Implications of Error-Prone Long-Read Whole-Genome Shotgun Sequencing on Characterizing Reference Microbiomes.

Yu Hu,Kai Wang,Christopher Nicholson,Li Fang

doi:10.1016/j.isci.2020.101223

Yu Hu, Kai Wang + Show 2 more

Open Access

https://doi.org/10.1016/j.isci.2020.101223

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Abstract

SummaryLong-read sequencing techniques, such as the Oxford Nanopore Technology, can generate reads that are tens of kilobases in length and are therefore particularly relevant for microbiome studies. However, owing to the higher per-base error rates than typical short-read sequencing, the application of long-read sequencing on microbiomes remains largely unexplored. Here we deeply sequenced two human microbiota mock community samples (HM-276D and HM-277D) from the Human Microbiome Project. We showed that assembly programs consistently achieved high accuracy (∼99%) and completeness (∼99%) for bacterial strains with adequate coverage. We also found that long-read sequencing provides accurate estimates of species-level abundance (R = 0.94 for 20 bacteria with abundance ranging from 0.005% to 64%). Our results not only demonstrate the feasibility of characterizing complete microbial genomes and populations from error-prone Nanopore sequencing data but also highlight necessary bioinformatics improvements for future metagenomics tool development.

Highlights

The fundamental importance of microbiota as the microbial communities that reside in human body is increasingly recognized
Owing to the higher per-base error rates than typical short-read sequencing, the application of long-read sequencing on microbiomes remains largely unexplored
We showed that assembly programs consistently achieved high accuracy (99%) and completeness (99%) for bacterial strains with adequate coverage

Summary

Introduction

The fundamental importance of microbiota as the microbial communities that reside in human body is increasingly recognized. The high-throughput nature of metagenomics sequencing allows us to interpret microbial community by using computational approaches such as operational taxonomic unit (OTU) identification (Hao and Chen, 2012), abundance quantification (Chen et al, 2017), read assembly (Ruan and Li, 2019; Bertrand et al, 2019; Koren et al, 2017; Kolmogorov et al, 2019; Li et al, 2015), and binning and taxonomic profiling (Gregor et al, 2016; Huson et al, 2016, 2018; Francis et al, 2013; Hong et al, 2014; Byrd et al, 2014). 16S rRNA sequencing targets on very specific regions that are highly variable between species, which is much cost-efficient This is very useful for us to examine and compare the microbiota across a high number of samples in a large-scale project. It allows us to perform deep investigation of the microbial community as its ability to capture sequences from all organisms

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