Evidence map›Paper›PMID 41566196›Full record

ArticleBMC bioinformatics2026

CRESCENT, a comprehensive RNA-Seq expression, splicing, and coding/non-coding element network tool.

Gilles Sireta, Gwendal Cueff, Vincent Darbot, Marie Lefebvre, Simon Amiard, Aline V Probst, Christophe Tatout

Abstract read
In one paragraph

Article in BMC bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Gilles SiretaUniversité Clermont Auvergne, CNRS, INSERM, iGReD Clermont-Ferrand, Clermont-Ferrand, France.
Gwendal CueffUMR 1019, INRAE, Unité de Nutrition Humaine, Université Clermont Auvergne, 63000, Clermont-Ferrand, France.
Vincent DarbotAviwell SAS, TWB Campus, 31077, Toulouse, France.
Marie LefebvreUniversité Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France.
Simon AmiardUniversité Clermont Auvergne, CNRS, INSERM, iGReD Clermont-Ferrand, Clermont-Ferrand, France.
Aline V ProbstUniversité Clermont Auvergne, CNRS, INSERM, iGReD Clermont-Ferrand, Clermont-Ferrand, France.
Christophe TatoutUniversité Clermont Auvergne, CNRS, INSERM, iGReD Clermont-Ferrand, Clermont-Ferrand, France. christophe.tatout@uca.fr.

Funding

Agence Nationale de la Recherche 16IDEX0001 CAP 2025 CIR 1
6 · The paper itself

Abstract

backgroundTraditional short-read RNA-Seq analysis pipelines predominantly focus on protein-coding genes, often overlooking other genomic sequences such as transposable elements (TEs) and non-coding RNA dynamics and do not usually investigate splicing events or transcript usage. To fully capture the complexity of the transcriptome, and in particular transcriptomic regulation, it is crucial to adopt a comprehensive approach that integrates these diverse aspects, providing a more complete and nuanced understanding of expression dynamics in the studied organism.

resultsTo address these limitations, we present CRESCENT (Comprehensive RNA-seq Expression, Splicing, and Coding/non-coding Element Network Tool), a Snakemake workflow capable of performing a fully automated and comprehensive RNA-Seq analysis. CRESCENT integrates multiple tools at each step of the workflow and enables analysis of differential expression, differential alternative splicing, differential transcript usage, and gene ontology-based functional enrichment for all three. The workflow takes advantage of multiple Snakemake wrappers to minimize required installations for the user, integrating the latest versions of popular bioinformatic tools. It can be run for a complete analysis or for only a specific part in accordance with the configuration file provided by the user. The CRESCENT workflow was validated, demonstrating the pipeline's reliability, as differentially expressed protein-coding genes, TEs and differential alternative splicing events were consistent with previously published datasets. Finally, benchmarking CRESCENT performance indicated that it can be run on a personal computer or a remote server, including a high-performance computing cluster, allowing a user to process small single-end sequencing on species possessing a small genome like Arabidopsis thaliana to very large paired-end sequencing on polyploid species like wheat. CONCLUSION AND AVAILABILITY: CRESCENT is a scalable solution for comprehensive transcriptomic profiling. It is freely available at https://github.com/gilless429/crescent .

Indexed as

RNA-SeqRNA SplicingSequence Analysis, RNASoftwareAlternative SplicingComputational BiologyGene Expression ProfilingTranscriptomeAlternative splicingNon-coding RNAProtein coding genesRNA-Seq workflowSnakemakeTransposable elements

Identifiers

PMID41566196
PMCPMC12895761

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.