RNA-seq and Next Generation Transcriptomics BIOL5177

  • Academic Session: 2019-20
  • School: School of Life Sciences
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

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

Timetable

The course will run in one of the three designated timetable blocks in Semester 2. Contact teaching for this course will take place over 5 weeks. Lecture/Tutorial sessions of 1-3 hours duration several times per week; computer practical sessions of 1-3 hours several times per week.

Requirements of Entry

None

Assessment

This course will be assessed by means of coursework (100%). The coursework will comprise a written assignment of 1500-2000 words (40%), a set exercise (1500-2000 words) based on computer practical work (40%) and a group presentation (20%).

Course Aims

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. 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.

Intended Learning Outcomes of Course

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

Knowledge and understanding:

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

- specialist integrative knowledge and critical understanding of the reasons for differences in the 
performance of different data analysis methods

Practice, general cognitive skills, and ICT:

- in a creative way, the principal skills involved in analysing next generation sequencing data using a range of transcriptomics tools

- in a creative way a variety of skills in building bioinformatic analysis pipelines related to RNA-seq applications

- in a creative way a variety of skills in constructing data analysis workflows coded in the statistical language R and their testing and execution in the R-studio environment

- the use of computer programs to visualize, annotate and interpret the results of a transcriptomics experiment

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

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

Minimum Requirement for Award of Credits

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