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.
