Evaluating Reference-Independent Pipelines for the Detection of Spreading Organisms in Metagenomic Datasets
The emergence of unidentified pathogens, or "Disease X," poses a significant threat to global health, necessitating the development of proactive surveillance strategies for the wildlife and human virosphere. Since novel viruses often lack universal genetic markers or known homologs, this study evaluates four reference-independent computational pipelines: coverage-based, k-mer-based, nucleotide clustering, and Large Language Model (LLM)-based designed to detect spreading organisms by comparing distinct metagenomic datasets. Using a real-world pandemic dataset of human nasopharyngeal RNA-seq runs and a semi-synthetic dataset enriched with divergent Egovirales sequences, we measured the sensitivity, selectivity, and computational efficiency of each approach. The coverage-based method proved most robust, consistently achieving 100% genome coverage of SARS-CoV-2 and maintaining high selectivity even at low viral concentrations, though it required extensive computational resources (20 days of CPU time for 2B reads). In contrast, the k-mer-based approach offered a tenfold reduction in execution time and high selectivity but was sensitive to data depletion, failing to detect targets at very low abundances. The clustering-based pipeline performed effectively at moderate concentrations but suffered from sequence fragmentation in sparse data, while the LLM-based method (using ViraLM), despite its efficiency, exhibited critically low selectivity due to current latent space partitioning limitations. These results demonstrate that while k-mer and LLM-based tools provide rapid screening capabilities, the coverage-based approach remains the most reliable for sensitive pathogen discovery. Ultimately, these reference-independent workflows are essential for illuminating metagenomic "dark matter" and establishing early warning systems for emerging infectious diseases