The training team at the Harvard Chan Bioinformatics Core provides bioinformatics training through both shorter workshops and longer in-depth courses. Our current workshops and courses are designed to help biologists become comfortable with using tools to analyse high-throughput data. We are slowly beginning to expand this repertoire to include training for researchers with more advanced bioinformatics skills.
No prior NGS or command line expertise is required for our workshops or courses unless explicitly stated.
The goal of these workshops (2-3 days) are to enable researchers to design their NGS studies appropriately and perform preliminary data analyses.
| Training topic and link to lessons | Prerequisites | Workshop Duration |
|---|---|---|
| R and ggplot2 | None | 2 days |
| RNA-seq data analysis using High-Performance Computing | None | 2 - 3 days |
| R and Differential Gene Expression (DGE) analysis | None | 3 days |
| Differential Gene Expression (DGE) analysis | R and ggplot2 | 1.5 days |
| ChIP-seq using High-Performance Computing | None | 3 days |
| Identifying variants in genome/exome sequencing data | None | In development |
These short workshops (half-day or less) are designed to allow researchers, who have some familiarity with R or bash, to learn new tools and methods. Only a subset of the short workshops topics are listed and linked below, for the full list please click here.
| Training topic and link to lessons | Prerequisites |
|---|---|
| Introduction to R & Visualizations with ggplot2 | None |
| Plotting and visualization in R using ggplot2 | Intro to R |
| Functional analysis of gene lists | Beginner R or IntroR workshop |
| Reproducible research using R (Rmarkdown: report generation) | Beginner R or IntroR workshop |
| Introduction to shell/bash | None |
| Intermediate shell/bash | Intro to shell |
| Accessing public data for genomics | Intro to shell |
| Version control using Git and Github | Intro to shell |
| Exploring genomic variants using GEMINI (In development) | Intro to shell |
| Introduction to tidyverse R packages for data visualization (In development) | Intro to R |
This intensive course runs for between 8 to 12 days and is aimed at bench biologists interested in learning how to independently perform NGS data analyses using best practices. Topics include:
- High-performance computing
- Best practice workflows for NGS data analysis (RNA-seq, ChIP-seq, Variant calling)
- R for statistical analysis and data visualization
- Functional analysis with gene lists
- Additional skils and tools for better reproducibility like reports with RMarkdown, version control with Git/Github, etc.
Email: hbctraining@hsph.harvard.edu
Webpage: http://bioinformatics.sph.harvard.edu
Twitter: @bioinfocore