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Analysis of small RNA-seq data


This course focuses on methods for the analysis of small non-coding RNA data obtained from high-throughput sequencing (HTS) applications (small RNA-seq). During the course, approaches to the investigation of all classes of small non-coding RNAs will be presented, in all organisms.

Day 1 will focus on the analysis of microRNAs and day 2 will cover the analysis of other types of small RNAs, including Piwi-interacting (piRNA), small interfering (siRNA), small nucleolar (snoRNA) and tRNA-derived (tsRNA).



Anton Enright, EBI

Tommaso Leonardi, Gurdon Institute

Matthew Davis, EBI

Stijin van Dongen, EBI

Tomas Di Domenico, Gurdon InstituteToma


Audience and Prerequisites

  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals


Syllabus, Tools and Resources

During this course you will learn about:

  • Analysis of small RNA-seq data (QC, trimming, mapping)
  • Appropriate statistical techniques for HTS data analysis
  • Prediction of miRNA binding sites in genome-wide data sets
  • Identification of other classes of small RNAs (piRNAs, siRNAs, snoRNAs, tsRNAs)


Learning Objectives

After this course you should be able to:

  • Design a useful and statistically powerful small RNA-seq experiment
  • Understand the theory of count-based data analysis and differences for small RNA-seq
  • Perform a full analysis of a small RNA-seq dataset from raw data to stats
  • Predict microRNA targets for subsequent validation
  • Locate and characterise piRNA precursors
  • Identify genomic loci with high abundance of small RNAs



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