This course will run from 31st October – 4th November, Millport Field
Station, Ilse of Cumbrae, Scotland
The main objectives of the course are to teach the theoretical bases of
phylogenetic analysis, and to give the ability to initiate a
phylogenetic analysis starting from the files of molecular sequences
until the interpretation of the results and the graphics. The
introduction will cover a brief historical background and an overview of
the different methods of phylogenetic inference. Different kinds of data
will be considered, but with a special emphasis on DNA sequences. The
software used will be based on R and several specialized packages
(particularly ape and phangorn). Other software will be used (e.g.,
MUSCLE or Clustal) called from R. Overall, the course will cover almost
all aspects of phylogenetic inference from reading/downloading the data
to plotting the results. This course is intended for PhD and
postgraduate students, researchers and engineers in evolutionary
biology, systematics, population genetics, ecology, conservation.
Course content is as follows
• Refresher on R: data structures, data manipulation with the indexing
system, scripts, using the help system.
• Introduction to phylogenetic inference.
• Basics on phylogenetic data (sequences, alignments, trees, networks,
“splits”) and other data in R.
• Reading / writing data from files or from internet.
• Matching data. Manipulating labels. Subsetting data.
• Main package: ape.
• Plotting and annotating trees.
• Theory of sequence alignment. Comparing alignments. Graphical
analyses of alignments.
• Main packages: ape (with MUSCLE and Clustal).
• Theory and methods of phylogeny reconstruction.
• Parsimony methods.
• Evolutionary distances.
• Distance-based methods: General principles and the main methods (NJ,
BIONJ, FastME, MVR).
• Methods for incomplete distances matrices (NJ*, BIONJ*, MVR*).
Methods for combining several matrices (SDM).
• Main packages: ape, phangorn.
• Theory of maximum likelihood estimation.
• Application to phylogeny reconstruction.
• Substitution models.
• Tree space and topology estimation.
• Main packages: ape, phangorn.
• Tree comparison, consensus methods.
• Topological space and distances.
• Bayesian methods.
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