Introduction to RNA-seq and ChIP-seq data analysis

The aim of this course is to familiarize the participants with the primary analysis of datasets generated through two popular high-throughout sequencing (HTS) assays: ChIP-seq and RNA-seq.

This course starts with a brief introduction to the transition from capillary to high-throughput sequencing (HTS) and discusses quality control issues, which are common among all HTS datasets.

Next, we will present the alignment step and how it differs between the two analysis workflows.

Finally, we focus on dataset specific downstream analysis, including peak calling and motif analysis for ChIP-seq and quantification of expression, transcriptome assembly and differential expression analysis for RNA-seq.

The timetable for this event can be found here.

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

• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Further details regarding eligibility criteria are available here
• Further details regarding the charging policy are available here

• Basic experience of command line UNIX
• Sufficient UNIX experience might be obtained from one of the many UNIX tutorials available online.
• Basic knowledge of the R syntax
• For a real beginner's introduction into R see here. More advanced R instructions can be found at Quick-R or An Introduction to R

Number of sessions: 2

After this course you should be able to:

• Understand the advantages and limitations of the high-throughput assays presented
• Assess the quality of your datasets
• Understand the difference between splice-aware and splice-unaware aligners
• Perform alignment and peak calling of ChIP-seq datasets
• Perform alignment, quantification of expression and guided transcriptome assembly of RNA-seq datasets

During this course you will learn about:

• High-throughput sequencing technology
• Quality control of raw reads: FASTQC and
• Considerations on experiment design for ChIP-seq and RNA-seq
• Read alignment to a reference genome: Bowtie and Tophat
• File format conversion and processing: UCSC tools and samtools
• Peak calling: MACS
• Motif analysis: MEME
• Quantification of expression and guided transcriptome assembly: Cufflinks
• Differential expression analysis: Cuffdiff

Presentations, demonstrations and practicals

2

A number of times per year