Bioinformatics Workshops & Training

Learn how to use R and RStudio to load, transform, and visualize data.

Prior coding/scripting experience is not required. 

By the end of this workshop, participants will be able to:

• Use RStudio to build R scripts, examine R data structures, and navigate directories and files
• Understand how R are installed and loaded
• Understand the basics of importing data into R and using tidyverse to transform data
• Visualize data as plots using ggplot

Check out the Intro to R and RStudio lesson plans.

A step-by-step guide to the analysis of single-cell RNA-Seq data. 

This workshop assumes a basic knowledge of genetics and familiarity with R/RStudio. Experience with R/Studio or attendance in a prior workshop is strongly recommended (Intro to R & RStudio, Computational Foundations, Software Carpentry). 

By the end of this workshop, participants will be able to:

• Describe how single-cell samples are sequenced, along with the strengths of a few popular library preps.
• Create and interpret preliminary QC visualizations from a single-cell experiment.
• Use PCA and UMAP to create cell-type clusters.
• Identify marker genes and annotate clusters based on gene expression.
• Execute and visualize differential expression across clusters.

Read the Intro to Single-Cell lesson plans. 

Functional Enrichment Analysis places a list of differentially expressed genes into a biological context. It presents a systems-level perspective that can reveal expression patterns, elucidate molecular mechanisms, and guide follow-up experiments.

This workshop is targeted toward researchers comfortable with R/RStudio who would like to better understand and apply functional enrichment approaches for bulk and/or single-cell RNA-seq datasets.

By the end of this workshop, participants will be able to:

• Describe common approaches, tools, and reference databases.
• Run analyses on single-cell and bulk RNA-Seq inputs.
• Run over-representation analysis and GSEA analyses.
• Build meta-analyses across multiple comparisons.
• Visualize gene-level activity within gene sets and create publication-ready figures.

Check out the Intro to Functional Analysis lesson plans.

Learn best practices around data management, software management, and task automation in the U-M IT ecosystem.

This workshop assumes a basic knowledge of Bash/command line or previous attendance in a Computational Foundations workshop, Software Carpentry workshop, or equivalent experience.

This is a workshop for researchers who manage lots of data and/or execute compute-intensive analyses. 

At the end of the workshop, participants will be able to:

• Describe data storage resources available at U-M, best practices for different kinds of data, and how to use Globus to transfer and share data.
• Manage software requirements in a modular, portable fashion using modules (lmod), Conda, and containers (Docker/Singularity).
• Submit jobs to GreatLakes using basic SLURM commands (e.g., squeue) and also using workflow managers (e.g., Nextflow).

Review the Reproducible Computing lesson plans.

An introduction to Unix/Bash and R/R-Studio.

Prior coding/scripting experience is not required. 

By the end of the workshop, attendees will be able to:

• Understand how to use a range of basic Bash commands, including techniques to view and manipulate files from the command line.
• Combine Bash commands to create custom scripts.
• Understand the basics of importing data into R and using tidyverse to transform data
• Visualize data as plots using ggplot

Review the Computational Foundations lesson plans.

A step-by-step guide to the analysis of differential gene expression in bulk RNA-Seq data. 

This workshop assumes a basic knowledge of genetics and prior enrollment in a Computational Foundations workshop, Software Carpentry workshop, or equivalent experience.

By the end of the workshop series, attendees will be able to:

• Use the command line and R to navigate, analyze, and visualize data files.
• Transform raw sequencing data into annotated differential expression values using a suite of open-source tools.
• Visualize and interpret common data quality problems and understand their impact and possible mitigations.

Learn about the RNA-Seq Demystified lesson plans.