Congratulations! You have successfully concluded this training session

Citation reference (copy and paste into your CV): 

"Analysis of epigenomics data obtained by Chromatin Immunoprecipitation sequencing (ChIP-seq) in the R/RStudio environment". (November 3rd, 2022). 2-hours workshop by Mariateresa Mazzetto, PhD. Organized by the Bioinformatics Support Hub, Cushing/Whitney Medical Library at Yale School of Medicine.  https://guides.library.yale.edu/epigenomics_R 

Workshop Summary:

This workshop was part of the third season of the Peer-to-Peer Teaching sessions. These are taught by our own community members to fill knowledge gaps and keep up with the accelerated pace at which bioinformatics grows. This season we are pleased to announce sessions covering * Image analysis methodologies (imageJ and QuPath) * Omics analysis in R and/or Python * Biomedical Data analysis using the clusters * Statistical analysis software (Prism) *Science communication (Illustrator).

This session on "Analysis of epigenomics data obtained by Chromatin Immunoprecipitation sequencing (ChIP-seq) in the R/RStudio environment" and was designed and delivered by Mariateresa Mazzetto, PhD from the  Department of Genetics. 

Introduction: 

You were able to reproduce a script aimed at the analysis of raw chromatin immunoprecipitation sequencing (ChIP-seq) data. Chip-seq is a high-throughput technique used for the detection of enriched loci within a genome. The technique uses an antibody for a specific DNA-binding protein or a histone modification and needs a systematic downstream analysis to discover how the epigenomic landscape contributes to cell development and specification, as well as disease.

In this lecture you have learned the basics of epigenomic data downstream analysis, in particular differential binding profiles, annotation, and visualization of the data, using the “DiffBind” package in R/RStudio environment. 

This workshop was designed for: 

Biomedical researchers at every stage. Minimal experience of R programming is appreciated but not essential. Limited to 20 seats.

You have learned how to:

- import your data into the R environment

- load and run the required packages

- perform differential binding analysis with DiffBind

- perform basic data visualization (PCA, heatmap, dotplots).