Infectious Disease Metagenomics: Error Mitigation and Best Practices for the Clinical Routine Use of Metagenomic Sequencing

Nur A Hasan, Huai Li, Brian Fanelli, Arne Materna, Manoj Dadlani, Rita R Colwell
2019 Journal of Biomolecular Techniques  
Shotgun metagenomic sequencing is increasingly adopted by the biomedical community for clinical infection diagnosis and for surveillance applications. Benefits include a highly accurate, unbiased, and culture independent characterization of microbial communities. As a consequence, metagenomics is complementing traditional infectious disease tools, such as culture, AST and PCR. Despite its potential for clinical microbiology, many laboratories are challenged by the method's disruptive effect on
more » ... raditional lab workflows and by the complexities inherent to establishing a robust, standardized, and validated workflow in the clinical lab. Metagenomics is uniquely sensitive to the introduction of contamination and bias along almost every step of the workflow which can impact accuracy, precision, and a timely and actionable diagnosis. Therefore, the optimization and standardization of pre-sequencing, sequencing, and post-sequencing steps have to be carefully considered. In this presentation we shed light on failure-modes and present mitigation strategies employed at the CosmosID CLIA-certified NGS Service Laboratory. We address the optimization and validation of laboratory methods designed to avoid laboratory contamination and to control for the introduction of bias or contamination. The use of internal standards, including positive and negative controls are an important part of quality control. Also, the bioinformatic analysis of metagenomic data remains a challenge for many laboratories. A myriad of published algorithms scientifically explore different approaches for deconvoluting the valuable biological signal from bias and error introduced during the pre-sequencing and sequencing phases. While the clinically informative and actionable unit in microbiology is a strain, not a genus or species, most available methods fail to taxonomically classify detected microbes with sub-species level resolution. We present data from independent validations demonstrating that CosmosID algorithms and proprietary databases enable classification of microbes with strain-level resolution and industry-leading sensitivity and precision.
pmid:31892891 pmcid:PMC6936895 fatcat:nw2yugjlbrhchi4dlzge5xhlbe