Monday. Classes from 2 to 8 pm Berlin time
We will discuss the rationale behind the use of low-coverage whole-genome sequencing for population genomic inference. We will then walk through typical workflows for going from sample to raw sequencing data, perform a de novo genome assembly, and finally obtain mapped sequencing reads.
Tuesday. Classes from 2 to 8 pm Berlin time
We will introduce the concept of sequencing data uncertainty and genotype likelihoods. We will then discuss the currently available software for population genetic analysis from low-coverage data, including ANGSD, ngsTools and Atlas. Finally, we will explore how to estimate allele frequencies and perform SNP calling with ANGSD.
Wednesday. Classes from 2 to 8 pm Berlin time
We will explore how to infer population structure and demographic parameters from low-coverage sequencing data using ANGSD. We will focus on performing principal component analysis and admixture inference. We will finally discuss how to estimate the site frequency spectrum and inbreeding coefficients.
Thursday. Classes from 2 to 8 pm Berlin time
We will explore how to estimate summary statistics to detect signals of natural selection. Specifically, we will explain the protocol for estimating genetic variation indexes at the genome-wide level and in sliding-windows with ANGSD. We will focus on both single-population metrics, such as Tajima’s D and linkage disequilibrium, and multi-population metrics, such as FST and PBS.