- Applications close: 29 October 2023
- Course dates: 12 – 16 February 2024
- Course fee: £225.00
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.
Virtual course
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.
Pre-recorded material may be provided before the course starts that participants will need to watch, read, or work through to gain the most out of the actual training event. In the week before the course, there will be a brief induction session. Computational practicals will run on EMBL-EBI's virtual training infrastructure, meaning participants will not require access to a powerful computer or install complex software on their own machines.
Participants will need to be available between the hours of 09:00 – 16:00 hrs GMT each day of the course. Trainers will be available to assist, answer questions, and provide further explanations during these times.
Who is this course for?
This course is aimed at life science researchers 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 is beneficial. 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.
Learning outcomes
After the course you should be able to:
- 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
During this course you will learn about:
- RNA-seq file formats and basics of experimental design
- 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
- Introduction to single-cell transcriptomics
- Data resources covered:
- Expression Atlas
- Single-cell Expression Atlas
- g:Profiler
- Reactome
Trainers
- Simon Andrews, Babraham Institute
- Patricia Carvajal Lopez, EMBL-EBI
- Victor Flores López, University of Cambridge
- Irene Papatheodorou, EMBL-EBI
bioinformaticswas not a good tag; every post on the forum is related to bioinformatics. I've addedRNA-seqas a tag. Feel free to edit the post and add more specific and relevant tags.