Using metagenomic methods to detect organismal contaminants in microbial materials
- Published
- Accepted
- Subject Areas
- Bioinformatics, Genomics, Microbiology
- Keywords
- Genomic Purity, Whole Genome Sequencing, Bioinformatics, Biodetection, Microbial Materials, Reference Materials
- Copyright
- © 2017 Olson et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Using metagenomic methods to detect organismal contaminants in microbial materials. PeerJ Preprints 5:e2913v1 https://doi.org/10.7287/peerj.preprints.2913v1
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. Therefore, high sensitivity methods not requiring a priori assumptions about the contaminant are needed. We demonstrate the use of whole genome sequencing (WGS) and a metagenomic taxonomic classification algorithm for assessing the organismal purity of a microbial material. Using this proposed method we characterized the types of false positive contaminants reported and the dependence of detectable contaminant concentration on material and contaminant genome using simulated WGS data. Using the proposed method to characterize microbial material purity will help to ensure that the materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods are free of contaminants adversely impacting measurement results.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Supplemental Table 1
Organism name, NCBI taxonomic identifier and accession number of genomes and plasmid sequences, and sequence length used in the baseline assessment. The random seed used to generate the simulated reads is also provided.
Supplemental Table 2
Taxonomic and sequence accession information for representative genomes used for contaminant detection assessment.