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Interactive training for functional analysis and interpretation of disease data using computational modelling tools
Antimicrobial resistance (AMR) is critical to monitor as it threatens modern medical and public health systems. The Clinically Oriented antimicrobial Resistance Network (ACORN) project is developing an effective AMR surveillance system across nine African and Asian countries. This page is a landing for training developed in collaboration with Wellcome Connecting Science.
This programme aims to train ACORN team members across nine centres with core bioinformatics skills which can be utilised to analyse and identify resistance from pathogen genome data.
At the end of this course, participants will be able to:
- Apply appropriate tools to analyse WGS data of bacterial genomes
- Identify sequencing data formats
- Use pathogen genomic analysis web-tools to process sequence data
- Execute the basic processes of genome assembly for pathogen species
- Identify and interrogate antibiotic resistance genes and mutations
- Implement data workflows and bioinformatic analysis of genomic sequence data, including risk and resistance prediction for bacterial epidemiology and pandemic response
- Conduct phylogenetic analysis in the context of outbreak and other epidemiological investigations
- Compare phylogenetic trees for different species and outbreak scenarios Add text here
This virtual bioinformatics course will offer a series of lectures and practical sessions including the following topics:
- Pathogen genome assembly
- Annotation of pathogen genomes
- Phylogenetics in clinical contexts
Course Runs
| Course Date | Course Title | Location | Citation DOI |
|---|---|---|---|
| 21 May - 6 June 24 | WCS ACORN - Bioinformatics for Antimicrobial Resistance | Virtual |  |
| 15–20 September 2024 | Integrated AMR: From Genomic Analysis to Clinical Application - Africa & Asia | KEMRI-Wellcome Trust, Kilifi, Kenya | |
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).
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