Evidence map›Paper›PMID 37321987›Full record

ArticleNature communications2023

grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis.

Teresa Rummel, Lygeri Sakellaridi, Florian Erhard

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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  9. bioRxiv : the preprint server for biology · 2025
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  15. mCell · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Teresa Rummel *Institute for Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany.ORCID 0000-0002-9434-0836
Lygeri Sakellaridi *Institute for Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany.ORCID 0000-0002-6109-1626
Florian ErhardInstitute for Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany. Florian.Erhard@informatik.uni-regensburg.de.ORCID 0000-0002-3574-6983

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

Indexed as

Gene Expression ProfilingSoftwareBayes TheoremNucleotidesRNARNA-SeqSequence Analysis, RNANucleotidesRNA

Identifiers

PMID37321987
PMCPMC10272207

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.