Conservation Genomics

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Start - End
27 Sep - 30 Sep
Study Options

Dates: 27th-30th September 2021

Due to the COVID-19 outbreak, this course will be held online


This course will introduce biologists to how the tools of population genomics can be used to inform conservation. The instructors will guide students through study design, genomic data collection methods, handling of raw genomic data, and SNP filtering to produce a dataset. Then, we will work through a suite of analyses looking at population structure, local adaptation, effective population size, inbreeding and relatedness. We will provide background on the theory and application of these analyses, and then run hands-on exercises running analyses and interpreting results. Through hands-on exercises, the course will teach basic bioinformatics skills and how to manipulate, visualize and interpret genomic data and patterns in a conservation related context.

Target audience and assumed background

The course is aimed at graduate students and researchers who are interested in using genomic tools to address issues in conservation. Participants should have some basic background in evolution and population genetics. Previous experience in UNIX-based command line and R is required. Hands-on exercises will be run in a Linux environment on remote servers and data analysis and visualization will be run in R using RStudio.

Teaching format

The course will be delivered fully online over 4 half-day (5 hour) sessions, with a combination of lectures and practical exercises that will be live (synchronous). Discussions among participants and with the instructors on concepts and data analyses will be possible through video conferencing and a dedicated Slack workspace.

Learning outcomes

1. Study design and genomic data collection methods
2. Handling genomic data from raw reads to a filtered dataset of SNP genotypes
3. Assessing population structure using multiple methods
4. Searching for signals of adaptation
5. Estimating effective population size
6. Calculating inbreeding
7. Estimating relatedness


Monday – Classes from 2-8 pm Berlin time

Part 1: Introduce the group, study design, data collection methods


  • Go around the room, introduce ourselves and research interests
  • Discuss study design: DNA sample sources, quality of DNA, sample sizes
  • Discuss data collection methods: whole genome sequencing, capture (exon or mtDNA), RADseq, SNP panel
  • Introduce various file types (fastq, fasta, SAM/BAM, vcf, BED, program specific inputs)

Part 2: Data assembly (to reference genome), SNP and haplotype calling


  • Accessing reference genomes, understanding their quality, “in-group” reference bais
  • General introduction to de novo approaches
  • General introduction to the idea of SNP calling


  • Looking at the files associated with a reference genome, indexing it
  • Use BWA to assemble some reads to the genome
  • Check quality of assembly, filter BAM
  • Call SNPs using BCFtools mpileup/call, output VCF

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