GenEpi-BioTrain - Virtual training 22 - Genomic Epidemiology and Outbreak Investigation of Brucellosis in a One Health Context

Topic outline

• Welcome to the virtual training on Genomic Epidemiology and Outbreak Investigation of Brucellosis in a One Health Context

  Welcome to the virtual training on Genomic Epidemiology and Outbreak Investigation of Brucellosis in a One Health Context

  Course description
  

  This two-half-day training course introduces participants to genomic epidemiology and phylogenetic analysis of Brucella spp. using a realistic One Health scenario, and it explores the application of combined genomic and epidemiological data for outbreak investigations. Through guided analysis of Brucella spp. genomes, participants will evaluate the genomic diversity within the genus and gain practical skills for interpreting results from taxonomic analysis and typing, reading phylogenetic trees and assessing outbreak clusters. Importantly, participants will integrate metadata and epidemiological data with the genomic results, to infer outbreak sources and transmission dynamics.

  The course includes a simulation exercise to reinforce practical understanding.

  
  

  Outline

  Target Audience

  Microbiologists, clinicians and epidemiologists with basic bioinformatics skills

  Pre-requisites: Equipment and Software requirements

  To participate effectively in this course, ensure you have access to the necessary materials and that your machine meets the following requirements and has the appropriate tools installed:

  • A laptop with internet access.
  • Sample datasets (provided via ScienceData.dk: https://sciencedata.dk/shared/e9cba091ad1d946970bfdd67e8491b2a).
  • For command-line usage: macOS or Linux recommended; Windows users should use WSL (Windows Subsystem for Linux) or Docker.
  • Mamba or Conda for managing software environments and packages (installed through Miniforge for for example: https://github.com/conda-forge/miniforge)

  Participants may choose to use web-based (basic) tools or command-line (advanced) tools depending on their comfort and experience:

  • Pre-install the command-line tools before Session 1 for a smooth workflow.
  • Web tools require no installation (access via provided links).

  
  

  Curriculum and session details

  The training is two half-days (Monday 19 January and Monday 26 January). Each session lasts 3.5 hours, from 9:00 to 12:30.

  The practical exercises assigned in Session 1 should be completed before the next session. The discussion of the simulated exercise will take place during Session 2.

  
  

  Suggested Readings

  • Centre for Disease Prevention and Control (CDC). About Brucellosis. CDC, 2025. Available at: https://www.cdc.gov/brucellosis/about/index.html
  • Centre for Disease Prevention and Control (CDC). Clinical Overview of Brucellosis. CDC, 2025. Available at: https://www.cdc.gov/brucellosis/hcp/clinical-overview/index.html
  • World Health Organization (WHO). Brucellosis. WHO, 2025. Available at: https://www.who.int/news-room/fact-sheets/detail/brucellosis
  • Whatmore AM, Perrett LL, MacMillan AP. Characterisation of the genetic diversity of Brucella by multilocus sequencing. BMC Microbiol. 2007;7:34. doi: 10.1186/1471-2180-7-34.
  • Whatmore AM, Koylass MS, Muchowski J, et al. Extended Multilocus Sequence Analysis to Describe the Global Population Structure of the Genus Brucella: Phylogeography and Relationship to Biovars. Front Microbiol. 2016;7:2049. doi: 10.3389/fmicb.2016.02049.
  • Whatmore AM, Foster JT. Emerging diversity and ongoing expansion of the genus Brucella. Infect Genet Evol. 2021;92:104865. doi: 10.1016/j.meegid.2021.104865.

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  Aim and Objectives

  Aim and Objectives
  This course explores the application of combined genomic and epidemiological data for investigation of Brucellosis outbreaks in a One Health context.

  Upon completion of this workshop, participants will be able to:

  1. Understand the biological diversity within the Brucella genus and its significance within the One Health context.
  2. Perform taxonomic analysis and typing using simple bioinformatics tools.
  3. Explain the principles of SNP-based phylogenetic analysis and its role in outbreak investigation.
  4. Analyse and interpret SNP matrices and maximum parsimony trees.
  5. Perform SNP-based phylogenetic analysis using simple bioinformatics tools.
  6. Understand the principles behind epidemiological investigations.
  7. Create epidemiological curves and generate a hypothesis.
  8. Integrate genomic and epidemiological data  for hypothesis generation including outbreak sources and transmission.
  9. Evaluate the limitations and strengths of genomic approaches in zoonotic disease surveillance.
  10. Understand the value of the One Health approach for real-world applications.
• Software Requirements

  Software Requirements

  Equipment and Tools

  To participate effectively in this course, ensure you have access to the necessary materials and that your machine meets the following requirements and has the appropriate tools installed.

  Equipment:

  • A laptop with internet access.
  • Sample datasets (provided latter via Sciencedata.dk). 
  • For command-line usage: macOS or Linux recommended; Windows users should use WSL (Windows Subsystem for Linux) or Docker. 
  • Mamba or Conda for managing software environments and packages (installed for example through Miniforge: https://github.com/conda-forge/miniforge).

  Tool Setup:

  Participants may choose to use web-based (basic) tools or command-line (advanced) tools depending on their comfort and experience.

  • Pre-install the command-line tools before Session 1 for a smooth workflow.
  • Web tools require no installation — access via provided links.

  1. Snippy (Command-line – Advanced)

  • Objective: Call SNPs from assembled genomes and generate a core SNP alignment.
  • URL with detailed instructions: https://github.com/tseemann/snippy.
  • Installation (using Bioconda):

  conda config --add channels conda-forge conda config --add channels bioconda conda config --set channel_priority strict # Installation using mamba mamba install snippy   # Verify installation snippy --version snippy --check

  2. CSI Phylogeny (Web – Basic)

  • Objective: Upload FASTA files and a reference genome to call SNPs, filter them, and automatically infer a phylogenetic tree.
  • Access: Use the online interface (no installation needed).
  • URL: https://cge.food.dtu.dk/services/CSIPhylogeny/.

  3. MINTyper (Web + CLI – Basic)

  • Objective: Automatically select a reference, call and mask SNPs, and build a phylogenetic tree (Neighbor-Joining-NJ or Maximum Likelihood-ML).
  • Access:
  • Web interface: https://cge.food.dtu.dk/services/MINTyper/.
  • CLI (optional install): pip install mintyper.

  4. IQ‑TREE (Command-line – Advanced)

  • Objective: Perform maximum-likelihood phylogenetic inference from SNP alignments.
  • URL with detailed instructions: https://github.com/iqtree/iqtree3.
  • Installation (Bioconda):

  # Installation using mamba mamba install iqtree # Verify installation iqtree --version iqtree --help

  5. iTOL (Visualization – Basic)

  • Objective: Visualize and annotate phylogenetic trees (Neighbor-Joining-NJ or Maximum Likelihood-ML).
  • Access:
  • iTOL: Web-based (sign-up link https://itol.embl.de/).
  • MEGA: Downloadable desktop GUI (Windows, macOS, Linux — installation file provided in https://www.megasoftware.net/).

  6. KmerFinder (Command-line – Advanced)

  • Objective: Perform species identification from WGS data using k-mer matching. 
  • URL with detailed instructions: https://bitbucket.org/genomicepidemiology/kmerfinder/src/master/. 
  • Installation (Bioconda):

  # Installation using mamba mamba install kmerfinder   # Verify installation kmerfinder --version kmerfinder --help

  7. KmerFinder (Web – Basic)

  • Objective: Species identification using an online k-mer matching interface. 
  • URL: https://cge.food.dtu.dk/services/KmerFinder/ 

  8. rMLST (Web – Basic)

  • Objective: Ribosomal MLST typing using WGS data. 
  • URL: https://pubmlst.org/species-id. 

  9. PubMLST (Web – Basic)

  • Objective: MLST typing, allele calling, database exploration, and species typing. 
  • URL: https://pubmlst.org/

  10. Galaxy  (Web – Basic)

  • Objective: Run bioinformatics workflows (species identification, read mapping, assembling, variant calling, etc.) via a graphical interface. 
  • URL: https://usegalaxy.eu/

  10. PathogenWatch (Web – Basic)

  • Objective: Perform species identification, MLST typing, AMR gene detection, plasmid detection, and generate interactive phylogenetic trees directly from assembled genomes. 
  • URL: https://pathogen.watch/ 

  
  

  
  
• Session 1: Introduction to molecular epidemiology and epidemiological analysis in outbreak Investigations

  Session 1: Introduction to molecular epidemiology and epidemiological analysis in outbreak Investigations

  
  

  • 1. Introduction to Brucella spp. and brucellosis 

    • Epidemiological background of Brucella spp., diversity, overview of brucellosis
      

  • FileTopic1_Introduction_presentation
  • 2. One Health perspective: 

    • One-Health concept.
      
    • Incidence, prevalence, transmission and global context. Human, animal, and environmental relationships.
      

  • FileTopic2_One_Health_presentation
  • 3. Taxonomic analysis and typing:

    • Concept, application, WGS-based tools (alignment and gene-by-gene approaches) and interpretation.
      

  • FileTopic3_Taxonomy_presentation
  • 4. Epidemiological investigations:

    • Epidemiological data, epidemiological curves and hypothesis generation.
      

  • FileTopic_4_Epidemiological_investigations.
  • 5. Phylogenetic analysis: 

    • Overview of phylogenetic analyses, brief comparison of classical and genomic approaches, explanation of SNP-based phylogeny.
      
    • How SNP trees are used in epidemiology, and overview of tree construction and interpretation.
      

  • FileTopic5_Phylogenetic_analysis_presentation
  • 6. Introduction to the exercise:

    You will receive a small set of approximately 30 Brucella spp. genomes (assemblies as FASTA files and raw FASTQ files) and the respective metadata via ScienceData (https://sciencedata.dk/shared/e9cba091ad1d946970bfdd67e8491b2a).

  • FileTopic6_Exercise_presentation
• Homework - between sessions

  Homework - between sessions

  Your task is to investigate the outbreak and track its source, using the sequencing and epidemiological data as well as freely available or recommended bioinformatics tools (you can use different tools).

  Your task (using tools of your choice):

  • Identify the bacterial species present in the dataset
    
  • Identify SNP differences between isolates
    
  • Construct and interpret a phylogenetic tree
    
  • Assess whether a genomic outbreak cluster is present, and define the outbreak cluster if so
    
  • Infer the most likely source and route(s) of transmission, and discuss alternative explanations where relevant
    
  • Assess whether the reported livestock miscarriage events are epidemiologically linked to the human cases 
    
  • Create basic epidemiological curves from the metadata using Microsoft Excel
    
  • Critical reflection: Notes on any limitations or uncertainties in your analysis.
    

  The data:

  • 30 Brucella spp. genomes 
  • Illumina sequences
  • Provided as FASTA files and raw FASTQ files
  • Metadata and quality control of isolates in excel file
  • Outbreak scenario PDF 

  All files are available on ScienceData via this link:

  https://sciencedata.dk/shared/e9cba091ad1d946970bfdd67e8491b2a  

  Detailed outline of the exercise with tool suggestions:

  1. Identify and type the bacterial isolates. Below are some tool suggestions:

  • KmerFinder (web-based): https://cge.food.dtu.dk/services/KmerFinder/
  • rMLST (web-based): https://pubmlst.org/species-id
  • pubMLST (web-based): https://pubmlst.org/organisms
  • Galaxy (web-based): https://usegalaxy.org/

  2. Create basic epidemiological curves for the datasets using Microsoft Excel:

  • Consider which species to include in the curves: all species or only some species.
  • Consider which isolates to include in the curves: all isolates or selected isolates.
  • Consider which metadata to highlight in the curves: source, date, other.
  • Create a hypothesis stating the suspected outbreak source and transmission routes.

  3. Perform SNP-based phylogenetic analysis with isolates belonging to the same species. Below are some tool suggestions:

  • CSI Phylogeny (web-based): https://cge.food.dtu.dk/services/CSIPhylogeny/
  • Snippy (if using Linux/command line): https://github.com/tseemann/snippy
  • Galaxy platform SNP analysis workflows (web-based): https://usegalaxy.org/

  4. Build phylogenetic trees from the SNP alignments or VCF outputs. Below are some tool suggestions:

  • MEGA (for local tree reconstruction): https://www.megasoftware.net/
  • CSI Phylogeny or MINTyper (auto-generates tree from uploaded genomes): https://cge.food.dtu.dk/services/CSIPhylogeny/ ; https://cge.food.dtu.dk/services/MINTyper/
  • PathogenWatch (auto-generates tree from uploaded genomes): https://pathogen.watch/
  • iTOL (for visualization): https://itol.embl.de/

  5. Interpret the trees:

  • Define what are the appropriate genetic relatedness thresholds for each phylogenetic tree.
  • Identify clusters and closely related isolates within each tree.
  • Identify the outbreak strain.
  • Estimate the likely source of the outbreak strain based on tree topology and SNP distance.

  6. Correlate the phylogenetic clusters with the epidemiological data:

  • Re-evaluate your hypothesis based on the results from phylogenetic analysis. Did your hypothesis change after incorporating the phylogeny data?
  • What is your final conclusion regarding the outbreak source and transmission?
  • What was the impact of the two approaches (epidemiological investigation and phylogenetic analysis)?
  • Assess whether the reported livestock miscarriage events are epidemiologically linked to the human cases 
• Session 2: Interpretation of results and discussion on the application of WGS and epidemiological investigations in outbreak scenarios

  Session 2: Interpretation of results and discussion on the application of WGS and epidemiological investigations in outbreak scenarios

  
  

  • Opening

    
    

  • 1. Guided discussion using interactive polls:

    • Participants will engage in a structured discussion of their analysis results using live polls and questions.

    • Topics covered include: Taxonomic analysis and typing; Creation of epidemiological curves and hypothesis generation; Phylogenetic tree construction and interpretation; Identification of outbreak clusters; Correlation of epidemiological data with phylogenetic trees.

    
    

  • FileTopic1_dicussion_presentation
  • 2. Comparative and critical analysis:

    • Live discussion of participant responses and aggregated poll results.

    • Facilitators will explore different analytical paths and conclusions.

    • Live discussion of the advantages and limitations of WGS-based and epidemiological outbreak investigations.

     

     
    

  • FileTopic2_Analysis_presentation
  • 3. Troubleshooting:

    • Discussion of common challenges encountered during the analysis, including ambiguities and tool limitations.

    • Strategies for dealing with conflicting molecular and epidemiological data.

     

     
    

  • FileTopic3_Troubleshooting_presentation
  • 4. Reflections:

    • Real-life examples of WGS-based outbreak investigations.

    • Reflection on One Health implications and genomic surveillance strategies including challenges in surveillance of zoonotic diseases.

    • Open Q&A session for participants to raise questions and share reflections on integrating WGS into outbreak investigations.

     

     
    

  • FileTopic4_Reflections_Presentation
• Contacts

  Contacts

  For any questions regarding the this webinar series, please use the discussion forum.

  If you have questions regarding other training initiatives please contact: courses@ecdc.europa.eu.

  If you have technical issues please contact ECDC Helpdesk - ICT Front Office ICT.FrontOffice@ecdc.europa.eu.