)

Learning Outcomes
  1. Describe different approaches to the design of genomics-based studies and critically evaluate their outcomes.
  2. Perform QC and association testing on GWAS data in order to detect disease-associated variants.
  3. Carry our SNP imputation and check for population structure in a GWAS study.
  4. Use functional genomic data to annotate and prioritize disease-associated variants.
  5. Explain the role of pharmacogenomics and personal genetics in precision medicine.
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5106: "Medical Genomics 1" and is valid from 2023 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 2020 onwards.
Note: Module offerings and details may be subject to change.

Required MA5117: Genomics Research Methods


Semester 2 | Credits: 5

This module will provide students with the skills necessary to carry out modern genomics research in a critical, rigorous manner, and to communicate research findings at a standard suitable for conference presentation or publication - the latter will include the use of Latex to typeset scientific manuscripts / documents. Students will also be exposed not only to ethical, legal, and social issues specifically related to the handling of genomic data, but also more generally, to issues pertaining to academic and scholarly integrity.
(Language of instruction: English)

Learning Outcomes
  1. Critically evaluate a piece of existing literature, recognising limitations and sources of potential bias.
  2. Synthesise knowledge from multiple different sources, to provide a concise, integrated overview of the current state-of-the art in a particular area of genomics.
  3. Describe ethical, social, legal, and privacy issues related to the generation and handling of individual and population-based genomic data.
  4. Present research findings, both orally and in written form, in a clear, concise, and engaging manner.
  5. Create a Latex document suitable for submission to a high-quality peer-reviewed academic journal.
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5117: "Genomics Research Methods" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Required MA5107: Medical Genomics II


Semester 2 | Credits: 5

This module is designed to provide students with an understanding of cancer genomics: how cancers arise, somatic variant calling, distinguishing driver from passenger mutations, supervised machine learning methods for cancer subclass classifification and biomarker discovery, and the use of genomics for targeted therapy.
(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 MA5107: "Medical Genomics II" and is valid from 2023 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 poisson 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 conducting genomic analysis at scale
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5112: "Genomics Data Analysis II" and is valid from 2023 onwards.
Note: Module offerings and details may be subject to change.

Required MA5105: Genomics Project


15 months long | 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 2022 onwards.
Note: Module offerings and details may be subject to change.

Optional 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.

Optional MA5108: Statistical Computing with R


Semester 1 | Credits: 5

This is a self-contained module designed to train the student into the use of applied statistical techniques in genomics. It overlaps with content in MA5111 (Genomics Data Analysis 1) reinforcing the use of R in genomics contexts. It consists of three parts (i) hypothesis testing (ii) statistical modeling and (iii) fundamentals in statistical computing. Extensive use is made of R in demonstrating and illustrating statistical concepts, and all examples are genomics related.
(Language of instruction: English)

Learning Outcomes
  1. Implement statistical solutions using the R statistics software ecosystem
  2. Understand the statistical properties of data & how to implement hypothesis testing
  3. Understand the sensitivity, specificity and power of a test, and how to determine these parameters
  4. Implement statistical models constructed from genomics datasets in R
  5. Understand and deploy linear and general linear models in R, including logistic regression and survival analysis
  6. Use R Markdown to prepare professional quality statistical reports, including appropriate plots/graphics
Assessments
  • Continuous Assessment (100%)
Teachers
The above information outlines module MA5108: "Statistical Computing with R" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional MA5116: Introductory Probability for Genomics


Semester 1 | Credits: 5

This module is designed to provide students with a grounding in probability theory, sufficient to enable them to learn about the application of probability models to genomics data. The module will apply probability to commonly encountered problems in genomics and provide students with experience of solving biological and biology-inspired problems involving probability.
(Language of instruction: English)

Learning Outcomes
  1. Demonstrate an understanding of concepts of probability, including set-theoretic description of probability
  2. Derive rules of probability from probability axioms
  3. Demonstrate an understanding of conditional probability, independence and Bayes' rule
  4. Use rules of probability for problem-solving including solving non-trivial problems in genomics
  5. Explain the concept of a random variable and describe common statistical models in terms of functions of random variables
  6. Use R to obtain maximum likelihood values for the parameters of probability models
  7. Select appropriate methods to compare the fit of alternative probability models
  8. Apply profile likelihood to obtain confidence intervals for maximum likelihood parameter estimates
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5116: "Introductory Probability for Genomics" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional BI5107: Introduction to Molecular and Cellular Biology


Semester 1 | Credits: 5

This module provides a concise introduction to key principles and mechanisms in molecular cell biology. It comprises an overview of cell structure and the cell cycle including mitosis and meiosis, a description of the molecular properties of the major classes of biomolecules, an overview of the principles of genetics and evolution, and an introduction to the mechanisms of replication, transcription and translation including gene regulation and epigenetics.
(Language of instruction: English)

Learning Outcomes
  1. Outline the structure of animal cells and the cell cycle, including mitosis and meiosis
  2. Describe how the properties of biological macromolecules contribute to cell function
  3. Summarise the structure of DNA and chromosomes and how this enables genetics and evolution
  4. Explain the mechanisms of DNA replication, gene transcription and translation
  5. Describe the biochemical basis of gene regulation, epigenetics and DNA technologies
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Teachers
Reading List
  1. "Campbell Biology, Global Edition" by Lisa A. Urry,Michael L. Cain,Steven A. Wasserman,Peter V. Minorsky,Jane B. Reece,Neil A. Campbell
    ISBN: 9781292170435.
    Publisher: Pearson
The above information outlines module BI5107: "Introduction to Molecular and Cellular Biology" and is valid from 2020 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 1 | 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
  • Department-based Assessment (100%)
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 2021 onwards.
Note: Module offerings and details may be subject to change.

Optional CT5100: Data Visualisation


Semester 2 | Credits: 5

Data Visualisation is concerned with techniques and technologies for the visual representation of data and the results and evaluation of data analytic processes. This module takes a practical approach to introducing learners to the strengths and weaknesses of human perception, and the use of best practices to represent complex and large data stories using visual primitives. The module demonstrates the role of visualisation in exploratory data analysis and its fundamental role in explaining data analytics outcomes. The practical work in this module is done using the R programming language - and learners are expected to have completed an introductory module in R.
(Language of instruction: English)

Learning Outcomes
  1. describe the basic design principles underlying human perception, color theory and narrative
  2. select different types of visualisations for use in various exploratory and explanatory scenarios
  3. deploy the grammar of graphics to build interpretable static and interactive visualisations using R's visualisation packages and other processing libraries from the tidyverse set of R packages
  4. deploy visualisation techniques during exploratory analysis work flow
  5. build custom visualisations that represent specific data-driven narratives
  6. outline at least one research area in Data Visualisation based on your reading of at least two research papers
Assessments
  • Written Assessment (65%)
  • Continuous Assessment (35%)
Teachers
Reading List
  1. "R Graphics Cookbook" by Winston Chang
    Publisher: O'Reilly
  2. "ggplot2" by Hadley Wickham
    ISBN: 9783319242750.
    Publisher: Springer
  3. "Information Visualization" by Colin Ware
    ISBN: 9780123814647.
    Publisher: Elsevier
  4. "Now You See it" by Stephen Few
    ISBN: 9780970601988.
    Publisher: Analytical Press
  5. "The Visual Display of Quantitative Information" by Edward R. Tufte
    ISBN: 9781930824133.
    Publisher: Graphics Press
The above information outlines module CT5100: "Data Visualisation" and is valid from 2023 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. Define and apply various centrality measures on networks;
  4. Describe various random graph generators and derive properties of the resulting networks;
  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. "Complex Networks" by Vito Latora,Vincenzo Nicosia,Giovanni Russo
    ISBN: 9781107103184.
    Publisher: Cambridge University Press
  2. "Networks, An Introduction" by Mark Newman
    Publisher: Oxford University Press
  3. "Networks, Crowds and Markets" by D. Easley & J. Kleinberg
    Publisher: Cambridge University Press
The above information outlines module CS4423: "Networks" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Optional REM508: Graduate Course in Basic and Advanced Immunology


Semester 2 | Credits: 5

A 1-semester, Master's level module focussed on basic principles of the immune system and their relevance to human health and disease. The module may also be suitable for PhD students with limited prior immunology teaching who are seeking a basic grounding in immunology that may be relevant to their thesis project.
(Language of instruction: English)

Learning Outcomes
  1. Identify the primary cellular and non-cellular components of the innate and adaptive arms of the immune system and discuss their development, anatomical locations and functional responses.
  2. Understand how the innate and adaptive arms of the immune systems combine to protect against invasive microbial pathogens and other health threats.
  3. Identify cellular and non-cellular mechanisms of immune regulation and their role in health and disease.
  4. Discuss the immunological basis for autoimmunity, vaccination, transplant rejection, cancer and stem cell therapies.
  5. Be capable of writing a scientifically informative brief review of a topic related to human health that requires detailed understanding of one or more basic aspects of the immune system.
Assessments
  • Continuous Assessment (80%)
  • Oral, Audio Visual or Practical Assessment (20%)
Teachers
Reading List
  1. "Janeway's Immunobiology" by Kenneth Murphy,Casey Weaver
    ISBN: 9780815345053.
    Publisher: Garland Science
  2. "Lippincott Illustrated Reviews: Immunology" by Doa,Thao Doan, MD,Roger Melvold, PhD,Dr Susan Viselli, PH D,Carl Waltenbaugh, PhD
    ISBN: 9781451109375.
    Publisher: Lippincott Williams&Wilkins
  3. "Immunology" by Peter M. Lydyard,Alex Whelan,Michael W. Fanger
    ISBN: 9780415607537.
    Publisher: Taylor & Francis
  4. "Case Studies in Immunology" by Raif S. Geha,Luigi Notarangelo
    ISBN: 9780815345121.
    Publisher: Garland Science Taylor & Francis Group LLC
The above information outlines module REM508: "Graduate Course in Basic and Advanced Immunology" and is valid from 2020 onwards.
Note: Module offerings and details may be subject to change.

Optional MA5118: Advanced Chemoinformatics


Semester 2 | Credits: 5

This module will provide students with both the theoretical foundations and practical experience necessary to perform quantitative structure–activity relationship (QSAR) modelling. Drawing on and integrating learning from related modules in Programming (MA5114) and Statistical Computing (MA5108), students will develop a rich toolkit which will allow them connect experimental measures with a set of chemical descriptors in order to predict biological activity (toxicity) for a given compound.
(Language of instruction: English)

Learning Outcomes
  1. Describe the different databases and file formats associated with chemical compounds and their molecular descriptors.
  2. Demonstrate the use of software packages/tools (e.g. webchem, rcdk, Babel, ChemDraw, PaDel) to mine existing databases, visualise compounds and generate molecular descriptors.
  3. Distinguish between regression and classification models and describe machine learning algorithms for QSAR including: support vector machine (SVM), random forest (RF), and naive Bayes (NB) approaches.
  4. Perform a QSAR analysis, including appropriate data filtering/curation, feature extraction, and model checking and validation.
Assessments
  • Department-based Assessment (100%)
Teachers
The above information outlines module MA5118: "Advanced Chemoinformatics" and is valid from 2019 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

Transferable Skills Employers Value

Work Placement

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€8,640 p.a. (including levy) 2024/25

Fees: Tuition

€8,500 p.a. 2024/25

Fees: Student levy

€140 p.a. 2024/25

Fees: Non EU

€27,000 p.a. (€27,140 including levy) 2024/25


Postgraduate students in receipt of a SUSI grant—please note an F4 grant is where SUSI will pay €4,000 towards your tuition (2024/25).  You will be liable for the remainder of the total fee.  A P1 grant is where SUSI will pay tuition up to a maximum of €6,270. SUSI will not cover the student levy of €140.

Postgraduate fee breakdown = Tuition (EU or NON EU) + Student levy as outlined above.

Note to non-EU students: learn about the 24-month Stayback Visa here

Find out More

Cathal Seoighe
T: +353 91 492 343
E: cathal.seoighe@universityofgalway.ie

Haixuan Yang
T: +353 89 949 2030
E: haixuan.yang@universityofgalway.ie


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.
Eamon

Eamon McAndrew |   MSc Biomedical Genomics Graduate

My undergraduate degree was in industrial biochemistry, so my background was laboratory-based with a focus on the development of and manufacture of biopharmaceuticals. I became interested in this MSc after learning about the potential of precision medicine to revolutionise many areas of healthcare. The short course format at the beginning of the MSc is a very effective style of teaching - it brings everyone up to a standard level of knowledge in the underlying biology and the ability to read and write code, while also establishing an intuitive understanding of the mathematics underpinning the methods used in genomics. With limited knowledge of computer science and mathematics, I did find it challenging, but the small course size was invaluable as help was always on hand from my peers or my tutors, allowing for instant feedback and discussion. The skills developed are not only applicable to the field of genomics but to any profession with data science at its core. As the demand for skills taught in this course are projected to grow, I would recommend this course to individuals interested in a career transition and to those wishing to develop skills to maximise future employability.

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