Chou, M.-H. | bioRxiv Science

bioRxiv2026

Three annotated tiger beetle genomes (Coleoptera, Adephaga, Cicindelidae)

Advances and accessibility to next-generation technology provide opportunities to sequence non-model organisms. Despite this increase in whole-genome sequencing data, annotation and the production of reference genomes remain limited. Reference genomes are a critical tool for a variety of studies in evolutionary biology, functional genomics, and conservation genetics. Tiger beetles (Cicindelidae) are a diverse and globally distributed family of beetles that serve as bioindicator taxa and flagship species for insect conservation. Here, we report highly complete, contiguous, and annotated genome assemblies representing draft reference genomes for three species of tiger beetle spanning the phylogeny. These draft reference genomes are for Audouins night-stalking tiger beetle, Omus audouini; the montane giant tiger beetle, Amblycheila baroni; and the western red-bellied tiger beetle, Cicindelidia sedecimpunctata. Article SummaryTiger beetles are a charismatic group with [~]3000 species distributed globally. Despite their popularity among insect enthusiasts and their role as bioindicators of ecosystem health, the group currently lacks a reference genome. This article outlines genome assembly and annotation for three tiger beetle species that span evolutionary relationships within the lineage. Quality control analyses show that the assemblies are reference quality and demonstrate high contiguity, completeness, and accuracy. The resulting draft genome annotations will be a valuable resource for scientific endeavors and allow for continued research on tiger beetles and their allies.

genomics↗

bioRxiv2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impacts on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and the data quality, dominate detection performance. Increases in spectral noises and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol%. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying a ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

bioinformatics↗