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Course: Speciation Genomics in Canada

carlo pecoraro, March 14, 2019

Course: Speciation Genomics

When: 2-6 September 2019

Where: Institut de Biologie Intégrative et des Systèmes (IBIS) Pavillon Charles-Eugène-Marchand 1030, Avenue de la Médecine Room 1210 (Hydro-Québec Room) Université Laval Québec (Québec) G1V 0A6 Canada


Dr. Mark Ravinet (CEES, Univeristy of Oslo, Norway)

Dr. Joana I. Meier (University of Cambridge, UK)


This course will provide a thorough introduction to the growing field of speciation genomics. The course aims to take students from the initial steps required for handling raw sequencing data to demographic modelling and inference of genome-wide signatures of selection and introgression. Through a combination of lectures covering key theoretical and conceptual topics, alongside hands-on exercises, participants will learn the most important computational approaches used in speciation genomics. This will include a heavy emphasis on data visualization and intepretation. After completing of the course, the participants should be able to begin using NGS data to shed light on the genomic aspects of speciation in their study system of choice.


This course is designed for researchers and graduate students with strong interests in applying novel high-throughput DNA sequencing technologies to study the population genomic basis of speciation. The course will mainly focus on the analysis of NGS data for study systems for which a reference genome is available. We will provide theoretical lectures and hands-on exercises drawing on examples of whole-genome resequenced and RAD-sequencing data. Participants will make use of the UNIX command line, R and Python throughout the course.

Assumed Background

The participants should have some basic background in evolution and genomics. No programming or scripting expertise is required. Previous experience in UNIX-based command line and R is an advantage but a standard introduction will be provided. All hands-on exercises will be run in a Linux environment on remote servers. Statistical analyses will be run in R using RStudio.

Learning Outcomes

Handling NGS data from raw reads to genetic variants

Applying basic population genetic statistics

Visualizing the genetic structure

Inferring demographic history

Identifying regions under divergent selection or barriers to gene flow

Understanding the potential and limitations of different methods to detect regions under selection

Please visit our website to have more information about the course content:

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Carlo Pecoraro, Ph.D

Physalia-courses DIRECTOR

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