This virtual course, run by EMBL-EBI, will help you to gain an introduction to the technology, data analysis, tools, and resources used in RNA sequencing and transcriptomics. The content will provide a broad overview of the subject area, and introduce participants to basic analysis of transcriptomics data using the command line. It will also highlight key public data repositories and methodologies that can be used to start the biological interpretation of expression data. Topics will be delivered using a mixture of lectures, practical exercises, and open discussions. Computational work during the course will use small, example data-sets; and there will be no opportunity to analyse personal data.
Participants will learn via a mix of pre-recorded lectures, live presentations, and trainer Q&A sessions. Practical experience will be developed through group activities and trainer-led computational exercises. Live sessions will be delivered using Zoom with additional support and asynchronous communication via Slack.
Who is this course for?
This course is aimed at life science researchers, wet and/or dry lab, wanting to learn more about processing RNA-seq data and later downstream analysis. It will help those wanting a basic introduction to handling RNA-seq data, guiding them through several common approaches that can be applied to their own datasets. It features taught and practical sessions that cover how to interpret gene expression data and learn more about the biological significance of certain results.
Some experience with R and the linux-based command line is beneficial, but not essential. During the course some of the practicals will make use of a Linux-based command line interface, and R statistical packages. We recommend completing some basic tutorials on this topic in preparation for the upcoming course. There are many tutorials available online and here are some that may be of help:
Introduction to the Unix environment – https://swcarpentry.github.io/shell-novice/index.html
Introduction to R – https://swcarpentry.github.io/r-novice-inflammation/
What will I learn?
-Describe a variety of applications and workflow approaches for NGS technologies
-Apply bioinformatics software and tools to undertake analysis of RNA-seq data
-Evaluate the advantages and limitations of NGS analyses
-Interpret and annotate data with functional information using public resources
Course content
-High throughput sequencing technologies for RNA-Seq
-Basics of experimental design
-RNA-seq file formats
-RNA-seq bioinformatics workflow steps following sequence generation
-Methods for transcriptomics; QC, mapping, and visualisation tools
-Data resources to assist in the functional analysis and interpretation of transcriptomic data
-Introduction to de novo approaches (options in the absence of a reference)
-Fundamentals of pipeline implementation (with Nextflow) for bulk RNA-seq analysis
Data resources covered:
-Expression Atlas
-g:Profiler
-Sequencing repositories: ENA, GEO, SRA