Evidence map›Paper›PMID 38384712›Full record

ArticleFrontiers in genetics2024

Revisiting genomes of non-model species with long reads yields new insights into their biology and evolution.

Nadège Guiglielmoni, Laura I Villegas, Joseph Kirangwa, Philipp H Schiffer

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.6field-weighted citation impact, top 11% of its field
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

8 citing papers in PubMed, 11 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Nadège GuiglielmoniInstitut für Zoologie, Universität zu Köln, Cologne, Germany.
Laura I VillegasInstitut für Zoologie, Universität zu Köln, Cologne, Germany.
Joseph KirangwaInstitut für Zoologie, Universität zu Köln, Cologne, Germany.
Philipp H SchifferInstitut für Zoologie, Universität zu Köln, Cologne, Germany.
University of Cologne · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-quality genomes obtained using long-read data allow not only for a better understanding of heterozygosity levels, repeat content, and more accurate gene annotation and prediction when compared to those obtained with short-read technologies, but also allow to understand haplotype divergence. Advances in long-read sequencing technologies in the last years have made it possible to produce such high-quality assemblies for non-model organisms. This allows us to revisit genomes, which have been problematic to scaffold to chromosome-scale with previous generations of data and assembly software. Nematoda, one of the most diverse and speciose animal phyla within metazoans, remains poorly studied, and many previously assembled genomes are fragmented. Using long reads obtained with Nanopore R10.4.1 and PacBio HiFi, we generated highly contiguous assemblies of a diploid nematode of the Mermithidae family, for which no closely related genomes are available to date, as well as a collapsed assembly and a phased assembly for a triploid nematode from the Panagrolaimidae family. Both genomes had been analysed before, but the fragmented assemblies had scaffold sizes comparable to the length of long reads prior to assembly. Our new assemblies illustrate how long-read technologies allow for a much better representation of species genomes. We are now able to conduct more accurate downstream assays based on more complete gene and transposable element predictions.

Indexed as

genome annotationgenome assemblygenomicslong readsNematoda

Identifiers

PMID38384712
PMCPMC10879605
OpenAlexW4391609743

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.