crukcibioinformatics/quantitative_chip_workshop repository overview

⁠Quantitiative analysis of ChiP-seq, ATAC-seq, and related DNA enrichment assays.

⁠Instructor

Rory Stark, Principal Scientist Computational Biology, University of Cambridge, Cancer Research UK Cambridge Institute

[email protected]⁠

⁠Workshop Description

This workshop will demonstrate the steps involved in performing a quantitative analysis of ChIP-seq data in Bioconductor (up to and including differential binding analysis), with some discussion of related assays such as ATAC-seq. Particular attention will be paid to processing of aligned reads, including blacklisting, greylisting, filtering for quality and duplication, and the particular challenges presented when normalizing these data. While the workshop follows the DiffBind package vignette, the use of a number of other Bioconductor packages is discussed, including csaw, ChIPQC, edgeR, DESeq2, and GreyListChIP.

⁠Pre-requisites

Attendees should be familiar with the following:

• Basic knowledge of R syntax
• Familiarity with core Bioconductor classes, such as GRanges.
• Familiarity with ChIP-seq or a related assay, including aligning sequencing reads.
• Familiarity with generalized linear models (GLMs), especially as realized in the edgeR and/or DESeq2 packages.

Helpful background reading:

• Read through the DiffBind package vignette
• Read through the csaw package vignette

There is a book chapter which offers a detailed outline of the design and analysis of ChIP-seq experiments:

• Stark, Rory, and James Hadfield. "Characterization of DNA-protein interactions: design and analysis of ChIP-seq experiments." Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing. Springer, Cham, 2016. 223-260.

Email the instructor if you would like a copy.

⁠Workshop participation

A mini-lecture will be given at the beginning that will cover basic ideas behind ChIP-seq, comparision of commonly used tools for ChIP-seq data analysis. Then, a hands-on demo will be performed to demonstrate the downstream analysis with ChIPpeakAnno and trackViewer packages. Last will be a Q/A section.

⁠How to run Docker

• Install Docker (https://www.docker.com/⁠)
• Pull the Docker image from Docker hub: docker pull crukcibioinformatics/quantitative_chip_workshop
• Run the image: docker run -e PASSWORD=yourpassword -p 8787:8787 crukcibioinformatics/quantitative_chip_workshop
• Log in to RStudio at http://localhost:8787⁠ using username rstudio and password yourpassword. For Windows users, you also need to provide your IP address, you can find it using docker-machine ip default.
• Inside the RStudio, run: browseVignettes(package = "Quantitative-ChIPseq-Workshop")
• You can click one of the links: "HTML", "source", "R code"

⁠R / Bioconductor packages used

• DiffBind
• csaw
• ChIPQC
• DESeq2
• edgeR
• GreyListChIP

⁠Time outline

The workshop duration is 90 min. Approximate timing of activities:

⁠Workshop goals and objectives

Participants will learn a complete set of steps for conducting a differential binding analysis of data from a ChIP-seq or related DNA enrichment experiment within Bioconductor.

⁠Learning goals

• Gain an understanding of the processing flow for performing quantitative analysis of data from ChIP-seq and related DNA enrichment assays
• Be able to filter and count aligned reads to prepare them for analysis
• Appreciate the challenges inherent in normalizing these data
• Complete a differential binding analysis

⁠Learning objectives

• Load experimental data into DiffBind
• Filter aligned reads using blacklists, greylists, and mapping quality
• Derive a consensus peak set and compute a matrix of read counts
• Normalize read counts appropriately
• Establish a design to model the data and one or more contrasts to test hypotheses based on the model
• Report, plot, and export results for further analysis