RNA-seq data analysis: from raw reads to gene expression
Course details
In this 18-hour course, we will cover all the steps included in an RNA-seq study. We will introduce participants to the analyses involved in generating RNA-seq data, from sampling design to RNA extraction, library preparation, and sequencing. The course will then cover all the steps involved in the bioinformatic analysis, from raw data quality control, to mapping reads against a reference genome to generate RNA-seq gene counts. We will then focus on gene expression quantification and differential expression analyses. Lastly, the resulting list of significantly differentially expressed genes will be used to perform the annotation and the functional enrichment tests.
During the course we will discuss the different data formats and tools commonly used in every step of the process. Participants will also learn how to assemble a transcriptome de novo in order to work with organisms with limited sequence data available in public databases.
By the end of the course, participants will be able to design an RNA-seq gene expression project, as well as conducting the bioinformatic analyses required to visualise and interpret the results.
The course will combine theory with hands-on exercises using real datasets. We will offer consultation on the participant’s own data and results during the course.
- Language: Spanish.
- Number of places available: 18.
- Instructors: Dr Alejandra Perina, Dr Fátima Sánchez-Barreiro, and Antón Vizcaíno.
Target audience and requirements
Master's students, early career researchers (pre-docs and post-docs), or senior researchers, either from Industry or Academia, interested in RNA-seq. Please note that this is an introductory course and is not intended for researchers with experience in RNA-seq data analysis.
Basic skills in high-throughput sequencing are required. Researchers who are not familiar with high-throughput sequencing might want to consider signing up first to our introductory course.
Basic use of UNIX-based command line is desirable. Hands-on work will be performed in UNIX environments (Linux, Mac).
All participants should bring and use their own laptops. Software packages and datasets used during the course will be provided to the participants via a virtual machine, that will be installed before the beginning of the course.
An attendance certificate will be provided at the end of the course.