Snipe: highly sensitive pathogen detection from metagenomic sequencing data

Managing false positives during detection of pathogen sequences in shotgun metagenomics datasets

BACKGROUND

Culture-independent diagnostic tests are gaining popularity as tools for detecting pathogens in food. Shotgun sequencing holds substantial promise for food testing as it provides abundant information on microbial communities, but the challenge is in analyzing large and complex sequencing datasets with a high degree of both sensitivity and specificity. Falsely classifying sequencing reads as originating from pathogens can lead to unnecessary food recalls or production shutdowns, while low sensitivity resulting in false negatives could lead to preventable illness.

RESULTS

We used simulated and published shotgun sequencing datasets containing Salmonella-derived reads to explore the appearance and mitigation of false positive results using the popular taxonomic annotation softwares Kraken2 and Metaphlan4. Using default parameters, Kraken2 is sensitive but prone to false positives, while Metaphlan4 is more specific but unable to detect Salmonella at low abundance. We then developed a bioinformatic pipeline for identifying and removing reads falsely identified as Salmonella by Kraken2 while retaining high sensitivity. Carefully considering software parameters and database choices is essential to avoiding false positive sample calls. With well-chosen parameters plus additional steps to confirm the taxonomic origin of reads, it is possible to detect pathogens with very high specificity and sensitivity.

cgMSI: pathogen detection within species from nanopore metagenomic sequencing data

BACKGROUND

Metagenomic 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.

RESULTS

We 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 1× coverage.

CONCLUSIONS

We developed cgMSI for nanopore metagenomic pathogen detection within species. cgMSI is available at https://github.com/ZHU-XU-xmu/cgMSI .

Use of Whole Genome Sequencing by the Federal Interagency Collaboration for Genomics for Food and Feed Safety in the United States

ABSTRACT

This multiagency report developed by the Interagency Collaboration for Genomics for Food and Feed Safety provides an overview of the use of and transition to whole genome sequencing (WGS) technology for detection and characterization of pathogens transmitted commonly by food and for identification of their sources. We describe foodborne pathogen analysis, investigation, and harmonization efforts among the following federal agencies: National Institutes of Health; Department of Health and Human Services, Centers for Disease Control and Prevention (CDC) and U.S. Food and Drug Administration (FDA); and the U.S. Department of Agriculture, Food Safety and Inspection Service, Agricultural Research Service, and Animal and Plant Health Inspection Service. We describe single nucleotide polymorphism, core-genome, and whole genome multilocus sequence typing data analysis methods as used in the PulseNet (CDC) and GenomeTrakr (FDA) networks, underscoring the complementary nature of the results for linking genetically related foodborne pathogens during outbreak investigations while allowing flexibility to meet the specific needs of Interagency Collaboration partners. We highlight how we apply WGS to pathogen characterization (virulence and antimicrobial resistance profiles) and source attribution efforts and increase transparency by making the sequences and other data publicly available through the National Center for Biotechnology Information. We also highlight the impact of current trends in the use of culture-independent diagnostic tests for human diagnostic testing on analytical approaches related to food safety and what is next for the use of WGS in the area of food safety.

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