Processing and Analysing ChIP-Seq Data

Available Dates

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ChIP-Seq is a high throughput sequencing technology which allows you to identify the position on the genome of anything you can target with an antibody. It was originally used to find trancsription factor binding sites, but has now been extended to many other epigenetic and regulatory marks. There are also other related techniques such as ATAC-Seq or Cut-n-Run etc. which adopt the same analysis proceedures as more traditional ChIP.

In this course we look at the processing and analysis of this data, looking carefully at how to evaluate data quality and identify artefacts which might confound your analysis. We go through options for visualisation and exploration of the data as well as more formal analyses such as peak calling and differential enrichment.

Pre-Course Requirements & Suggestions

Whilst not required, it may be useful to attend the following courses to supplement the knowledge you'll get from this one.

Introduction to Linux and Bash

Course Content

ChIP-Seq theory
We start by looking at how ChIP-Seq libraries are produced and the different populations which can be sequenced. We look at what you can, and cannot, measure with ChIP and how different types of change would affect the reads produced.
ChIP-Seq Data Processing
We go step by step through a standard processing pipeline for ChIP data, starting from raw fastq files and finishing with aligned reads in a BAM file.
Exploring ChIP-Seq data
Of all of the sequencing techniques ChIP-seq is the one which has the highest rate of failure or artefacts. In this session we look at how to work out whether you actually have enrichment in your sample, what the enriched regions look like, and how they might change between samples. We look at the use of input or IgG controls and the role they can play.
Analysis of ChIP data
In the final section we look at some more formal analysis. We look at how peak calling works and how you decide whether to peak call or not. We look at data quantitation and normalisation and the many options this provides, and we finish by looking at different statistical approaches to the analysis of differential enrichment.