Inversion characterization in Timema stick insects: from local PCA to pangenome approaches
- 1Utah State University
Abstract
Structural variants (SVs) can strongly influence evolutionary processes by suppressing recombination to create jointly inherited regions and by altering gene expression and the 3D organization of DNA. Recent improvements in sequencing technologies have led to an influx of high-resolution genomic data, bolstering research on SVs. When detecting and characterizing SVs, researchers face trade-offs in data and methods concerning scale, resolution, and computational intensity. Here, we evaluate such trade-offs by quantifying how data type, analytical approach, and SV attributes affect the ability to detect and characterize SVs in Timema cristinae stick insects, with a focus on inversions. We specifically evaluate inversions characterized by (i) a population genomic approach based on patterns of local population structure inferred from genotyping-by-sequencing data, (ii) pairwise alignments of eight de novo genome assemblies and SV calling with SyRI, and (iii) a pangenome constructed from the alignment of the eight genome assemblies. Our results suggest that while broad genomic regions harboring SVs can be detected by any of the approaches, the approaches differ in how they characterize inversions, especially small or complex ones. The local population structure approach is exploratory and generally only detects the largest inversions. Pairwise comparative alignments identify the greatest number of inversions, but downstream analyses are influenced by identification of shared inversions across genome pairs. Pangenome approaches are scalable and produce network graphs that describe complex SVs but require a conceptual shift in visualization and interpretation. The collective results highlight limitations and benefits of different approaches in a burgeoning research field.
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BibTeX
@misc{tataru2026inversion,
title = {{Inversion characterization in Timema stick insects: from local PCA to pangenome approaches}},
author = {D. Tataru and K. Vasquez-Kapit and P. Nosil and Z. Gompert},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.18.752789},
url = {https://www.biorxiv.org/content/10.64898/2026.09.18.752789v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Inversion characterization in Timema stick insects: from local PCA to pangenome approaches
AU - D. Tataru
AU - K. Vasquez-Kapit
AU - P. Nosil
AU - Z. Gompert
PY - 2026
DA - 2026/09/18
PB - bioRxiv
DO - 10.64898/2026.09.18.752789
UR - https://www.biorxiv.org/content/10.64898/2026.09.18.752789v1
AB - Structural variants (SVs) can strongly influence evolutionary processes by suppressing recombination to create jointly inherited regions and by altering gene expression and the 3D organization of DNA. Recent improvements in sequencing technologies have led to an influx of high-resolution genomic data, bolstering research on SVs. When detecting and characterizing SVs, researchers face trade-offs in data and methods concerning scale, resolution, and computational intensity. Here, we evaluate such trade-offs by quantifying how data type, analytical approach, and SV attributes affect the ability to detect and characterize SVs in Timema cristinae stick insects, with a focus on inversions. We specifically evaluate inversions characterized by (i) a population genomic approach based on patterns of local population structure inferred from genotyping-by-sequencing data, (ii) pairwise alignments of eight de novo genome assemblies and SV calling with SyRI, and (iii) a pangenome constructed from the alignment of the eight genome assemblies. Our results suggest that while broad genomic regions harboring SVs can be detected by any of the approaches, the approaches differ in how they characterize inversions, especially small or complex ones. The local population structure approach is exploratory and generally only detects the largest inversions. Pairwise comparative alignments identify the greatest number of inversions, but downstream analyses are influenced by identification of shared inversions across genome pairs. Pangenome approaches are scalable and produce network graphs that describe complex SVs but require a conceptual shift in visualization and interpretation. The collective results highlight limitations and benefits of different approaches in a burgeoning research field.
ER -