Baits design and testing (in-silico and in-vivo) for nematodes couple to XGboost model for phylogeneomics and rapid genera assignment
Nematodes are highly diverse and found in nearly all environments, but their evolutionary relationships are hard to study due to their small size and simple features. Common genetic methods using single genes often lack the resolution needed for accurate classification. This study presents a new approach using ultraconserved elements (UCEs) to improve phylogenetic analysis in two nematode families: Panagrolaimidae and Rhabditidae. The method captures thousands of DNA regions without requiring full genome sequencing. In Panagrolaimidae, up to 1,457 UCEs were recovered, enabling robust evolutionary analysis and the reclassification of one strain. Machine learning was used to identify the most informative DNA regions for genus-level classification. For Rhabditidae, 46 key loci were sufficient; for Panagrolaimidae, 63 loci were identified, despite limited genomic data. This UCE-based method is a scalable, low-cost tool for improving nematode taxonomy and understanding their evolution, even with minimal sequencing.