High-Accuracy, Ultrafast DNA Barcode Identification via Statistical Sketching and Approximate Nearest Neighbor Search

Abstract

High-throughput DNA barcoding, a cornerstone of modern biodiversity and environmental genomics, is critically limited by the computational cost of traditional, alignment-based identification methods. While faster alignment-free approaches have been proposed, first-generation techniques based on k-mer hashing are fundamentally unreliable due to their inherent sensitivity to insertions and deletions (indels), a common form of sequence variation. Here, we introduce DNA-Sketch, a novel alignment-free framework that overcomes this limitation. DNA-Sketch transforms a DNA sequence into a robust statistical fingerprint by vectorizing its binned dinucleotide frequencies. These high-dimensional "sketches" are then indexed for ultrafast similarity search using an Approximate Nearest Neighbor (ANN) library. We benchmarked a single-pass sketch and a "Multi-Sketch Ensemble" against the state-of-the-art aligner VSEARCH on a large, challenging benchmark simulating real-world noise and intra-species variation. The Multi-Sketch Ensemble achieved 100% accuracy, perfectly matching VSEARCH, while delivering a 31-fold speed improvement. The single-pass sketch achieved 99.98% accuracy with a 95-fold speedup. DNA-Sketch resolves the classic speed-versus-accuracy trade-off, demonstrating that by pairing robust feature extraction with high-performance ANN indexing, it is possible to achieve the accuracy of gold-standard alignment at a fraction of the computational cost, providing a powerful and highly scalable solution for modern bioinformatics.