Expression estimation

Expression estimation


GFF3, BAM, Populus-tremula, RNA-Seq, Expression-estimation


  • Nicolas Delhomme (@delhomme)
  • Bastian Schiffthaler (@bastian)


  • Lecture


This introduces how to summarise short read alignments by the annotation of interest to obtain a count-table; i.e. the structure necessary to most downstream expression based analyses. Here, the focus is put on gene-expression, but the aspects of transcript-expression are briefly addressed.


Understand how to combine alignments and annotation information to generate a count table; Learn about the common pitfalls of this process


  • HTS-Introduction
  • R-programming
  • Unix

Target audience

From undergrade on, provided that the prerequisites above are fulfilled

Learning objectives

  • Understand the principles of expression estimation
  • Learn how to create an expression count-table from a set of alignments and annotation
  • Learn about common pitfalls
  • Awake the awareness about expression estimation caveats and limitations and their influence on downstream analyses


  • Lecture PDF in the corresponding folder


  • The data availability is described in the Dataset section
  • and in the corresponding course


1h (lecture)

Content stability


Technical requirements

  • Best is to use our Docker (a self contained environment) based on the Bioconductor NGS Docker that can be used to setup the course machines (physical or in the cloud)
  • Otherwise:
  • a UNIX OS
  • R (>=3.1), Bioconductor(>=3.0)

Literature references

  • Robinson, Delhomme et al.
  • Bioconductor

Keywords: GFF3, BAM, Populus-tremula, RNA-Seq, Expression-estimation

Additional information

Authors: Nicolas Delhomme @delhomme, Bastian Schiffthaler @bastian

Scientific topics: RNA-Seq