Introduction to RNA-seq Data Analysis


Date:

Thursday 29 - Friday 30 June 2017, 09:00-17:00

Venue:

The King's Buildings, The University of Edinburgh, Edinburgh, Scotland, UK

Application deadline:

Thursday 1 June 2017 noon

Cancellation deadline:

Thursday 22 June 2017 noon

Places:

12 (selection of applicants)

Registration fee:

£350 (includes refreshments and lunch)

Information:

Bert Overduin

Next date(s):

Autumn 2017


RNA sequencing (RNA-seq) is quickly becoming the method of choice for transcriptome profiling. Nevertheless, it is a non-trivial task to transform the vast amount of data obtained with high-throughput sequencers into useful information. Thus, RNA-seq data analysis is still a major bottleneck for most researchers in this field. The ability of correctly interpreting RNA-seq results, as well as knowledge on the intrinsic properties of these data, are essential to avoid incorrect experimental designs and the application of inappropriate analysis methodologies. The aim of this workshop is to familiarise researchers with RNA-seq data and to initiate them in the analysis by providing lectures and practicals on analysis methodologies. In the practicals Illumina-generated sequencing data and various widely used software programs will be used.


"Loved it! I'm now itching to get my hands on my RNA-seq data and analyse it myself." (November 2014)


Instructors

Edinburgh Genomics staff

Workshop format

The workshop consists of presentations and hands-on tutorials.

Who should attend

Graduates, postgraduates, and PIs, who are using, or planning to use, RNA-seq technology in their research and want to learn how to process and analyse RNA-seq data.

Requirements

A general understanding of molecular biology and genomics.
A working knowledge of Linux at the level of the Edinburgh Genomics Linux for Genomics workshop.


Covered topics (and used software)

Introduction to Next Generation Sequencing
Quality control and data pre-processing (FastQC, cutadapt)
Mapping to a reference genome (STAR)
Mapping quality control (SAMtools, Picard, RSeQC)
Visualisation of mapped reads (IGV)
Estimating gene count (featureCount)
Introduction to R
Differential expression analysis (edgeR)
Functional analysis (GSEA)