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Resolving the Complexity of Human Skin Metagenomes Using Single-Molecule Sequencing

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ABSTRACT Deep metagenomic shotgun sequencing has emerged as a powerful tool to interrogate composition and function of complex microbial communities. Computational approaches to assemble genome fragments have been demonstrated to be an effective tool for de novo reconstruction of genomes from these communities. However, the resultant “genomes” are typically fragmented and incomplete due to the limited ability of short-read sequence data to assemble complex or low-coverage regions. Here, we use single-molecule, real-time (SMRT) sequencing to reconstruct a high-quality, closed genome of a previously uncharacterized Corynebacterium simulans and its companion bacteriophage from a skin metagenomic sample. Considerable improvement in assembly quality occurs in hybrid approaches incorporating short-read data, with even relatively small amounts of long-read data being sufficient to improve metagenome reconstruction. Using short-read data to evaluate strain variation of this C. simulans in its skin community at single-nucleotide resolution, we observed a dominant C. simulans strain with moderate allelic heterozygosity throughout the population. We demonstrate the utility of SMRT sequencing and hybrid approaches in metagenome quantitation, reconstruction, and annotation. IMPORTANCE The species comprising a microbial community are often difficult to deconvolute due to technical limitations inherent to most short-read sequencing technologies. Here, we leverage new advances in sequencing technology, single-molecule sequencing, to significantly improve reconstruction of a complex human skin microbial community. With this long-read technology, we were able to reconstruct and annotate a closed, high-quality genome of a previously uncharacterized skin species. We demonstrate that hybrid approaches with short-read technology are sufficiently powerful to reconstruct even single-nucleotide polymorphism level variation of species in this a community.
Title: Resolving the Complexity of Human Skin Metagenomes Using Single-Molecule Sequencing
Description:
ABSTRACT Deep metagenomic shotgun sequencing has emerged as a powerful tool to interrogate composition and function of complex microbial communities.
Computational approaches to assemble genome fragments have been demonstrated to be an effective tool for de novo reconstruction of genomes from these communities.
However, the resultant “genomes” are typically fragmented and incomplete due to the limited ability of short-read sequence data to assemble complex or low-coverage regions.
Here, we use single-molecule, real-time (SMRT) sequencing to reconstruct a high-quality, closed genome of a previously uncharacterized Corynebacterium simulans and its companion bacteriophage from a skin metagenomic sample.
Considerable improvement in assembly quality occurs in hybrid approaches incorporating short-read data, with even relatively small amounts of long-read data being sufficient to improve metagenome reconstruction.
Using short-read data to evaluate strain variation of this C.
 simulans in its skin community at single-nucleotide resolution, we observed a dominant C.
 simulans strain with moderate allelic heterozygosity throughout the population.
We demonstrate the utility of SMRT sequencing and hybrid approaches in metagenome quantitation, reconstruction, and annotation.
IMPORTANCE The species comprising a microbial community are often difficult to deconvolute due to technical limitations inherent to most short-read sequencing technologies.
Here, we leverage new advances in sequencing technology, single-molecule sequencing, to significantly improve reconstruction of a complex human skin microbial community.
With this long-read technology, we were able to reconstruct and annotate a closed, high-quality genome of a previously uncharacterized skin species.
We demonstrate that hybrid approaches with short-read technology are sufficiently powerful to reconstruct even single-nucleotide polymorphism level variation of species in this a community.

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