curated by UC Davis College of Biological Sciences

Category: Genomics

Practical Introduction to Bioinformatics and Genomics – Bio720 Course at McMaster University

This repository contains many of the course materials (for free!) from the Bio720 course taught by Brian Golding and Ian Dworkin at McMaster University in 2018. While this course is structured for graduate students, anyone who has a working knowledge of basic molecular biology and genetics should be able to use this course. The goal of this course is to allow the user to develop fundamental computational skills that will prepare them to further develop skills appropriate for bioinformatics and genomics.

To learn more about this course, click here.

Comparative Methods in R

This is a set of lectures, exercises, and challenges using R. Topics covered include: an introduction to comparative methods and R, Brownian motion, PGLS, ancestral state reconstruction, trait evolution, diversification with BAMM and RPanda, state dependent diversification, Mk models and character correlations, and placing fossils/recently extinct taxa on a tree.

NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016

This video series (~11 hours total) focuses on the use of R to build and test evolutionary models. It reviews basic aspects of quantitative genetic theory and illustrates how that theory can be tested with data , both from single species and with multiple-species phylogenies. To view the videos, select the “Videos” tab near the top right of the page.


EMBL-EBI Training Online


EMBL-EBI offers free online courses in bioinformatics to help novices become competent in processing large quantities of biological data. These tutorials are estimated to take 0.5-1 hour to complete and can be done at your own pace.


Seurat: R Toolkit for Single Cell Genomics (Satija Lab)


A guided analysis tutorial using the Seurat clustering workflow– featuring new computational methods for single-cell datasets. Implements QC and data filtration, calculation of high-variance genes, dimensional reduction, graph-based clustering and the identification of cluster markers.