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

• Published in: PloS one 2014-10, Vol.9 (10), p.e110723-e110723
• Main Authors: Ogawa, Diogo M O, Moriya, Shigeharu, Tsuboi, Yuuri, Date, Yasuhiro, Prieto-da-Silva, Álvaro R B, Rádis-Baptista, Gandhi, Yamane, Tetsuo, Kikuchi, Jun
• Format: Article
• Language: English
• Subjects: Analysis; Archaea; Biochemistry; Biodiversity; Biogeochemistry; Biology and Life Sciences; Biomass; Biotechnology; Communities; Consortia; Correlation; Data processing; Databases, Genetic; Design and construction; Earth Sciences; Ecology and Environmental Sciences; Ecosystem; Emission spectroscopy; Encyclopedias; Engineering; Environmental studies; Filtrate; Filtration; Fourier transforms; Inductively coupled plasma; Infrared analysis; Laboratories; Life sciences; Magnetic resonance; Metabolomics; Metagenome; Methods; Natural resources; Next-generation sequencing; NMR; Nuclear magnetic resonance; Nuclear magnetic resonance spectroscopy; Nutrients; Optical emission spectroscopy; Organic chemicals; Physical Sciences; Pipe lines; Pipelines; RNA; rRNA 16S; rRNA 18S; Sediments; Spectroscopy; Spectrum analysis; Statistical analysis; Trends; Typing
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0110723

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.