This course will examine transcriptomic next generation sequence data analysis. Students will get hands-on experience of how such RNA-seq data, including bulk, single-cell and spatial, are generated, manipulated, and analysed both bioinformatically and statistically.

The course will run in one of the designated timetable blocks in Semester 2. Lecture/Tutorial sessions of 1-3 hours duration several times per week; computer practical sessions of 1-3 hours several times per week.

The assessment will comprise of two components:

■ a written assignment of 1500-2000 words (50%) [ILOs 1,2,3,5,6]

■ a set exercise of 200-250 lines of code (50%) [ILOs 3,4,5]

This course aims to equip students with a detailed understanding of the ways in which transcriptome data are generated and analysed by a version of next generation sequencing called RNA-seq, including bulk, single-cell and spatial transcriptomics. Students will get hands-on experience of carrying out such analyses using a variety of approaches suited to different experimental scenarios and will develop analytical skills, practical computing skills and the ability to interpret critically, and in the appropriate biological context, the output of statistical analyses of transcriptomics data.

By the end of this course, students will be able to demonstrate:

1. specialist integrative knowledge and critical appraisal of the concepts underlying next generation sequencing-based transcriptomics data analysis in a variety of experimental scenarios

2. that they can compare and critically assess the performance of different transcriptomics data analysis methods

3. the principal skills involved in analysing bulk, single-cell and spatial next generation sequencing transcriptomic data using a range of tools

4. key skills in building bioinformatic analysis pipelines related to RNA-seq applications, and in constructing data analysis workflows coded in the statistical language R and their testing and execution in the R-studio environment

5. the use of computer programs to visualize, annotate and interpret the results of a transcriptomics experiment effectively

6. that they can critically evaluate and effectively choose from a range of statistical approaches to transcriptomics data analysis and that they can interpret statistical data analysis results by mining annotation databases

Students must submit at least 75% by weight of the components of the course's summative assessment.