Current Topics in Bioinformatics workshops

Audience	Computational Skills	Duration
Biologists	Beginner or intermediate R and/or beginner bash	2-3 hour workshops

This repository has teaching materials for 2-3 hour, hands-on workshops covering a variety of topics related to bioinformatics data analysis. The workshops will lead participants through performing different types of analyses using R/RStudio or Shell/bash.

** NOTE: Detailed information and preparation instructions for each of the workshops can be found by clicking on the workshop links in the table below.

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Current Topics in Bioinformatics workshops 2026 Schedule (9:30am - 12:30pm):

Topic and Link(s) to lessons	Prerequisites	Date	Registration
Getting Started with Nextflow Workflow Development	Basic experience with command line and scripting concepts (e.g. bash, python, R or equivalent)	6/10/26	Register here
Making Sense of Single-Cell Data: Practical Exploration and Interpretation with Loupe Browser	None	7/15/26	Register here
Machine Learning for Biologists	None	8/19/26	Register here
The cBioPortal for Cancer Genomics	None	9/23/26	Register here
Single Cell RNA-seq Analysis Using Cellenics	HarvardKey Credentials	10/21/26	Register here

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Links to workshop materials

R-based workshops:

Topic and Link(s) to lessons	Prerequisites
Foundations in R	None
Tidyverse	Introduction to R, Foundations in R, or Introduction to R online resource
Introduction to R Practical	Introduction to R, Foundations in R, or Introduction to R online resource
Gene annotations and functional analysis of gene lists	Introduction to R, Foundations in R, or Introduction to R online resource
Generating research analysis reports with RMarkdown	Introduction to R, Foundations in R, or Introduction to R online resource
Interactive Data Visualization with Shiny in R (with Ista Zahn from the Harvard Business School)	Introduction to R, Foundations in R, or Introduction to R online resource
Publication Perfect: Part I	Introduction to R, Foundations in R, or Introduction to R online resource
Publication Perfect: Part II	Publication Perfect: Part I
Functional analysis of gene lists	Introduction to R, Foundations in R, or Introduction to R online resource

Shell-based workshops:

Topic and Link(s) to lessons	Prerequisites
Foundations in Shell	None
Accelerate with Automation - Making your code work for you	Shell for Bioinformatics or Foundations in Shell
Needle in a Haystack - Finding and summarizing data from colossal files	Shell for Bioinformatics or Foundations in Shell
Shell Tips and Tricks on O2	Shell for Bioinformatics or Foundations in Shell
Version control using Git and Github	Shell for Bioinformatics or Foundations in Shell
Accessing genomic reference and experimental sequencing data	Shell for Bioinformatics or Foundations in Shell
Exploring genomic variants using GEMINI	Shell for Bioinformatics or Foundations in Shell

Additional workshops:

Topic and Link(s) to lessons	Prerequisites
Introduction to Python	None
Planning a bulk RNA-seq analysis: Part I	None
Planning a bulk RNA-seq analysis: Part II	None
Make your (RNA-seq) data analysis reproducible- Taught by Julie Goldman from Countway Library	None
Improving your (RNA-seq) data analysis using version control (Git)	None
Introduction to scRNA-seq and data pre-processing	Introduction to R, Foundations in R, or Introduction to R online resource and Shell for Bioinformatics or Foundations in Shell

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These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC) RRID:SCR_025373. These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

A lot of time and effort went into the preparation of these materials. Citations help us understand the needs of the community, gain recognition for our work, and attract further funding to support our teaching activities. Thank you for citing the corresponding course (as suggested in its "Read Me" section) if it helped you in your data analysis.