KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping

• DOI: 10.1093/bioinformatics/btac845
• Publication Date: 2022-12-29T13:26:47Z
• Authors: Wei Shen; Hongyan Xiang; Tianquan Huang; Hui Tang; Mingli Peng; Dachuan Cai; Peng Hu; Hong Ren
• Keywords: Profiling (computer programming); Benchmarking; MIT License; Bacterial genome size

Abstract Motivation The growing number of microbial reference genomes enables the improvement metagenomic profiling accuracy but also imposes greater requirements on indexing efficiency, database size and runtime taxonomic profilers. Additionally, most profilers focus mainly bacterial, archaeal fungal populations, while less attention is paid to viral communities. Results We present KMCP (K-mer-based Metagenomic Classification Profiling), a novel k-mer-based tool that utilizes genome coverage information by splitting into chunks stores k-mers in modified optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. combines k-mer similarity reduce false positive rate classification methods. Benchmarking results based simulated real data demonstrate KMCP, despite longer running time than all other methods, not only allows accurate prokaryotic populations provides more confident pathogen detection clinical samples low depth. Availability implementation software open-source under MIT license available at https://github.com/shenwei356/kmcp. Supplementary are Bioinformatics online.