Kun-peng enables scalable and accurate pan-domain metagenomic classification

Abstract Comprehensive pan-domain metagenomic classification is increasingly constrained by the memory and runtime costs of building and querying the rapidly expanding reference genome space. We introduce Kun-peng, a taxonomic classifier powered by an intelligent block-partitioned database structure and optimized search strategies, enabling ultra-scalable, memory-efficient pan-domain profiling. Using the Critical Assessment of Metagenome Interpretation II benchmark, Kun-peng substantially reduces the memory usage of database-building and querying by up to 24-fold, and accelerates sample classification by up to 4.73-fold compared with Kraken2. Kun-peng achieves competitive accuracy with fewer false positives than Kraken2, Centrifuger, and even KrakenUniq, while maintaining consistently high sensitivity across diverse datasets. In a real-world evaluation of 586 metagenomic samples spanning air, water, soil, and human-associated environments, we performed classification using a 4.3 TB pan-domain database comprising 204,477 genomes, which was built by Kun-peng with only 4.1 GB peak memory. Kun-peng processed each sample in 0.2–11.2 min with 4.0–35.4 GB peak memory, corresponding to a 54–473-fold reduction in memory usage relative to Kraken2. Compared with Sylph, Kun-peng achieved up to a 46-fold speedup while requiring 21-fold less memory. Kun-peng classified 69.8%–94.3% of reads, improving coverage by 20%–60% over the standard Kraken2 database with 62,026 genomes. This improvement reflects expanded reference coverage, although a small fraction of false positives is inherent to k-mer-based methods. Overall, Kun-peng effectively eliminates the long-standing memory bottleneck in pan-domain database building and classification, enabling rapid and scalable pan-domain taxonomic analysis of complex environmental, ecological, and exposomic sequencing datasets.