Biomedical Genomics (MSc)

Program Overview

The MSc in Biomedical Genomics at the University of Galway equips graduates with core skills in genomics analysis and practical experience in applying these skills to biological samples and data. The program focuses on providing students with the knowledge and skills necessary to analyze and interpret genomic data in a medical context, preparing them for careers in genomics research, healthcare, or the pharmaceutical industry. It includes a 30-credit research project and offers a variety of optional modules to enhance students' expertise.

Program Outline

Degree Overview:

The MSc in Biomedical Genomics aims to train graduates with backgrounds in the molecular life sciences in genomics relevant to medical applications. The program focuses on providing students with core skills in genomics analysis and practical experience in applying these skills to biological samples and data.

Objectives:

• To equip graduates with the knowledge and skills necessary to analyze and interpret genomic data in a medical context.
• To provide students with practical experience in applying genomics techniques to real-world problems.
• To prepare graduates for careers in genomics research, healthcare, or the pharmaceutical industry.

Outline:

The MSc in Biomedical Genomics is a 12-month, 90-credit course consisting of 60 credits of taught modules and a 30-credit research project.

Course Schedule:

• Semester 1:
• Required Modules:
• MA5111: Genomics Data Analysis I (5 credits)
• MA5106: Medical Genomics 1 (5 credits)
• BI5102: Genomics Techniques 1 (5 credits)
• MA5117: Genomics Research Methods (5 credits)
• Optional Modules:
• MA5114: Programming for Biology (5 credits)
• MA5108: Statistical Computing with R (5 credits)
• MA5116: Introductory Probability for Genomics (5 credits)
• BI5107: Introduction to Molecular and Cellular Biology (5 credits)
• Semester 2:
• Required Modules:
• MA5107: Medical Genomics II (5 credits)
• MA5112: Genomics Data Analysis II (5 credits)
• MA5105: Genomics Project (30 credits)
• Optional Modules:
• MA461: Probabilistic Models for Molecular Biology (5 credits)
• ST412: Stochastic Processes (5 credits)
• ST417: Introduction to Bayesian Modelling (5 credits)
• CT5100: Data Visualisation (5 credits)
• MA216: Mathematical Molecular Biology II (5 credits)
• MA324: Introduction to Bioinformatics (Honours) (5 credits)
• CS4423: Networks (5 credits)
• REM508: Graduate Course in Basic and Advanced Immunology (5 credits)
• MA5118: Advanced Chemoinformatics (5 credits)

Module Descriptions:

• MA5111: Genomics Data Analysis I: Introduces students to the Linux environment, HPC infrastructure, and reproducible research using R/Bioconductor/RStudio and Rmarkdown.
Covers NGS technologies, data analysis techniques, and read QC and alignment algorithms.
• MA5106: Medical Genomics 1: Covers the role of genetic variation in human health, high-throughput genomics techniques for identifying disease-associated variants, and computational and statistical techniques for GWAS analysis.
Also covers pharmacogenomics and its role in precision medicine.
• BI5102: Genomics Techniques 1: Provides an introduction to next-generation sequencing (NGS) technologies, study design, and preparation of biological samples for sequencing studies.
Includes lectures, journal club sessions, and practical laboratory sessions.
• MA5117: Genomics Research Methods: Equips students with the skills necessary to conduct modern genomics research, communicate research findings, and address ethical, legal, and social issues related to genomic data.
• MA5107: Medical Genomics II: Covers cancer genomics, including carcinogenesis, somatic variant calling, driver and passenger mutations, machine learning methods for cancer subclass classification and biomarker discovery, and the use of genomics for targeted therapy.
• MA5105: Genomics Project: Students work on an experimental genomics project or the analysis of genomics data under the supervision of a genomics researcher.
• MA5114: Programming for Biology: Provides postgraduate students with foundation and applied programming skills using the Python ecosystem.
• MA5108: Statistical Computing with R: Trains students in the use of applied statistical techniques in genomics, reinforcing the use of R in genomics contexts.
• MA5116: Introductory Probability for Genomics: Provides students with a grounding in probability theory and its application to genomics data.
• BI5107: Introduction to Molecular and Cellular Biology: Provides a concise introduction to key principles and mechanisms in molecular cell biology.
• MA461: Probabilistic Models for Molecular Biology: Covers applications of probabilistic models and related techniques in genomics and systems biology.
• ST412: Stochastic Processes: Introduces the main ideas and methods of stochastic processes, focusing on Markov chains.
• ST417: Introduction to Bayesian Modelling: Introduces Bayesian statistical modelling and analysis, covering basic theory and methods of Bayesian model development.
• MA216: Mathematical Molecular Biology II: Provides students with an understanding and knowledge of the application of mathematical or algorithmic methods to defined problems in molecular biology.
• MA324: Introduction to Bioinformatics (Honours): Provides students with an appreciation of the application of computers and algorithms in molecular biology.
• CS4423: Networks: Introduces network science, covering graph theoretic terms, graph traversal techniques, centrality measures, random graph generators, and network analysis applications.
• REM508: Graduate Course in Basic and Advanced Immunology: Focuses on basic principles of the immune system and their relevance to human health and disease.
• MA5118: Advanced Chemoinformatics: Provides students with the theoretical foundations and practical experience necessary to perform quantitative structure–activity relationship (QSAR) modelling.

Assessment:

Students are formally assessed through a variety of both continuous assessment and end-of-semester written examinations.

Continuous Assessment:

• Written assignments
• Programming exercises
• Genomic analyses
• Group and individual presentations

Research Project Assessment:

• Examination of a written thesis
• Oral presentations
• Participation in a research seminar series

Teaching:

• The program is taught by a team of experienced faculty members with expertise in genomics, bioinformatics, and related fields.
• Teaching methods include lectures, seminars, workshops, practical laboratory sessions, and individual and group projects.
• The program emphasizes a hands-on approach to learning, providing students with the opportunity to develop practical skills in genomics analysis.

Careers:

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 healthcare, the pharmaceutical industry, and academic research will continue to increase, generating opportunities to seek employment in each of these areas.

Other:

• The program is designed for students with a strong background in the molecular life sciences.
• Students are encouraged to apply for postgraduate scholarships.
• The program is offered by the University of Galway's College of Science and Engineering.

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