Evidence map›Paper›PMID 40048597›Full record

ArticleGenome biology and evolution2025

Comparing Small and Large Genomes Within Monogonont Rotifers.

Jonathon E Mohl, Patrick D Brown, Aaron J Robbins, Philip Lavretsky, Rick Hochberg, Robert L Wallace, Elizabeth J Walsh

Abstract readComparative Study
In one paragraph

Article in Genome biology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jonathon E MohlDepartment of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.ORCID 0000-0003-4440-4608
Patrick D BrownDepartment of Biological Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.ORCID 0000-0002-9562-788X
Aaron J RobbinsDepartment of Biological Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.ORCID 0000-0001-5730-269X
Philip LavretskyDepartment of Biological Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.ORCID 0000-0002-5904-8821
Rick HochbergDepartment of Biological Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA.ORCID 0000-0002-5567-5393
Robert L WallaceDepartment of Biology, Ripon College, Ripon, WI 54971, USA.ORCID 0000-0001-6305-4776
Elizabeth J WalshDepartment of Biological Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.ORCID 0000-0002-6719-6883

Funding

National Science Foundation DEB 2051704
6 · The paper itself

Abstract

Genome size is an important correlate of many biological features including body size, metabolic rate, and developmental rate and can vary due to a variety of mechanisms, including incorporation of repetitive elements, duplication events, or reduction due to selective constraints. Our ability to understand the causes of genome size variation is hampered by limited sampling of many nonmodel taxa, including monogonont rotifers. Here, we used high-throughput Nanopore sequencing and flow cytometry to estimate genome sizes of nine species of monogonont rotifers representing seven families, including three representatives of Superorder Gnesiotrocha. We annotated the genomes and classified the repetitive elements. We also compared genome size with two biological features: body size and metabolic rate. Body sizes were obtained from the literature and our estimates. Oxygen consumption was used as a proxy for metabolic rate and was determined using a respirometer. We obtained similar genome size estimates from genome assemblies and flow cytometry, which were positively correlated with body size and size-specific respiration rate. Importantly, we determined that genome size variation is not due to increased numbers of repetitive elements or large regions of duplication. Instead, we observed higher numbers of predicted proteins as genome size increased, but currently many have no known function. Our results substantially expand the taxonomic scope of available genomes for Rotifera and provide opportunities for addressing genetic mechanisms underlying evolutionary and ecological processes in the phylum.

Indexed as

Genome, HelminthGenome SizeRotiferaAnimalsBody SizePhylogenyRepetitive Sequences, Nucleic Acidbody sizegenome sizemetabolic raterepetitive elementsRotifera

Identifiers

PMID40048597
PMCPMC11954553

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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.