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Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data

Identifying the microbes present in probiotic products is an important issue in product quality control and public health. The most common methods used to identify genera containing species that produce lactic acid are matrix-assisted laser desorption/ionization–time of flight mass spectrometry (MAL...

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Autores principales: Seol, Donghyeok, Jhang, So Yun, Kim, Hyaekang, Kim, Se-Young, Kwak, Hyo-Sun, Kim, Soon Han, Lee, Woojung, Park, Sewook, Kim, Heebal, Cho, Seoae, Kwak, Woori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693478/
https://www.ncbi.nlm.nih.gov/pubmed/31440213
http://dx.doi.org/10.3389/fmicb.2019.01683
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author Seol, Donghyeok
Jhang, So Yun
Kim, Hyaekang
Kim, Se-Young
Kwak, Hyo-Sun
Kim, Soon Han
Lee, Woojung
Park, Sewook
Kim, Heebal
Cho, Seoae
Kwak, Woori
author_facet Seol, Donghyeok
Jhang, So Yun
Kim, Hyaekang
Kim, Se-Young
Kwak, Hyo-Sun
Kim, Soon Han
Lee, Woojung
Park, Sewook
Kim, Heebal
Cho, Seoae
Kwak, Woori
author_sort Seol, Donghyeok
collection PubMed
description Identifying the microbes present in probiotic products is an important issue in product quality control and public health. The most common methods used to identify genera containing species that produce lactic acid are matrix-assisted laser desorption/ionization–time of flight mass spectrometry (MALDI-TOF MS) and 16S rRNA sequence analysis. However, the high cost of operation, difficulty in distinguishing between similar species, and limitations of the current sequencing technologies have made it difficult to obtain accurate results using these tools. To overcome these problems, a whole-genome shotgun sequencing approach has been developed along with various metagenomic classification tools. Widely used tools include the marker gene and k-mer methods, but their inevitable false-positives (FPs) hampered an accurate analysis. We therefore, designed a coverage-based pipeline to reduce the FP problem and to achieve a more reliable identification of species. The coverage-based pipeline described here not only shows higher accuracy for the detection of species and proportion analysis, based on mapping depth, but can be applied regardless of the sequencing platform. We believe that the coverage-based pipeline described in this study can provide appropriate support for probiotic quality control, addressing current labeling issues.
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spelling pubmed-66934782019-08-22 Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data Seol, Donghyeok Jhang, So Yun Kim, Hyaekang Kim, Se-Young Kwak, Hyo-Sun Kim, Soon Han Lee, Woojung Park, Sewook Kim, Heebal Cho, Seoae Kwak, Woori Front Microbiol Microbiology Identifying the microbes present in probiotic products is an important issue in product quality control and public health. The most common methods used to identify genera containing species that produce lactic acid are matrix-assisted laser desorption/ionization–time of flight mass spectrometry (MALDI-TOF MS) and 16S rRNA sequence analysis. However, the high cost of operation, difficulty in distinguishing between similar species, and limitations of the current sequencing technologies have made it difficult to obtain accurate results using these tools. To overcome these problems, a whole-genome shotgun sequencing approach has been developed along with various metagenomic classification tools. Widely used tools include the marker gene and k-mer methods, but their inevitable false-positives (FPs) hampered an accurate analysis. We therefore, designed a coverage-based pipeline to reduce the FP problem and to achieve a more reliable identification of species. The coverage-based pipeline described here not only shows higher accuracy for the detection of species and proportion analysis, based on mapping depth, but can be applied regardless of the sequencing platform. We believe that the coverage-based pipeline described in this study can provide appropriate support for probiotic quality control, addressing current labeling issues. Frontiers Media S.A. 2019-08-07 /pmc/articles/PMC6693478/ /pubmed/31440213 http://dx.doi.org/10.3389/fmicb.2019.01683 Text en Copyright © 2019 Seol, Jhang, Kim, Kim, Kwak, Kim, Lee, Park, Kim, Cho and Kwak. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Seol, Donghyeok
Jhang, So Yun
Kim, Hyaekang
Kim, Se-Young
Kwak, Hyo-Sun
Kim, Soon Han
Lee, Woojung
Park, Sewook
Kim, Heebal
Cho, Seoae
Kwak, Woori
Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data
title Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data
title_full Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data
title_fullStr Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data
title_full_unstemmed Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data
title_short Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data
title_sort accurate and strict identification of probiotic species based on coverage of whole-metagenome shotgun sequencing data
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693478/
https://www.ncbi.nlm.nih.gov/pubmed/31440213
http://dx.doi.org/10.3389/fmicb.2019.01683
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