Genome-Wide k-Mer Signatures Identify Species of Anopheles gambiae Complex from Low-Coverage Sequencing

Species-level identification of mosquitoes within the Anopheles gambiae complex is essential for vector surveillance but remains challenging because several members are morphologically indistinguishable and share substantial genomic variation. Here, I evaluate varKoding, a genome-wide k-mer fingerprinting approach that classifies low-coverage sequencing data without genome assembly using 296 specimens representing seven species of the An. gambiae complex and two non-complex Anopheles species.Leave-one-out validation was performed across five sequencing data amounts (500K–10M bp) using varKode and conventional chaos game representation (CGR) images. varKode consistently outperformed CGR at every sequencing depth, with multi-class accuracy increasing from 95.27% at 500K bp to 98.65% at 10M bp. At 10M bp, six of nine taxa achieved 100% recall; An. gambiae s.s. and An. coluzzii each achieved 97.5% recall, while An. stephensi achieved 95.0%. Reciprocal gambiae–coluzzii errors were concentrated among specimens from Tiassalé, Côte d'Ivoire, including a pre-2013 specimen whose species label cannot be independently verified. An independent Ag3 analysis using geographically held-out An. gambiae and An. coluzzii specimens confirmed strong discrimination of near-pure individuals but showed that relative class scores did not reliably resolve fine-scale genomic ancestry within populations. Confidence scores generally separated correct from incorrect predictions. Classification remained correct for all six evaluable species at up to 20% simulated contamination across four biologically relevant non-target DNA backgrounds.Overall, varKoding provides high-accuracy identification from low-coverage data across the represented members of the complex, including reliable identification of the important malaria vector An. arabiensis and the non-vector An. quadriannulatus. Difficult or unexpected specimens can be directed to orthogonal molecular or population-genomic confirmation, allowing the method to serve as a practical first-pass surveillance tool. Broader geographic validation of the full multi-species classifier and expanded reference representation would further support routine surveillance applications.