Wellcome Connecting Science Course Run Website
Course Time Table 2025
Course Informatics Guide
Multiplex assays of variant effects (MAVEs) are a series of high-throughput experimental methods used to interrogate the phenotypic or functional effects of thousands of genetic variants in parallel. They have rapidly increased our ability to assess variants of uncertain significance (VUS) where rare or unknown variants may be responsible for pathogenic disease-associated effects. Their recent adoption into clinical diagnostics provides additional evidence for variant interpretation and translation.
This week-long residential course offers participants an opportunity to engage with an international team of MAVE experts, and gain insight into the generation, application, analysis, and interpretation and evaluation of MAVE data. Participants will learn how these assays are performed, how to access available tools and resources, and how utilise various analysis pipelines to determine and interpret variant effects according to the American College of Medical Genetics and Genomics (ACMG) framework.
What will this course cover?
This new addition to our programme will be delivered as a combination of seminars and discussions, interactive tutorials, and hands-on laboratory demonstrations and computer practical sessions which cover:
- Introduction to MAVEs and their applications
- How data is generated and the methods of experimentation
- Open Source tools, databases, and resources
- Quality control and analysis of MAVE data
- Evaluation and assessment of suitability of MAVE outputs
- Translation and interpretation of MAVE data in clinical settings
The course content will be delivered in English.
This course is open to postdoctoral scientists, advanced PhD students, clinicians or clinical scientists, interested in genetic variant interpretation and actively engaged in or soon to commence research or utilise MAVE and MAVE datasets in their work from anywhere in the world.
After completing this course, you will be able to:
- Discuss the principles behind different MAVE assay technologies and their applications.
- Access and work with publicly available MAVE datasets and resources utilising open-source tools and reproducible, well-documented analysis pipelines
- Implement computational workflows for processing and quality control of MAVE data
- Apply appropriate statistical methods to interpret MAVE data
- Analyse real-world MAVE datasets to gain biological insights into a disease or population of interest by integrating them with other data drawn from population genomics or disease association
- Evaluate the potential of MAVE to enable discoveries in disease gene studies and therapeutic development
- Discuss the challenges and opportunities in leveraging MAVE for more equitable global health applications
- Critically evaluate the strengths and limitations of different MAVE approaches
The programme will include lectures, seminars, demonstrations, and hands-on analysis sessions on the following topics:
- Introduction to MAVE
- Overview and history of MAVE and how and where it is being currently applied
- Applications of MAVE
- From research to medicine to industry – how has MAVE been incorporated into - what we do
- Data generation and experimental design
- Saturation mutagenesis
- Massively parallel reporter assays
- Different phenotypic readout
- MAVE resources and tools
- How to access relevant resources and utilise open source tools
- Analysis
- MAVE data analysis pipelines
- QC and data evaluation
- Interpretation and clinical translation
- ACMG standards and guidelines for variant interpretation
- Real-world scenarios and applications of MAVE
Participants will also complete group projects using existing datasets to consolidate their learning.
Course Instructors
- Dave Adams, Wellcome Sanger Institute, UK
- Daniel Jaramillo Calle, Wellcome Sanger Institute, UK
- Elizabeth Radford, Cambridge University Hospitals NHS, UK
- Alan Rubin, University of Melbourne, Australia
- Irene Gallego Romero, St Vincent's Institute for Medical Research, Australia
- Clare Turnbull, The Institute of Cancer Research, UK
- Helen Firth, Newnham College, University of Cambridge, UK
- Joseph Marsh, University of Edinburgh, Scotland, UK
- Matthew Coelho, Wellcome Sanger Institute, UK
- Adam Hunter, Wellcome Sanger Institute, UK
- Sofia Obelenski-Koenig, Wellcome Sanger Institute, UK
- Rebeca Olvera-León, Wellcome Sanger Institute, UK
- Greg Findlay, The Francis Crick Institute, UK
- Julia Foreman, EMBL EBI, UK
- Sophie Allen, The Institute of Cancer Research, UK
- Miranda Durkie, NHS North East Yorkshire Genomic Laboratory Hub, UK
Wellcome Connecting Science Team
- Cassandra Soo, Laboratory Courses Manager
- Aaron Dean, Laboratory Technical Officer
- Christopher Adamson, Laboratory Operations Officer
- Martin Aslett, Informatics Manager
- Vaishnavi Vikas Gangadhar, Informatics Technical Officer
- Karon Chapell, Events Organiser
The course data are free to reuse and adapt with appropriate attribution. All course data in these repositories are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Each course landing page is assigned a DOI via Zenodo, providing a stable and citable reference. These DOIs can be found on the respective course landing pages and can be included in CVs or research publications, offering a professional record of the course contributions.
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