ChIP-seq and ATAC-seq analysis

PrerequisitesUpdated

The primary aim of this course is to familiarise participants with the analysis of ChIP-seq and ATAC-seq data and provide hands-on training on the latest analytical approaches.

The course starts with an introduction to ChIP-seq experiments for the detection of genome-wide DNA binding sites of transcription factors and other proteins. We first show data quality control and basic analytical steps such as alignment, peak calling and motif analysis, followed by practical examples on how to work with biological replicates and fundamental quality metrics for ChIP-seq datasets. On the second day, we then focus on the analysis of differential binding, comparing between different samples. We will also give an introduction to ATAC-seq data analysis for the detection of regions of open chromatin.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Please be aware that these courses are only free for registered University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here.
• Further details regarding eligibility criteria are available here

• Basic understanding of high-throughput sequencing technologies
• Basic experience of command line UNIX. Sufficient UNIX experience might be obtained from the following UNIX tutorial
• Basic knowledge of the R syntax. We recommend either attending Introduction to R for biologists, or working through the materials of the now discontinued An Introduction to Solving Biological Problems with R or Data Carpentry in R courses before attending this course.

Number of sessions: 2

Bioinformatics, Epigenomics, Functional genomics, Data visualisation, Data handing, Data mining, ChIP-seq

After this course you should be able to:

• Understand quality of high-throughput sequencing data
• Assess the quality of your ChIP-seq datasets and reproducibility of replicates
• Perform alignment and peak calling of ChIP-seq datasets
• Compare samples by performing differential binding analysis
• Detect regions of open chromatin by analysing ATAC-seq datasets
• Visualize multiple layers of epigenomic data

During this course you will learn about:

• Considerations on experimental design for ChIP-seq
• Quality control of raw reads
• Read alignment to a reference genome
• Peak calling and motif analysis
• Differential binding analysis
• Quality control, processing and analysis of ATAC-seq data

Presentations, demonstrations and practicals

• Free for registered University of Cambridge students
• £ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
• It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
• £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
• £ 100/day for all Industry participants. These charges must be paid at registration
• Further details regarding the charging policy are available here

2

2 times a year

• Introduction to R for Biologists (ONLINE LIVE TRAINING)
• CRUK: Data Carpentry in R
• Bulk RNA-seq analysis (IN-PERSON)
• Single-cell RNA-seq analysis (ONLINE LIVE TRAINING)
• Analysis of DNA Methylation using Sequencing (IN-PERSON)