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Background

  • In the context of viruses, molecular epidemiology is used to describe how we can make inferences about the transmission, distribution, etiology, and prevention of viral infections within a population
  • Given the decreasing cost of sequencing, the use of molecular epidemiology to understand viruses is increasing

Questions

  • There are many questions that can be addressed with sequence data
    • When did an epidemic start?
    • How did an epidemic spread spatially, and between different risk groups?
    • What are the dynamics of transmission over time
  • These questions involve reconstructing phylogenetic trees from sequence data

Transmission and phylogenies

Ideally, we would like to know about the transmission history of a pathogen, but even in ideal cases, there is a loss of information in the phylogeny.

  • No information before the common ancestor of the samples
  • Direction of transmission is lost
  • No information on individuals who have died/recovered before sampling
  • We do not know which host is infected by which virus (except sampled individuals)
  • Not all infection events are 'observed'

Workflow

  • A typical molecular epidemiology workflow is rather linear
    • Obtain sequences
    • Align
    • Screen for recombination, if necessary
    • Reconstruct phylogeny
    • Integrate phylogeny with other data (e.g. time, country)
    • Visualise results

Problems

  • There are many steps
  • There are many software packages available to perform even a single part of this workflow

Why R?

  • Free, general purpose statistical software
  • Many libraries (>4000), including those for sequence analysis
  • Can call external programs
  • Literate programming

Why RStudio

  • R runs in a terminal
  • RStudio sits on top of R, and offers a number of additional features
    • Multiple windows for editing and running of code
    • Workspace browser
    • File browser
    • Integrated help
    • Graphics window

What you'll (hopefully) learn

  • Retrieving sequence data
  • Obtaining sequence metadata
  • Processing and altering sequence names
  • Developing simple pipelines

What you won't learn

  • Processing of next-generation sequencing data
  • BEAST

Course structure

Next steps

Next, we'll go through a minimal example of generating a phylogeny. We'll do much more, both in terms of upstream analysis (data processing) and in downstream analysis (visualisation and interpretation) later.