Training

The aim of this course is to familiarise the participants with advanced data analysis methodologies and provide hands-on training on the latest analytical approaches. Lectures will give insight into how biological knowledge can be generated from High-throughput sequencing experiments and illustrate different ways of analyzing such data. Practicals will consist of computer exercises that will enable the participants to apply statistical methods to the analysis of sequencing data under the guidance of the lecturers and teaching assistants.

This course is aimed at advanced PhD students and post-doctoral researchers who are applying or planning to apply high throughput sequencing technologies and bioinformatics methods in their research. Familiarity with the technology and biological use cases of high throughput sequencing is required, as is some experience with R/Bioconductor.

During this course you will learn about:

• High-throughput sequencing technology
• Quality control of raw reads: FASTQC and fastx toolkit
• Considerations on experiment design for ChIP-seq and RNA-seq
• Read alignment to a reference genome: Bowtie and Tophat
• File format conversion and processing
• Peak calling: MACS, USeq and SISSR
• Motif analysis: MEME
• Quantification of expression and differential expression analysis and differential exon usage: R, DESeq, DEXSeq
• Small non-coding RNA analysis: R, Reaper

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
• Compare and apply appropriate short read aligners
• Perform alignment and peak calling of ChIP-seq datasets
• Perform alignment and quantification of expression for both small ncRNA and RNA-seq datasets
• Understand the challenges of single cell sequencing analysis