This course provides comprehensive end-to-end training on how to perform and analyse data from long-read single-cell RNA-seq experiments.
Consisting of a mixture of conceptual and methodological lectures and hands-on bioinformatics sessions, we will provide an overview of the current long-read methods for single-cell experiments, data processing, through to data interpretation and presentation - including best practice and tips as learned first-hand by Earlham Institute’s faculty.
What will I learn?
- Detailed overview of single-cell long-read sequencing technologies
- Optimal method selection across diverse experimental applications
- Best practices in experimental design and data management
- End-to-end workflow for generating, processing, and interpreting long-read RNA-seq data - specifically in the context of single-cell experiments, including:
- Quality control,
- Mapping,
- Annotation,
- Quantification,
- Dataset Integration,
- Single Cell Clustering and Annotation,
- Data Visualisation.
Who is this training for?
Researchers who are new to and are involved in design or delivery of project(s) centred around single-cell long-read sequencing. We particularly encourage those in the grant application or experimental design stages.
Course prerequisites
Basic experience with the programming language R: such as through completion of activities as they appear in Software Carpentry lesson(s): ‘Programming with R’ and ‘R for Reproducible Scientific Analysis’.
Basic experience in using the command line interface: such as through completion of activities as they appear in the Software Carpentry lesson ‘The Unix Shell’.
Familiarity with fundamental RNA-seq concepts: such as through completion of activities as they appear in the Data Camp ‘RNA-seq with Bioconductor in R’ course, chapter ‘Introduction to RNA-Seq theory and workflow’ .
Once accepted, participants will be granted access to our pre-requisite module to aid their progress in the course.
