Genomics Data Analysis Course: ‘ConGen’ 2026 Online
 
Course Objectives: To provide training in conceptual and practical approaches for using genomic data to address key questions in ecology, evolution, and conservation. Learn next-generation sequencing data analysis and interpretation from raw sequence reads through filtering and genotyping. Participants will learn to conduct genome assembly, estimate effective population size (Ne), assess inbreeding and gene flow, detect selection, conduct metabarcoding (for biodiversity assessment and diet analyses), apply genomic approaches to forensics, and conduct landscape genomics, and more. See lecture topics below & www.umt.edu/congen/
Who should apply: Advanced undergraduates, M.S. and Ph.D. students, post-docs, faculty, with a basic understanding of population genetics. We teach R and Linux skills the first week to help maximize your learning.
Where: Online (Zoom). Lectures are video-recorded to facilitate asynchronous participation by international participants.
When: Monday, Wednesday & Friday, 8-10:00 AM-ish, Sept. 24th – Nov. 13th (20 + lectures by 15 + experts)
Instructors: Eric Anderson, Ellie Armstrong, Matt DeSaix, Chris Funk, Marty Kardos, Brenna Forester, Will Hemstrom, Paul Hohenlone, Bruce Ranala, Rena Schweizer, Arun Sethuraman, Steven Spear, Robin Waples, Schuyler Liphardt, Steve Spear, and more...
To apply, email leif.howard@flbsad.umt.edu or go to www.umt.edu/congen/
Course credit: A course certificate and academic credit are available through the University of Montana (BIOB 591 Pop Gen Data Analysis).
Selected lecture topics:  see www.umt.edu/congen/ for the full list & instructors
The role of genomics in population genetics, ecology, and conservation.
Pop genomics: Concepts and tools to answer eco-evo questions
R & Linux basics, FastQ file format for next-generation sequencing data
Scripting, data handling, & organizing bioinformatics projects
Probability, Bayesian statistics, MCMC, and genotype likelihood calculations
The Coalescent: Theory and applications
Raw sequence read filtering and genotype calling (with and without a reference genome)
Filtering (QC) best practices and effects on downstream analyses (HWE, selection tests…)
Inbreeding and runs of homozygosity (RoH)
Forensics applications and non-invasive genetics (PID, assignment tests)
Genome sequencing and assembly: Conceptual and practical aspects
PacBio representatives will present recent technologies and services
Inferring population structure and conservation units
Effective population size estimation
Assignment tests for quantifying gene flow, dispersal, and forensics assignments (WGSassign, GeneClass)
Gene flow estimation (BayesAss)
Detecting local adaptation and selection signatures (Landscape Genomics)
Phylogeny and phylogenomics 
eDNA Metabarcoding applications (biodiversity monitoring, diet analysis, microbiomes, etc.)
Uses of AI in genomics data analysis.
Detection of selective sweeps.

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