cgMSI: pathogen detection within species from nanopore metagenomic sequencing data

BackgroundMetagenomic sequencing is an unbiased approach that can potentially detect all the known and unidentified strains in pathogen detection. Recently, nanopore sequencing has been emerging as a highly potential tool for rapid pathogen detection due to its fast turnaround time. However, identifying pathogen within species is nontrivial for nanopore sequencing data due to the high sequencing error rate.ResultsWe developed the core gene alleles metagenome strain identification (cgMSI) tool, which uses a two-stage maximum a posteriori probability estimation method to detect pathogens at strain level from nanopore metagenomic sequencing data at low computational cost. The cgMSI tool can accurately identify strains and estimate relative abundance at 1x coverage.ConclusionsWe developed cgMSI for nanopore metagenomic pathogen detection within species. cgMSI is available at https://github.com/ZHU-XU-xmu/cgMSI.

Automated summary

We developed the cgMSI tool for nanopore metagenomic pathogen detection within species. The tool utilizes a two-stage maximum a posteriori probability estimation method to accurately detect strains and estimate relative abundance at low computational cost.