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

ABSTRACT:

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 annota

SUBMITTER: Bradford LM 

PROVIDER: S-EPMC11613480 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature