Identifying and Predicting Novelty in Microbiome Studies

ABSTRACT With the expansion of microbiome sequencing globally, a key challenge is to relate new microbiome samples to the existing space of microbiome samples. Here, we present Microbiome Search Engine (MSE), which enables the rapid search of query microbiome samples against a large, well-curated re...

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Autores principales:	Xiaoquan Su, Gongchao Jing, Daniel McDonald, Honglei Wang, Zengbin Wang, Antonio Gonzalez, Zheng Sun, Shi Huang, Jose Navas, Rob Knight, Jian Xu
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2018
Materias:	microbiome search novelty data mining bioinformatics community similarity Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/7183f474a9614bfa8f1974cb4b791166
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spelling	oai:doaj.org-article:7183f474a9614bfa8f1974cb4b7911662021-11-15T15:52:19ZIdentifying and Predicting Novelty in Microbiome Studies10.1128/mBio.02099-182150-7511https://doaj.org/article/7183f474a9614bfa8f1974cb4b7911662018-12-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.02099-18https://doaj.org/toc/2150-7511ABSTRACT With the expansion of microbiome sequencing globally, a key challenge is to relate new microbiome samples to the existing space of microbiome samples. Here, we present Microbiome Search Engine (MSE), which enables the rapid search of query microbiome samples against a large, well-curated reference microbiome database organized by taxonomic similarity at the whole-microbiome level. Tracking the microbiome novelty score (MNS) over 8 years of microbiome depositions based on searching in more than 100,000 global 16S rRNA gene amplicon samples, we detected that the structural novelty of human microbiomes is approaching saturation and likely bounded, whereas that in environmental habitats remains 5 times higher. Via the microbiome focus index (MFI), which is derived from the MNS and microbiome attention score (MAS), we objectively track and compare the structural-novelty and attracted-attention scores of individual microbiome samples and projects, and we predict future trends in the field. For example, marine and indoor environments and mother-baby interactions are likely to receive disproportionate additional attention based on recent trends. Therefore, MNS, MAS, and MFI are proposed “alt-metrics” for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data. IMPORTANCE We introduce two concepts to quantify the novelty of a microbiome. The first, the microbiome novelty score (MNS), allows identification of microbiomes that are especially different from what is already sequenced. The second, the microbiome attention score (MAS), allows identification of microbiomes that have many close neighbors, implying that considerable scientific attention is devoted to their study. By computing a microbiome focus index based on the MNS and MAS, we objectively track and compare the novelty and attention scores of individual microbiome samples and projects over time and predict future trends in the field; i.e., we work toward yielding fundamentally new microbiomes rather than filling in the details. Therefore, MNS, MAS, and MFI can serve as “alt-metrics” for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data.Xiaoquan SuGongchao JingDaniel McDonaldHonglei WangZengbin WangAntonio GonzalezZheng SunShi HuangJose NavasRob KnightJian XuAmerican Society for Microbiologyarticlemicrobiomesearchnoveltydata miningbioinformaticscommunity similarityMicrobiologyQR1-502ENmBio, Vol 9, Iss 6 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	microbiome search novelty data mining bioinformatics community similarity Microbiology QR1-502
spellingShingle	microbiome search novelty data mining bioinformatics community similarity Microbiology QR1-502 Xiaoquan Su Gongchao Jing Daniel McDonald Honglei Wang Zengbin Wang Antonio Gonzalez Zheng Sun Shi Huang Jose Navas Rob Knight Jian Xu Identifying and Predicting Novelty in Microbiome Studies
description	ABSTRACT With the expansion of microbiome sequencing globally, a key challenge is to relate new microbiome samples to the existing space of microbiome samples. Here, we present Microbiome Search Engine (MSE), which enables the rapid search of query microbiome samples against a large, well-curated reference microbiome database organized by taxonomic similarity at the whole-microbiome level. Tracking the microbiome novelty score (MNS) over 8 years of microbiome depositions based on searching in more than 100,000 global 16S rRNA gene amplicon samples, we detected that the structural novelty of human microbiomes is approaching saturation and likely bounded, whereas that in environmental habitats remains 5 times higher. Via the microbiome focus index (MFI), which is derived from the MNS and microbiome attention score (MAS), we objectively track and compare the structural-novelty and attracted-attention scores of individual microbiome samples and projects, and we predict future trends in the field. For example, marine and indoor environments and mother-baby interactions are likely to receive disproportionate additional attention based on recent trends. Therefore, MNS, MAS, and MFI are proposed “alt-metrics” for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data. IMPORTANCE We introduce two concepts to quantify the novelty of a microbiome. The first, the microbiome novelty score (MNS), allows identification of microbiomes that are especially different from what is already sequenced. The second, the microbiome attention score (MAS), allows identification of microbiomes that have many close neighbors, implying that considerable scientific attention is devoted to their study. By computing a microbiome focus index based on the MNS and MAS, we objectively track and compare the novelty and attention scores of individual microbiome samples and projects over time and predict future trends in the field; i.e., we work toward yielding fundamentally new microbiomes rather than filling in the details. Therefore, MNS, MAS, and MFI can serve as “alt-metrics” for evaluating a microbiome project or prospective developments in the microbiome field, both of which are done in the context of existing microbiome big data.
format	article
author	Xiaoquan Su Gongchao Jing Daniel McDonald Honglei Wang Zengbin Wang Antonio Gonzalez Zheng Sun Shi Huang Jose Navas Rob Knight Jian Xu
author_facet	Xiaoquan Su Gongchao Jing Daniel McDonald Honglei Wang Zengbin Wang Antonio Gonzalez Zheng Sun Shi Huang Jose Navas Rob Knight Jian Xu
author_sort	Xiaoquan Su
title	Identifying and Predicting Novelty in Microbiome Studies
title_short	Identifying and Predicting Novelty in Microbiome Studies
title_full	Identifying and Predicting Novelty in Microbiome Studies
title_fullStr	Identifying and Predicting Novelty in Microbiome Studies
title_full_unstemmed	Identifying and Predicting Novelty in Microbiome Studies
title_sort	identifying and predicting novelty in microbiome studies
publisher	American Society for Microbiology
publishDate	2018
url	https://doaj.org/article/7183f474a9614bfa8f1974cb4b791166
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Identifying and Predicting Novelty in Microbiome Studies

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