RNA-Seq Blog Transcriptome Research & Industry News
Date: February 10th-12th, 2016
Location: Room PC95 (first floor), RADIX building (107), Wageningen Campus, Droevendaalsesteeg 1, Wageningen, the Netherlands
Directions: directions (check also Map Wageningen UR Campus)
Language: English
Group size: maximum of 35 participants
Credits: 0.8 ECTS
Registration and Fees: To register for this course, please use the Registration Form.
- € 75,– for EPS PhD candidates with approved iTSP and registered EPS post-doctoral fellows
- € 125,– for other PhD candidates, other postdocs and EPS staff members
- € 150,– other academia or researchers at non-profit organisations
- € 200,– researchers at profit organisations / industry
Fees do include coffee, tea, lunches,etc.
Keywords: RNA-seq, transcriptome analysis, read mapping, quality control, differential expression, clustering, co-expression, GO enrichment
Organisers: Harm Nijveen and Dick de Ridder (Bioinformatics – Wageningen UR)
Contact: Douwe Zuidema (EPS)
Description: Bioinformatics Group of Wageningen UR and the graduate school Experimental Plant Sciences (EPS) are organising an NGS application course on RNA-seq. This is a 2.5-day course that will consist of lectures in the morning and extensive hands-on computer practicals in the afternoon.
Researchers’ questions are the driving force of our programme:
- Which questions can be addressed with RNA-seq?
- How many samples and replicates do I need?
- Which steps are involved in an RNA-seq experiment?
- What is differential expression?
- How do I interpret a list of genes?
Instructors: Harm Nijveen, Dick de Ridder
Guest speakers: Wilco Ligterink, Basten Snoek and Richard Immink
Target audience: This beginner’s course is intended for (post) graduate researchers who want to start applying RNA-seq analysis methods on their data. The course is intended for people with a basic understanding of (NGS) data analysis. Previous experience with NGS or RNA-seq data analysis, R or Galaxy is not required. We do expect a reasonable computer literacy and a basic knowledge of biology, DNA-technology and statistics. Topics and examples used in this course will be taken from the ‘agri-domain’, but methods and theory are all generally applicable.
Detailed description: This 2.5-day course will cover general aspects of RNA-seq during lectures on NGS & RNA-seq theory, but also the context, applicability, power and expected results of RNA-seq experiments. During the practicals, you will learn the basic steps an RNA-seq pipeline consists of, how to interpret your data and to put the results to use in your research project. We will use Galaxy, R and webtools. Each day will be concluded with a presentation by one of the guest speakers, describing the application of RNA-seq in their research projects.
– Day 1 consists of an introduction to RNA-seq and NGS techniques in general, followed by specialised lectures on quality control of the raw data and mapping RNA-seq reads on a reference genome. In the practicals, the Galaxy web platform will be introduced and used for tutorials on quality control and the first steps of an RNA-seq data-analysis pipeline.
– Day 2 will continue with lectures on transcriptome assembly, annotation, differential gene expression and using clustering methods to find co-expressed genes. The statistical language ‘R’ will be introduced and RNA-seq data are analysed using both Galaxy and R.
– Day 3 will start with a lecture on function enrichment analysis, followed by a practical on gene ontology enrichment. The last lecture of the course will explore approaches to derive predictive models from RNA-seq data.
Topics: Sequencing requirements, Biological applications, An RNA-seq data analysis pipeline (quality control, mapping, identification, quantification), Differential expression, Clustering, Enrichment analysis and Use cases.
Practical Exercises: General introductions in R and Galaxy, Data quality control (Galaxy), Data quality control in a statistical perspective (R), RNA-seq data analysis pipeline (R), RNA-seq data analysis pipeline (Galaxy), Clustering and GO enrichment (R).
