Antimicrobial Databases and Genotype Prediction: Data Sharing and Analysis
2 February–8 September 2026
FutureLearn platform, online
Predict antimicrobial resistance, master data-sharing, and enhance your expertise in AMR genomics using cutting-edge databases
Overview
Duration: 3 weeks, 5 hours per week
Free Certificate of Achievement available on satisfactory completion
Start Date: The course is available ‘on demand’.
There will be a live facilitation period from 2 to 22 March 2026. During this time, the educators leading the course will actively join the discussions in the comments.
Why join this course?
On this course, you’ll learn how to effectively use antimicrobial resistance (AMR) genotype-phenotype databases to advance your research, clinical practice, and public health efforts. You’ll begin by understanding how AMR data is created and how these insights can enhance your work. This foundational knowledge will empower you to interpret AMR data with confidence and clarity.
After grasping data creation, you’ll discover how to effectively detect AMR in your own data using various tools and algorithms. You’ll learn to navigate AMR databases, understand their strengths and limitations, and harmonise outputs from different sources.
Next, you’ll learn how to share AMR data and understand how to do it in a FAIR (Findable, Accessible, Interoperable, Reusable) manner. At this point, you’ll also discuss the principles of open access data and explore databases for storing public data, as well as delving into future trends, including AI and machine learning, and how they are furthering AMR research and application.
Who is this course for?
This course is designed for early career researchers, healthcare professionals at any stage, and public health experts who are familiar with AMR but have limited experience with genotypic data.
No advanced prior knowledge is required, just a basic understanding of AMR concepts and a desire to enhance your expertise. Links to refresher materials are provided.
Learning outcomes
What will you achieve?
By the end of the course, you‘ll be able to:
- Explain the genotypic basis of AMR
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Contrast and critique the various ways to predict AMR from linked genotype/phenotype data
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Discuss the various AMR databases and their related pros and cons
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Explain how different AMR prediction tools can be operated and their outputs interpreted and implemented, understanding the current pitfalls of those tools
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Describe the process required to create and share FAIR and open AMR data
Programme and start dates
Course start dates
The course is available ‘on demand’.
What topics will you cover?
Week 1 What is AMR data and how is it created?
- Genetic basis for AMR
- Phenotypic data approaches
- Methods for linking genotypic with phenotypic evidence
Week 2 How do you detect AMR in your own data?
- AMR Databases
- Algorithms/Tools for detecting AMR
- Harmonisation of AMR outputs/databases
Week 3 What do we do with the data?
- Further analyses of AMR
- Why sharing is important?
- Principles of FAIR and open access data
- Databases for storing public data
- Where do we go next – AI/ML
Educators
Educators, Developers and Contributors
Conor Meehan is an Associate Professor at Nottingham Trent University (UK). His research focuses on the evolution, epidemiology, and genomics of pathogens, including Mycobacterium tuberculosis and other organisms of interest.
Jane Hawkey is a Research Fellow at Monash University’s Infectious Disease Genomics group, within the Department of Infectious Diseases at Alfred Health and the Central Clinical School. Her work focuses on developing the understanding of AMR in the hospital setting using WGS to study transmission in different bacterial species.
Kristy Horan is the Lead Bioinformatician for the AusTrakka platform. She has a strong interest in translational antimicrobial resistance genomics and in developing tools that deliver actionable results for public health and clinical settings.
Education Developer
Dusanka Nikolic – Wellcome Connecting Science, UK
Coordinators
Katherine Kaldeli – Wellcome Connecting Science, UK
Zoey Willard – Wellcome Connecting Science, UK
Contributors
Silvia Argimón, Senior Technical Advisor, International Pathogen Surveillance Network, WHO Hub for Pandemic and Epidemic Intelligence, World Health Organization
Rolf Kaas, Senior Scientist, Technical University of Denmark, Denmark
Margaret Lubwama, Senior Lecturer, Department of Medical Microbiology, Makerere University College of Health Sciences, Uganda
Finlay Maguire, Assistant Professor, Dalhousie University, Canada
Jake Lacey, Bioinformatician, Microbiological Diagnostic Unit Public Health Laboratory, Melbourne, Australia
Peter van Heusden, Researcher, South African National Bioinformatics Institute, University of the Western Cape, South Africa
Testimonials
The course “Antimicrobial Databases and Genotype Prediction: Data Sharing and Analysis” provides valuable insights into leveraging data for understanding antimicrobial resistance. Its focus on database usage and genotype prediction is crucial for advancing research and clinical applications in microbiology.
What's included
The Wellcome Connecting Science Learning and Training team are offering everyone who joins this course a free digital upgrade, so that you can experience the full benefits of studying online for free. This means that you get:
- Unlimited access to this course
- Includes any articles, videos, peer reviews and quizzes
- Tests to validate your learning
- A PDF Certificate of Achievement to prove your success when you’re eligible
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