Evidence map›Paper›PMID 41258425›Full record

ArticleScientific reports2025

Comprehensive detection of structural variations in long and short reads dataset of French cattle.

Maulana Mughitz Naji, Christophe Klopp, Camille Eché, Arnaud Di Franco, Clément Birbes, Camille Marcuzzo, Amandine Suin, Carole Iampietro, Claire Kuchly, Caroline Vernette and 9 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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. Review
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

19 authors.

Maulana Mughitz NajiUniversité Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy en Josas, France. maulana-mughitz.naji@inrae.fr.
Christophe KloppUniversité de Toulouse, INRAE, MIAT, Sigenae, BioinfOmics, 31326, Castanet-Tolosan, France.
Camille EchéINRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France.
Arnaud Di FrancoUniversité de Toulouse, INRAE, MIAT, Sigenae, BioinfOmics, 31326, Castanet-Tolosan, France.
Clément BirbesUniversité de Toulouse, INRAE, BioinfOmics, GenoToul Bioinformatics Facility, 31320, Castanet-Tolosan, France.
Camille MarcuzzoINRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France.
Amandine SuinINRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France.
Carole IampietroINRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France.
Claire KuchlyINRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France.
Caroline VernetteINRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France.
Sébastien FritzUniversité Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy en Josas, France.
Cécile GrohsUniversité Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy en Josas, France.
Thomas FarautGenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet-Tolosan, France.
Christine GaspinUniversité de Toulouse, INRAE, BioinfOmics, GenoToul Bioinformatics Facility, 31320, Castanet-Tolosan, France.
Denis MilanINRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France.
Cécile DonnadieuINRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France.
Didier BoichardUniversité Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy en Josas, France.
Marie-Pierre SanchezUniversité Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy en Josas, France.
Mekki BoussahaUniversité Paris Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy en Josas, France.

Funding

CARNOT France Future Elevage CASCAD
6 · The paper itself

Abstract

Structural variants (SVs) correspond to different types of genomic variants larger than 50 bp. Many findings suggest the use of long-read (LR) rather than short-read (SR) sequencing to improve the accuracy of SVs detection. Here, we present the results of an in-depth analysis for detection of SVs, mainly large insertions and deletions, in 14 French bovine breeds, based on whole-genome sequence (WGS) data comprising 176 LR and 571 SR samples, with 154 individuals having both LR and SR data available. We first investigated possible biases on the performances of well-known SVs detection tools, namely CUTESV, PBSV, and SNIFFLES, using LR from different technologies, including PacBio HiFi, Oxford ONT, and PacBio CLR. We subsequently highlighted the abilities of tools for detecting SVs (DELLY, LUMPY, and MANTA) and for genotyping known SVs (GRAPHTYPER, SVTYPER, PARAGRAPH, and VG toolkit) using SR data. We then show how the incremental composition of samples in the reference panel affected the SVs genotyping for six validation individuals sequenced in SR. We then searched for the optimal parameters and created the final SVs reference panel consisting of 25,191 deletions and 30,118 insertions. Finally, we emphasized the landscape of the genotyped SVs segregating across 571 SR individuals of 14 breeds.

Indexed as

Genomic Structural VariationAnimalsCattleFranceGenotypeWhole Genome Sequencing

Identifiers

PMID41258425
PMCPMC12630617

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.