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Bioinformatics. 2017 Jun 1;33(11):1735-1737. doi: 10.1093/bioinformatics/btx047.

Riborex: fast and flexible identification of differential translation from Ribo-seq data.

Author information

1
Molecular and Computational Biology, Division of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
2
Department of Cellular and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
3
Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.

Abstract

Motivation:

Global analysis of translation regulation has recently been enabled by the development of Ribosome Profiling, or Ribo-seq, technology. This approach provides maps of ribosome activity for each expressed gene in a given biological sample. Measurements of translation efficiency are generated when Ribo-seq data is analyzed in combination with matched RNA-seq gene expression profiles. Existing computational methods for identifying genes with differential translation across samples are based on sound principles, but require users to choose between accuracy and speed.

Results:

We present Riborex, a computational tool for mapping genome-wide differences in translation efficiency. Riborex shares a similar mathematical structure with existing methods, but has a simplified implementation. Riborex directly leverages established RNA-seq analysis frameworks for all parameter estimation, providing users with a choice among robust engines for these computations. The result is a method that is dramatically faster than available methods without sacrificing accuracy.

Availability and Implementation:

https://github.com/smithlabcode/riborex.

Contact:

andrewds@usc.edu.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28158331
PMCID:
PMC5860393
DOI:
10.1093/bioinformatics/btx047
[Indexed for MEDLINE]
Free PMC Article

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