Universität zu Köln

Villegas, Laura ORCID: 0000-0002-0906-8373, Jimenez, Lucy ORCID: 0000-0002-2176-0450, van der Sprong, Joëlle, Holovachov, Oleksandr ORCID: 0000-0002-4285-0754, Waldvogel, Ann‐Marie and Schiffer, Philipp H. ORCID: 0000-0001-6776-0934 (2025). Ultraconserved Elements and Machine Learning Classifiers Enable Robust Phylogenetics and Taxonomy in Model and Non‐Model Nematodes. Molecular Ecology Resources, 25 (8). pp. 1-15. Wiley. ISSN 1755-098X

PDF Molecular Ecology Resources - 2025 - Villegas - Ultraconserved Elements and Machine Learning Classifiers Enable Robust.pdf Bereitstellung unter der CC-Lizenz: Creative Commons Attribution. Download (2MB)

Abstract

[Artikel-Nr.: e70046] Nematodes are among the most diverse animals, yet only around 28,000 of an estimated one million species have been morpho- logically described. Their small size, morphological simplicity, and cryptic diversity complicate phylogenetic analyses. Traditional morphological and single-locus molecular approaches often lack resolution for both recent and ancient divergences. To address these limitations, we developed the first ultraconserved elements (UCEs) probe sets for two nematode families: Panagrolaimidae, a group of non-model organisms with limited genomic resources when compared to model taxa, and Rhabditidae, which includes the model species Caenorhabditis elegans. Our probe sets targeted 1612 loci for Panagrolaimidae and 100,397 for Rhabditidae. In vitro testing recovered up to 1457 loci in Panagrolaimidae, supporting robust phylogenetic reconstruction. Results were largely consistent with previous analyses, except for one strain reclassified as Neocephalobus halophilus BSS8. Using machine learning, we determined the minimum number of loci needed for accurate genus-level classification. For Rhabditidae, XGBoost achieved high accuracy with just 46 loci. For Panagrolaimidae, 39 loci were most informative. Our UCE-based approach offers a scalable and cost-effective framework for phylogenomics, enhancing taxonomic resolution and evolutionary inference in nematodes. It is well suited for biodiversity assessments and shallow, field-based sequencing, expanding research possibilities across this eco- logically important phylum.

Item Type:	Article
Creators:	Creators Email ORCID ORCID Put Code Villegas, Laura UNSPECIFIED https://orcid.org/0000-0002-0906-8373 UNSPECIFIED Jimenez, Lucy UNSPECIFIED https://orcid.org/0000-0002-2176-0450 UNSPECIFIED van der Sprong, Joëlle UNSPECIFIED UNSPECIFIED UNSPECIFIED Holovachov, Oleksandr UNSPECIFIED https://orcid.org/0000-0002-4285-0754 UNSPECIFIED Waldvogel, Ann‐Marie UNSPECIFIED UNSPECIFIED UNSPECIFIED Schiffer, Philipp H. UNSPECIFIED https://orcid.org/0000-0001-6776-0934 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-811094
Identification Number:	10.1111/1755-0998.70046
Journal or Publication Title:	Molecular Ecology Resources
Volume:	25
Number:	8
Page Range:	pp. 1-15
Number of Pages:	15
Date:	8 November 2025
Publisher:	Wiley
ISSN:	1755-098X
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Biology > Zoologisches Institut
Subjects:	Life sciences
Uncontrolled Keywords:	Keywords Language genus classification ; machine learning ; Nematoda ; Phylogenomics ; ultraconserved element English
['eprint_fieldname_oa_funders' not defined]:	Publikationsfonds UzK
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/81109