When: 10-13 October 2022
Instructors: Dr. Nina Overgaard Therkildsen (Cornell University, US) Dr. Tyler Linderoth (University of Cambridge, UK), Dr. Arne Jacobs (University of Glasgow, UK) and Nicolas Lou (Cornell University, US)
In this course, we will explore workflows and the underlying rationale behind producing, processing, and analyzing low-coverage sequencing data for population genomic inference. Given that most species have insufficient reference data to allow reliable genotype imputation, we will focus on genotype likelihood-based methodology that can be applied to any system. We will primarily cover methods and algorithms implemented in the ANGSD software package and associated programs, providing best-practice guidelines and discussion of how participants can make maximal use of low-coverage whole genome re-sequencing data for their studies.
The course is aimed at researchers who might have previous experience with next generation sequencing (NGS) data (e.g. exome/RAD/pooled sequencing) and wish to explore the potential for using low-coverage whole-genome sequencing for their studies.
All hands-on exercises will be run in a Linux environment on remote servers. Statistical analyses and data visualization will be run in R.
Full list of our courses and Workshops: https://www.physalia-courses.org/courses-workshops