BAM, Populus-tremula, RNA-Seq, QC, Alignment


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


  • Lecture


Introduction to short-read alignments, including a general overview of existing methods (Burrow-Wheeler-Transform, Maximum Mappable Prefix, etc.) and some cautionary tales.


Understand the principles of short read alignments; Learn about the shortcomings of such methods; Learn about how these might affect downstream analyses


  • HTS-Introduction
  • R-programming
  • Unix

Target audience

From undergrade on, provided that the prerequisites above are fulfilled

Learning objectives

  • Learn the principles of short read alignment
  • Learn about the different mainstream approaches to short read alignment
  • Learn about the shortcomings and caveats of short read alignment
  • Learn how to apply short read aligners to one own's data


  • Lecture PDF and associated R examples in the corresponding folder


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


1h (lecture) + 2h (practical)

Content stability

Stable. The analysis has been conducted using the Populus trichocarpa sister species and might only get updated once the Populus tremula genome sequence and annotation are released, which may happen by the end of 2015.

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
  • FastQC
  • SortMeRna (>= 1.9)
  • Trimmomatic (>=0.32)
  • STAR (>=2.4.0)

Literature references

  • Robinson, Delhomme et al.
  • SortMeRna
  • Trimmomatic
  • STAR

Keywords: BAM, Populus-tremula, RNA-Seq, QC, Alignment

Additional information

Authors: Nicolas Delhomme @delhomme, Bastian Schiffthaler @bastian

Scientific topics: RNA-Seq