"Analysis of Complex Microbial Samples Using High Definition Mapping"

February 27, 2019

Jay M. Sage, Barrett Bready, Anthony P. Catalano, Jennifer R. Davis, Michael D. Kaiser, John S. Oliver

Complex microbial communities play a critical role in a wide variety of biological systems in the environment and throughout the human body. Characterization of these communities has historically been limited to one or a small number of known genetic markers for species such as 16S rRNA genes. While the advent of inexpensive shotgun sequencing has enabled a more accurate measure of biodiversity than marker typing, short read lengths prevent accurate analysis of related strains within a mixture, as well as consistent characterization of large-scale structural variation that can distinguish highly related strains and significantly impact pathogenicity.

To address these issues, we have applied the Nabsys HD-MappingTM platform to strain-level identification of microbial strains in the context of complex mixtures. HD-Mapping employs fully electronic detection of tagged single DNA molecules, hundreds of kilobases in length, at a resolution superior to existing optical mapping approaches. This combination of long read lengths and high information density means that individual HD-Mapping reads tend to be much more specific to the genomes from which they derive than do NGS reads. As a result, differences between closely related strains of the same species become clear with minimal bioinformatics work.

Here we describe strain-level characterization of the ZymoBIOMICS Microbial Community Standard using Nabsys HD-Mapping. DNA was extracted using a standard kit-based isolation procedure, and single-molecule reads derived from the mixture were mapped to the NCBI database of all ~10,500 completed bacterial references, including ~1,700 references for species present in the mixture. Through analysis of unique read mapping characteristics, the correct reference was identified for each of the 8 bacterial strains present in the mixture as well as relative strain quantitation. In addition, we show that strain-level detection of the 8 bacterial strains is unaffected by the presence of 20% human DNA co-extracted with the mixture

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