Introduction
1. Background
2. Retrieving sequences
- 2.1. Theory
- 2.2. Practice
3. Sequence alignment
- 3.1. Theory
- 3.2. Practice
4. Distance-based analyses
- 4.1. Theory
- 4.2. Practice
  - 4.2.1. Neighbour joining example
  - 4.2.2. Exercise
5. Recombination
- 5.1. Theory
- 5.2. Practice
  - 5.2.1. Exercise
6. Maximum likelihood based analyses
- 6.1. Theory
- 6.2. Practice
7. Visualising trees
- 7.1. Practice
  - 7.1.1. Exercise
8. Time-stamped phylogenies
- 8.1. Theory
- 8.2. Practice
  - 8.2.1. Fixing the maximum likelihood tree
  - 8.2.2. Time tree exercise
9. Bayesian reconstruction of time trees
- 9.1. Theory
- 9.2. Practice
  - 9.2.1. MrBayes example
10. Effective population size estimation
- 10.1. Theory
- 10.2. Practice
  - 10.2.1. Fitting skyline
  - 10.2.2. Exercise
11. Structured populations
- 11.1. Theory
- 11.2. Practice
  - 11.2.1. 'Mugration' exercise
12. References
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Practice

There are a number of alignment packages that can be called from R.

msa: calls ClustalW, ClustalOmega, and MUSCLE (BioConductor package)
ips: calls mafft and prank.
ape: calls ClustalW, MUSCLE, T-Coffee

In addition, alignment programs can be called via system calls.

I will go through a worked example, showing how difficult it can sometimes be to align highly variable sequences, followed by an exercise involving simulated data, for which the true alignments are known.