Evidence map›Paper›PMID 25330259›Full record

ArticlePloS one2014

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.

Diogo M O Ogawa, Shigeharu Moriya, Yuuri Tsuboi, Yasuhiro Date, Álvaro R B Prieto-da-Silva, Gandhi Rádis-Baptista, Tetsuo Yamane, Jun Kikuchi

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Diogo M O OgawaBiotechnology and Natural Resources Program, University of the State of the Amazonas, Manaus, AM, Brazil; Laboratory of Biochemistry and Biotechnology, Institute for Marine Sciences, Federal University of Ceara, Fortaleza, CE, Brazil; Center for Environment and Biodiversity Studies, University of the State of the Amazonas, Manaus, AM, Brazil; RIKEN Center for Sustainable Resource Science, and Biomass Engineering Corporation Division, Yokohama, Japan.
Shigeharu MoriyaRIKEN Center for Sustainable Resource Science, and Biomass Engineering Corporation Division, Yokohama, Japan; RIKEN Antibiotics Laboratory, Yokohama, Japan; Graduate School of Medical Life Science, Yokohama City University, Suehiro-cho, Tsurumi-ku, Yokohama, Japan.
Yuuri TsuboiRIKEN Center for Sustainable Resource Science, and Biomass Engineering Corporation Division, Yokohama, Japan.
Yasuhiro DateRIKEN Center for Sustainable Resource Science, and Biomass Engineering Corporation Division, Yokohama, Japan; Graduate School of Medical Life Science, Yokohama City University, Suehiro-cho, Tsurumi-ku, Yokohama, Japan.
Álvaro R B Prieto-da-SilvaBiotechnology and Natural Resources Program, University of the State of the Amazonas, Manaus, AM, Brazil; Center for Environment and Biodiversity Studies, University of the State of the Amazonas, Manaus, AM, Brazil; Laboratory of Genetics, Butantan Institute, Sao Paulo, SP, Brazil.
Gandhi Rádis-BaptistaBiotechnology and Natural Resources Program, University of the State of the Amazonas, Manaus, AM, Brazil; Laboratory of Biochemistry and Biotechnology, Institute for Marine Sciences, Federal University of Ceara, Fortaleza, CE, Brazil; Center for Environment and Biodiversity Studies, University of the State of the Amazonas, Manaus, AM, Brazil.
Tetsuo YamaneBiotechnology and Natural Resources Program, University of the State of the Amazonas, Manaus, AM, Brazil; Center for Environment and Biodiversity Studies, University of the State of the Amazonas, Manaus, AM, Brazil; Center of Biotechnology of Amazon, Manaus, AM, Brazil.
Jun KikuchiRIKEN Center for Sustainable Resource Science, and Biomass Engineering Corporation Division, Yokohama, Japan; Graduate School of Medical Life Science, Yokohama City University, Suehiro-cho, Tsurumi-ku, Yokohama, Japan; Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Databases, GeneticEcosystemMetagenome

Identifiers

PMID25330259
PMCPMC4203823

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.