DDLS Population genomics in practice

NBIS workshop

 

6 - 10 Nov 2023

 

map iconTrippelrummet, Navet, BMC Uppsala 

Application form


 

Course links

Clock SVG Vector Schedule

Course web github pages

Github repository

 

Course teachers

Please use course email for contacts: edu.population-genomics-in-practice@nbis.se or edu.pgip@nbis.se

Per Unneberg (Uppsala University, course leader)

Nikolay Oskolkov (Lund University)

Jason Hill (Uppsala University)

Andre Soares (Uppsala University)

Out of Africa demographic model  
  

 

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Dna SVG FileThe aim of this workshop is to provide an introduction to commonly used methods in population genomics. As the focus of the course is on hands-on work, the topics have been designed to cover the fundamental analyses that are common in many population genomics studies. The course consists of lectures and exercises, with a focus on the practical aspects of analyses. Whereas lectures introduce some background theory, their primary aim is to set the stage for accompanying exercises.

Covered topics


  • Foundations of population genetics
  • Introduction to simulation and the coalescent
  • Basics of variant calling
  • Variant filtering and sequence masks
  • Characterization and intepretation of DNA sequence variation
  • Calculation and interpretation of summary statistics from variation data
  • Investigating population structure with admixture modelling and principal component analyses
  • Demographic modelling using sequentially Markovian coalescent models and linkage disequlibrium
  • Selection scans

Learning objectives


Upon completion of this course, you will be able to:

  • describe the different forces of evolution and how they influence genetic variation
  • understand and interpret genealogical trees and how they relate to genetic variation data
  • describe the basics of the coalescent
  • perform simple coalescent simulations with msprime
  • run simple SLiM forward simulation models
  • describe and run the steps of a variant calling pipeline, including quality control of raw reads, read mapping, and variant calling
  • know how and when to filter raw variant calls using manual coverage filters
  • describe and calculate nucleotide diversity from variation data
  • analyze population structure with admixture modelling and dimensionality reduction methods
  • perform demographic modelling with sequential Markovian coalescent models
  • describe methods that identify regions undergoing adaptation and selection
  • run selection scans, score identified regions and interpret findings in the context of genome annotations

 

Application


Course open for PhD students, postdocs, group leaders and core facility staff in Sweden looking for an introduction to the practical analyses of population genomic data. This course can accommodate a maximum of 25 participants. If we receive more applications, participants will be selected based on several criteria including entry requirements, motivation to attend the course as well as gender and geographical balance.  Please note that NBIS training events do not provide any formal university credits. If formal credits are crucial, the student needs to confer with the home department before submitting a course application in order to establish whether the course is valid for formal credits or not.

Applications are open.

Fee This onsite training event entails a fee of 3000 SEK. Should you accept a position at the workshop and do not participate (no-show) you will also be invoiced 3000 SEK. Please note that NBIS cannot invoice individuals.

Entry requirements

Practical exercises will be performed using R or Python, so we only accept students with previous experience in one of those programming languages.

Required:

  • Basic knowledge in R or Python
  • Basic knowledge of variant calling, or the equivalent of NBIS course "Introduction to Bioinformatics using NGS data"
  • Basic knowledge of population genetics
  • Basic understanding of frequentist statistics
  • A computer

Desirable:

  • Experience with analysis of NGS and other omic data

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