Literature DB >> 29028260

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

Alexey Uvarovskii1,2, Christoph Dieterich1,2.   

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: a.uvarovskii@uni-heidelberg.de or christoph.dieterich@uni-heidelberg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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Year:  2017        PMID: 29028260     DOI: 10.1093/bioinformatics/btx368

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  7 in total

1.  Dynamics of transcriptional and post-transcriptional regulation.

Authors:  Mattia Furlan; Stefano de Pretis; Mattia Pelizzola
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

2.  Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic labeling experiment.

Authors:  Micha Hersch; Adriano Biasini; Ana C Marques; Sven Bergmann
Journal:  BMC Bioinformatics       Date:  2022-04-22       Impact factor: 3.307

3.  A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover.

Authors:  Etienne Boileau; Janine Altmüller; Isabel S Naarmann-de Vries; Christoph Dieterich
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

4.  pulseTD: RNA life cycle dynamics analysis based on pulse model of 4sU-seq time course sequencing data.

Authors:  Xin Wang; Siyu He; Jian Li; Jun Wang; Chengyi Wang; Mingwei Wang; Danni He; Xingfeng Lv; Qiuyan Zhong; Hongjiu Wang; Zhenzhen Wang
Journal:  PeerJ       Date:  2020-07-08       Impact factor: 2.984

5.  Dissecting newly transcribed and old RNA using GRAND-SLAM.

Authors:  Christopher Jürges; Lars Dölken; Florian Erhard
Journal:  Bioinformatics       Date:  2018-07-01       Impact factor: 6.937

6.  On the optimal design of metabolic RNA labeling experiments.

Authors:  Alexey Uvarovskii; Isabel S Naarmann-de Vries; Christoph Dieterich
Journal:  PLoS Comput Biol       Date:  2019-08-07       Impact factor: 4.475

7.  Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling.

Authors:  Stefano de Pretis; Mattia Pelizzola; Mattia Furlan; Eugenia Galeota; Nunzio Del Gaudio; Erik Dassi; Michele Caselle
Journal:  Genome Res       Date:  2020-09-25       Impact factor: 9.043

  7 in total

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