pulseR: Versatile computational analysis of RNA turnover from metabolic labeling experiments

Bioinformatics. 2017 Oct 15;33(20):3305-3307. doi: 10.1093/bioinformatics/btx368.

Abstract

Motivation: Metabolic labelling of RNA is a well-established and powerful method to estimate RNA synthesis and decay rates. The pulseR R package simplifies the analysis of RNA-seq count data that emerge from corresponding pulse-chase experiments.

Results: The pulseR package provides a flexible interface and readily accommodates numerous different experimental designs. To our knowledge, it is the first publicly available software solution that models count data with the more appropriate negative-binomial model. Moreover, pulseR handles labelled and unlabelled spike-in sets in its workflow and accounts for potential labeling biases (e.g. number of uridine residues).

Availability and implementation: The pulseR package is freely available at https://github.com/dieterich-lab/pulseR under the GPLv3.0 licence.

Contact: [email protected] or [email protected].

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

  • Computational Biology / methods*
  • Gene Expression Profiling / methods*
  • Models, Biological
  • Models, Statistical
  • RNA / metabolism*
  • Sequence Analysis, RNA / methods*
  • Software*

Substances

  • RNA