Abstract

BackgroundOut of the many pathogenic bacterial species that are known, only a fraction are readily identifiable directly from a complex microbial community using standard next generation DNA sequencing. Long-read sequencing offers the potential to identify a wider range of species and to differentiate between strains within a species, but attaining sufficient accuracy in complex metagenomes remains a challenge.MethodsHere, we describe and analytically validate LoopSeq, a commercially available synthetic long-read (SLR) sequencing technology that generates highly accurate long reads from standard short reads.ResultsLoopSeq reads are sufficiently long and accurate to identify microbial genes and species directly from complex samples. LoopSeq perfectly recovered the full diversity of 16S rRNA genes from known strains in a synthetic microbial community. Full-length LoopSeq reads had a per-base error rate of 0.005%, which exceeds the accuracy reported for other long-read sequencing technologies. 18S-ITS and genomic sequencing of fungal and bacterial isolates confirmed that LoopSeq sequencing maintains that accuracy for reads up to 6 kb in length. LoopSeq full-length 16S rRNA reads could accurately classify organisms down to the species level in rinsate from retail meat samples, and could differentiate strains within species identified by the CDC as potential foodborne pathogens.ConclusionsThe order-of-magnitude improvement in length and accuracy over standard Illumina amplicon sequencing achieved with LoopSeq enables accurate species-level and strain identification from complex- to low-biomass microbiome samples. The ability to generate accurate and long microbiome sequencing reads using standard short read sequencers will accelerate the building of quality microbial sequence databases and removes a significant hurdle on the path to precision microbial genomics.4PHa9SHy5rS1yXExpPUL7_Video abstract.

Highlights

  • The characterization of bacterial species and strains directly from complex microbial samples using amplicon sequencing — in which PCR-amplified DNA fragments from complex genetic mixtures are sequenced — is still an ongoing challenge in microbiologyCallahan et al Microbiome (2021) 9:130 genetic variation [30]

  • synthetic long-read (SLR) technologies that were previously commercialized by 10x Genomics [40] and Moleculo [27] were not compatible with amplicon sequencing because they assign the same identifier to multiple DNA molecules in the same well/droplet, which is not amenable to reconstructing the sequence of single long molecules

  • We used the LoopSeq 16S Long Read Kit (Loop Genomics, CA) to barcode and amplify the full-length 16S rRNA gene, which was sequenced by Loop Genomics using Illumina NextSeq500 PE150

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Summary

Introduction

The characterization of bacterial species and strains directly from complex microbial samples using amplicon sequencing — in which PCR-amplified DNA fragments (amplicons) from complex genetic mixtures are sequenced — is still an ongoing challenge in microbiologyCallahan et al Microbiome (2021) 9:130 genetic variation [30]. The characterization of bacterial species and strains directly from complex microbial samples using amplicon sequencing — in which PCR-amplified DNA fragments (amplicons) from complex genetic mixtures are sequenced — is still an ongoing challenge in microbiology. SLR technologies that were previously commercialized by 10x Genomics [40] and Moleculo [27] were not compatible with amplicon sequencing because they assign the same identifier to multiple DNA molecules in the same well/droplet, which is not amenable to reconstructing the sequence of single long molecules. Out of the many pathogenic bacterial species that are known, only a fraction are readily identifiable directly from a complex microbial community using standard generation DNA sequencing. Long-read sequencing offers the potential to identify a wider range of species and to differentiate between strains within a species, but attaining sufficient accuracy in complex metagenomes remains a challenge

Methods
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Conclusion

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