Challenging a bioinformatic tool's ability to detect microbial contaminants using in silico whole genome sequencing data.

ABSTRACT: High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated W

SUBMITTER: Olson ND 

PROVIDER: S-EPMC5600177 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature