Multiple-Disease Detection and Classification across Cohorts via Microbiome Search

ABSTRACT Microbiome-based disease classification depends on well-validated disease-specific models or a priori organismal markers. These are lacking for many diseases. Here, we present an alternative, search-based strategy for disease detection and classification, which detects diseased samples via...

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Autores principales:	Xiaoquan Su, Gongchao Jing, Zheng Sun, Lu Liu, Zhenjiang Xu, Daniel McDonald, Zengbin Wang, Honglei Wang, Antonio Gonzalez, Yufeng Zhang, Shi Huang, Gavin Huttley, Rob Knight, Jian Xu
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	microbiome search disease detection and classification Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/6392988b2a2f43b491e1fb8de1f441c5
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spelling	oai:doaj.org-article:6392988b2a2f43b491e1fb8de1f441c52021-12-02T19:47:38ZMultiple-Disease Detection and Classification across Cohorts via Microbiome Search10.1128/mSystems.00150-202379-5077https://doaj.org/article/6392988b2a2f43b491e1fb8de1f441c52020-04-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00150-20https://doaj.org/toc/2379-5077ABSTRACT Microbiome-based disease classification depends on well-validated disease-specific models or a priori organismal markers. These are lacking for many diseases. Here, we present an alternative, search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares these to databases of samples from patients. Our strategy’s precision, sensitivity, and speed outperform model-based approaches. In addition, it is more robust to platform heterogeneity and to contamination in 16S rRNA gene amplicon data sets. This search-based strategy shows promise as an important first step in microbiome big-data-based diagnosis. IMPORTANCE Here, we present a search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares them to databases of samples from patients. This approach enables the identification of microbiome states associated with disease even in the presence of different cohorts, multiple sequencing platforms, or significant contamination.Xiaoquan SuGongchao JingZheng SunLu LiuZhenjiang XuDaniel McDonaldZengbin WangHonglei WangAntonio GonzalezYufeng ZhangShi HuangGavin HuttleyRob KnightJian XuAmerican Society for Microbiologyarticlemicrobiomesearchdisease detection and classificationMicrobiologyQR1-502ENmSystems, Vol 5, Iss 2 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	microbiome search disease detection and classification Microbiology QR1-502
spellingShingle	microbiome search disease detection and classification Microbiology QR1-502 Xiaoquan Su Gongchao Jing Zheng Sun Lu Liu Zhenjiang Xu Daniel McDonald Zengbin Wang Honglei Wang Antonio Gonzalez Yufeng Zhang Shi Huang Gavin Huttley Rob Knight Jian Xu Multiple-Disease Detection and Classification across Cohorts via Microbiome Search
description	ABSTRACT Microbiome-based disease classification depends on well-validated disease-specific models or a priori organismal markers. These are lacking for many diseases. Here, we present an alternative, search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares these to databases of samples from patients. Our strategy’s precision, sensitivity, and speed outperform model-based approaches. In addition, it is more robust to platform heterogeneity and to contamination in 16S rRNA gene amplicon data sets. This search-based strategy shows promise as an important first step in microbiome big-data-based diagnosis. IMPORTANCE Here, we present a search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares them to databases of samples from patients. This approach enables the identification of microbiome states associated with disease even in the presence of different cohorts, multiple sequencing platforms, or significant contamination.
format	article
author	Xiaoquan Su Gongchao Jing Zheng Sun Lu Liu Zhenjiang Xu Daniel McDonald Zengbin Wang Honglei Wang Antonio Gonzalez Yufeng Zhang Shi Huang Gavin Huttley Rob Knight Jian Xu
author_facet	Xiaoquan Su Gongchao Jing Zheng Sun Lu Liu Zhenjiang Xu Daniel McDonald Zengbin Wang Honglei Wang Antonio Gonzalez Yufeng Zhang Shi Huang Gavin Huttley Rob Knight Jian Xu
author_sort	Xiaoquan Su
title	Multiple-Disease Detection and Classification across Cohorts via Microbiome Search
title_short	Multiple-Disease Detection and Classification across Cohorts via Microbiome Search
title_full	Multiple-Disease Detection and Classification across Cohorts via Microbiome Search
title_fullStr	Multiple-Disease Detection and Classification across Cohorts via Microbiome Search
title_full_unstemmed	Multiple-Disease Detection and Classification across Cohorts via Microbiome Search
title_sort	multiple-disease detection and classification across cohorts via microbiome search
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/6392988b2a2f43b491e1fb8de1f441c5
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Multiple-Disease Detection and Classification across Cohorts via Microbiome Search

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