Taxonomic classification of metagenomic sequencing reads is a crucial task in metagenome analysis, with significant implications for research on health, diet, and drug responses. With the advancement of sequencing technologies, several methods have been developed for long-read taxonomic classification, including Kraken2, Centrifuge, and CLARK. While these classifiers achieve high precision, they often suffer from low sensitivity, leaving a substantial proportion of reads unclassified, especially for highly diverse and rapidly evolving organisms such as viruses. ClassGraph addressed this challenge by introducing a label propagation strategy, significantly improving sensitivity at the cost of precision.To further address this issue, we propose NCRP, a Neighborhood-Consistency Refinement and Propagation algorithm, designed to enhance both precision and sensitivity in long-read classification. NCRP builds upon an initial classifier and a read overlap graph. It first identifies and removes ambiguous classifications that are inconsistent with neighboring reads in the overlap graph, then propagates confident taxonomic assignments from classified reads to unclassified ones. Evaluation on simulated and mock datasets demonstrates that NCRP consistently outperforms existing classifiers in both precision and sensitivity, achieving the highest F1 score across all tests. We further benchmarked the ability of taxonomic classifiers to detect microbial species. NCRP combined with Kraken2 or Centrifuge achieved the best performance, whereas CLARK showed relatively poor performance on this task.NCRP is open-source and can be accessed at https://github.com/SDU-ACG-Lab/NCRP.

Co-authors: Xun Ding, Haitao Jiang, Zhu Daming