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Ultra-accurate microbial amplicon sequencing with synthetic long reads
BACKGROUND: Out 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 different...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179091/ https://www.ncbi.nlm.nih.gov/pubmed/34090540 http://dx.doi.org/10.1186/s40168-021-01072-3 |
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author | Callahan, Benjamin J. Grinevich, Dmitry Thakur, Siddhartha Balamotis, Michael A. Yehezkel, Tuval Ben |
author_facet | Callahan, Benjamin J. Grinevich, Dmitry Thakur, Siddhartha Balamotis, Michael A. Yehezkel, Tuval Ben |
author_sort | Callahan, Benjamin J. |
collection | PubMed |
description | BACKGROUND: Out 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. METHODS: Here, 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. RESULTS: LoopSeq 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. CONCLUSIONS: The 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-021-01072-3. |
format | Online Article Text |
id | pubmed-8179091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81790912021-06-05 Ultra-accurate microbial amplicon sequencing with synthetic long reads Callahan, Benjamin J. Grinevich, Dmitry Thakur, Siddhartha Balamotis, Michael A. Yehezkel, Tuval Ben Microbiome Research BACKGROUND: Out 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. METHODS: Here, 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. RESULTS: LoopSeq 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. CONCLUSIONS: The 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-021-01072-3. BioMed Central 2021-06-05 /pmc/articles/PMC8179091/ /pubmed/34090540 http://dx.doi.org/10.1186/s40168-021-01072-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Callahan, Benjamin J. Grinevich, Dmitry Thakur, Siddhartha Balamotis, Michael A. Yehezkel, Tuval Ben Ultra-accurate microbial amplicon sequencing with synthetic long reads |
title | Ultra-accurate microbial amplicon sequencing with synthetic long reads |
title_full | Ultra-accurate microbial amplicon sequencing with synthetic long reads |
title_fullStr | Ultra-accurate microbial amplicon sequencing with synthetic long reads |
title_full_unstemmed | Ultra-accurate microbial amplicon sequencing with synthetic long reads |
title_short | Ultra-accurate microbial amplicon sequencing with synthetic long reads |
title_sort | ultra-accurate microbial amplicon sequencing with synthetic long reads |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179091/ https://www.ncbi.nlm.nih.gov/pubmed/34090540 http://dx.doi.org/10.1186/s40168-021-01072-3 |
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