Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.

We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical...

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Autores principales:	Diogo M O Ogawa, Shigeharu Moriya, Yuuri Tsuboi, Yasuhiro Date, Álvaro R B Prieto-da-Silva, Gandhi Rádis-Baptista, Tetsuo Yamane, Jun Kikuchi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/63b7b8ec265b4231ae4ca61a33e383d6
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spelling	oai:doaj.org-article:63b7b8ec265b4231ae4ca61a33e383d62021-11-25T05:55:52ZBiogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.1932-620310.1371/journal.pone.0110723https://doaj.org/article/63b7b8ec265b4231ae4ca61a33e383d62014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0110723https://doaj.org/toc/1932-6203We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (1H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.Diogo M O OgawaShigeharu MoriyaYuuri TsuboiYasuhiro DateÁlvaro R B Prieto-da-SilvaGandhi Rádis-BaptistaTetsuo YamaneJun KikuchiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e110723 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Diogo M O Ogawa Shigeharu Moriya Yuuri Tsuboi Yasuhiro Date Álvaro R B Prieto-da-Silva Gandhi Rádis-Baptista Tetsuo Yamane Jun Kikuchi Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.
description	We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (1H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.
format	article
author	Diogo M O Ogawa Shigeharu Moriya Yuuri Tsuboi Yasuhiro Date Álvaro R B Prieto-da-Silva Gandhi Rádis-Baptista Tetsuo Yamane Jun Kikuchi
author_facet	Diogo M O Ogawa Shigeharu Moriya Yuuri Tsuboi Yasuhiro Date Álvaro R B Prieto-da-Silva Gandhi Rádis-Baptista Tetsuo Yamane Jun Kikuchi
author_sort	Diogo M O Ogawa
title	Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.
title_short	Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.
title_full	Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.
title_fullStr	Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.
title_full_unstemmed	Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.
title_sort	biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/63b7b8ec265b4231ae4ca61a33e383d6
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Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.

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