Balanced DNA interpolation improves learning of genetic distance-informed embeddings in plants

In taxonomic research, traditional phylogenetic tree- and structure-based analyses of genetic data are increasingly complemented by machine-learning-based identification and representation learning. Although the amount of DNA data needed to train state-of-the-art machine learning models often exceeds what can realistically be collected and sequenced in biological studies, the number of samples can be extended artificially through data augmentation. Genetic data augmentation usually refers to the introduction of random base variations, translocations, and reverse complementing. These augmentations do not take into account the inherent structures of populations and species, potentially blurring the lines between entities within genetic datasets. Here, we propose DNAInterpolator, an approach based on interpolation of DNA sequences within a given dataset that presents a neighbor-guided alternative to random mutations. We tested interpolation as an augmentation technique using four flowering plant datasets and an artificial neural network trained to predict genetic distances between paired samples. To address unequally distributed distances within our training datasets, we examined the effect of balancing the distance distribution by curating interpolated sequences. We found that balancing helps models capture genetic distances across the full distance range by strengthening performance in underrepresented regions of the distribution. Our new approach leverages the potential of taxonomic DNA datasets for modern machine learning applications.Author summary: In recent years, interest in the study of genetic data using machine-learning-based approaches is increasing. However, for research focusing on fine-grained species classification or delimitation, limited genetic data exist. The finite amount of readily available fine-grained data and the challenges researchers face in material collection, e.g., narrow endemics or remote areas, constitute a significant issue when aiming to finetune state-of-the-art genome foundation models or train large models from scratch. Up-sampling genetic data through DNA augmentation methods has been shown to improve model performance. Typically, these augmentation methods introduce random transmutations, potentially blurring species boundaries and population structures inherent to a given dataset. We examined the potential of an offline augmentation technique that entails interpolating between samples of the same species to add semi-artificial DNA sequences to the dataset. Our approach is transparent, interpretable, and independent from encoding methods or subsequent analyses.