I. Introduction

  • Conceptual framework (theory).
  • A primer on bioinformatics (hands-on).
  • High-throughput sequencing data: presenting the different types of data and identifying the most suitable for each research project (seminar).
  • Data formats (fastq, nexus, phylip, BAM, SAM, mpileup, VCF, etc.) (hands-on).
  • Sanitation: filtering of low quality and low complexity reads, adapter removal and checking for contamination (hands-on).
  • Assembly, Mapping, Alignment, Orthology inference (theory and hands-on).

II. Phylogenomics: Species Tree reconstruction.

  • Models of DNA and protein evolution (theory).
  • Model selection and partitioning scheme (hands-on).
  • Methods for phylogenetic inference (I): Parsimony (theory and hands-on).
  • Methods for phylogenetic inference (II): Maximum Likelihood (theory and hands-on).
  • Methods for phylogenetic inference (III): Bayesian Inference (theory and hands-on).
  • Sensitivity analysis: testing phylogeny robustness (theory and hands-on).

III. Population Genomics (I): Neutral variation.

  • The coalescent theory. Gene genealogies and sample configuration
  • Quantifying neutral genetic variation across the genome. Population genetics parameters and neutrality tests
  • Modelling neutrall variation across the genome. Coalescent samplers
  • Variant calling from low-coverage population genomics data
  • Inferring the demographic history of multiple populations from the site frequency spectrum (SFS) and using Moran models.

IV. Neutral genomic variation: Applications.

  • Molecular dating. Sources of calibration points. Molecular clocks.
  • Species delimitation, discovery and validation methods.
  • Biodiversity assessment methods (metabarcoding).

V. Population Genomics (II): Adaptive variation.

  • Positive selection and the SFS
  • Distribution of fitness effects of new mutations and proportion of adaptive substitutions.
  • The McDonald and Kreitman test at its extensions.
  • Modelling neutral and adaptive variation across the genome. Forward genetics simulations of complex scenarios.
  • Population differentiation and local adaptation. Inferences based on covariance matrices.
  • Genome-wide scans of selection. Composite statistics and local differentiation.

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