ECOMICS: A Web-Based Toolkit for Investigating the Biomolecular Web in Ecosystems Using a Trans-omics Approach
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{"title"=>"ECOMICS: A web-based toolkit for investigating the biomolecular web in ecosystems using a trans-omics approach", "type"=>"journal", "authors"=>[{"first_name"=>"Yoshiyuki", "last_name"=>"Ogata", "scopus_author_id"=>"7201752902"}, {"first_name"=>"Eisuke", "last_name"=>"Chikayama", "scopus_author_id"=>"6506922861"}, {"first_name"=>"Yusuke", "last_name"=>"Morioka", "scopus_author_id"=>"54944897300"}, {"first_name"=>"R. Craig", "last_name"=>"Everroad", "scopus_author_id"=>"26648383100"}, {"first_name"=>"Amiu", "last_name"=>"Shino", "scopus_author_id"=>"54946063800"}, {"first_name"=>"Akihiro", "last_name"=>"Matsushima", "scopus_author_id"=>"36724863700"}, {"first_name"=>"Hideaki", "last_name"=>"Haruna", "scopus_author_id"=>"54940349300"}, {"first_name"=>"Shigeharu", "last_name"=>"Moriya", "scopus_author_id"=>"7101667124"}, {"first_name"=>"Tetsuro", "last_name"=>"Toyoda", "scopus_author_id"=>"7201723948"}, {"first_name"=>"Jun", "last_name"=>"Kikuchi", "scopus_author_id"=>"56423275500"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"364181133", "sgr"=>"84856471272", "issn"=>"19326203", "pmid"=>"22319563", "scopus"=>"2-s2.0-84856471272", "doi"=>"10.1371/journal.pone.0030263", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"658fa1b3-4d13-3337-b266-1e9d5d31acc8", "abstract"=>"Ecosystems can be conceptually thought of as interconnected environmental and metabolic systems, in which small molecules to macro-molecules interact through diverse networks. State-of-the-art technologies in post-genomic science offer ways to inspect and analyze this biomolecular web using omics-based approaches. Exploring useful genes and enzymes, as well as biomass resources responsible for anabolism and catabolism within ecosystems will contribute to a better understanding of environmental functions and their application to biotechnology. Here we present ECOMICS, a suite of web-based tools for ECosystem trans-OMICS investigation that target metagenomic, metatranscriptomic, and meta-metabolomic systems, including biomacromolecular mixtures derived from biomass. ECOMICS is made of four integrated webtools. E-class allows for the sequence-based taxonomic classification of eukaryotic and prokaryotic ribosomal data and the functional classification of selected enzymes. FT2B allows for the digital processing of NMR spectra for downstream metabolic or chemical phenotyping. Bm-Char allows for statistical assignment of specific compounds found in lignocellulose-based biomass, and HetMap is a data matrix generator and correlation calculator that can be applied to trans-omics datasets as analyzed by these and other web tools. This web suite is unique in that it allows for the monitoring of biomass metabolism in a particular environment, i.e., from macromolecular complexes (FT2DB and Bm-Char) to microbial composition and degradation (E-class), and makes possible the understanding of relationships between molecular and microbial elements (HetMap). This website is available to the public domain at: https://database.riken.jp/ecomics/.", "link"=>"http://www.mendeley.com/research/ecomics-webbased-toolkit-investigating-biomolecular-web-ecosystems-using-transomics-approach", "reader_count"=>43, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>13, "Student > Ph. D. Student"=>8, "Student > Master"=>9, "Other"=>2, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>13, "Student > Ph. D. Student"=>8, "Student > Master"=>9, "Other"=>2, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Engineering"=>1, "Environmental Science"=>5, "Biochemistry, Genetics and Molecular Biology"=>4, "Agricultural and Biological Sciences"=>24, "Medicine and Dentistry"=>1, "Chemistry"=>2, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>24}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>5}}, "reader_count_by_country"=>{"Colombia"=>1, "Belgium"=>1, "Philippines"=>1, "Denmark"=>1, "United Kingdom"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/686305"], "description"=>"<p>(A) To query the E-class database, a several step process is used (see the Implementation section). (B) Network-based association between sequences of the E-class databases. A network module or group is composed of sequences and links between sequences representing sequence similarity. To reduce sizes of the ‘original’ databases, we selected one representative sequence from each identified module and set these as ‘modularized’ databases. (C) Our experimental example for a denaturing gradient gel electrophoreses (DGGE) analysis of a time series investigation of an Okinawa seawater microcosm experiment. Top numbers indicate sampling days for the microcosm experiments, and colored yellows are six DNA bands used for following E-class classification. (D) An example of this classification using our DGGE dataset as mentioned in Design and Implementation. (E) An example of a huge number of data, including 84 402 sequences, to the CBM sequence database, as mentioned in Design and Implementation.</p>", "links"=>[], "tags"=>["e-class", "ribosomal", "enzymatic", "sequences"], "article_id"=>356781, "categories"=>["Biotechnology", "Biochemistry", "Plant Biology", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Yoshiyuki Ogata", "Eisuke Chikayama", "Yusuke Morioka", "R. Craig Everroad", "Amiu Shino", "Akihiro Matsushima", "Hideaki Haruna", "Shigeharu Moriya", "Tetsuro Toyoda", "Jun Kikuchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0030263.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_E_class_web_tool_to_classify_ribosomal_and_enzymatic_sequences_from_environmental_samples_/356781", "title"=>"The E-class web tool to classify ribosomal and enzymatic sequences from environmental samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:22:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/686493"], "description"=>"<p>(A) A table of the Bm-Char main page, comprised of a data table displaying relationships between 88 chemical groups in 11 plant samples, 2D NMR signals for lignin and hemicellulose, and structures for components of lignocellulose. (B) Overlay of lignin aromatic region of 2D <sup>1</sup>H-<sup>13</sup>C HSQC spectra of poplar (brown), Japanese cedar (light brown) and <i>Erianthus</i> sp. (green). Lignin signal assignments and their chemical structures are highlighted along with corresponding cross peaks. (C) The query form. See the main text for detail.</p>", "links"=>[], "tags"=>["bm-char"], "article_id"=>356973, "categories"=>["Biotechnology", "Biochemistry", "Plant Biology", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Yoshiyuki Ogata", "Eisuke Chikayama", "Yusuke Morioka", "R. Craig Everroad", "Amiu Shino", "Akihiro Matsushima", "Hideaki Haruna", "Shigeharu Moriya", "Tetsuro Toyoda", "Jun Kikuchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0030263.g004", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Bm_Char_diagrams_/356973", "title"=>"The Bm-Char diagrams.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:23:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/686813"], "description"=>"<p>Top correlations (|<i>r</i>|>0.75) between DGGE bands and chemical shifts during time-course experiments (n = 7).</p>", "links"=>[], "tags"=>["correlations", "dgge", "bands", "shifts", "time-course", "experiments"], "article_id"=>357291, "categories"=>["Biotechnology", "Biochemistry", "Plant Biology", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Yoshiyuki Ogata", "Eisuke Chikayama", "Yusuke Morioka", "R. Craig Everroad", "Amiu Shino", "Akihiro Matsushima", "Hideaki Haruna", "Shigeharu Moriya", "Tetsuro Toyoda", "Jun Kikuchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0030263.t001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_correlations_r_0_75_between_DGGE_bands_and_chemical_shifts_during_time_course_experiments_n_7_/357291", "title"=>"Top correlations (|<i>r</i>|>0.75) between DGGE bands and chemical shifts during time-course experiments (n = 7).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 09:24:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/686624"], "description"=>"<p>Four input data files: meta-genomic, meta-transcriptomic, meta-metabolomic, and biomass data (A) from the same series of experiments for environmental samples are displayed in this figure. A user can obtain an image file that shows positive (red) and negative (blue) correlations among all the heterogeneous data that a user inputs (B). Example of <i>r</i>-value investigation using three kinds of correlation calculation (C, from left to right: Pearson, Spearman and cosine). Distribution of the correlation coefficient <i>r</i> is visualized in pie-chart with red (positive) to blue (negative) color gradation. Expanded region of 2D heat map image (|<i>r</i>|>0) derived from inputting DNA sequence (blue) and metabolites (green) data matrices of aquatic microcosm experiments (n = 7, Day 1, 7, 19, 42, 68, 75, and 83). From the number of samples included at a cutoff of <i>p</i><0.05, we have chosen |<i>r</i>|>0.75 for visualizing significant correlation between DGGE and NMR data (E).</p>", "links"=>[], "tags"=>["performing", "trans-omics"], "article_id"=>357103, "categories"=>["Biotechnology", "Biochemistry", "Plant Biology", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Yoshiyuki Ogata", "Eisuke Chikayama", "Yusuke Morioka", "R. Craig Everroad", "Amiu Shino", "Akihiro Matsushima", "Hideaki Haruna", "Shigeharu Moriya", "Tetsuro Toyoda", "Jun Kikuchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0030263.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_HetMap_a_tool_for_performing_a_trans_omics_analysis_for_multiple_input_data_/357103", "title"=>"HetMap, a tool for performing a trans-omics analysis for multiple input data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:24:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/686215"], "description"=>"<p>Since the global ecosystem is composed of biodiversity in plant, animal and bacterial systems, our research target is not focused to single species, but accepts matrix datasets measured from complex systems. Experimental data should be comprised of a mixture of molecules from DNA to biomass. We developed four kinds of web tools and databases; the E-class web tool for taxonomic (metagenomic) classification based on prokaryotic and eukaryotic ribosomal sequences and for functional (enzymatic) classification based on sequential domains, FT2DB for the digitization of NMR spectra for downstream chemical (from metabolic to macromolecular) phenotyping, Bm-Char for the chemical (macromolecular biomass) assignment of lignocellulose components, and HetMap for identifying and viewing correlations between heterogeneous trans-omics data sets that are produced by such web tools.</p>", "links"=>[], "tags"=>["ecomics", "schematic", "relationships", "omics"], "article_id"=>356686, "categories"=>["Biotechnology", "Biochemistry", "Plant Biology", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Yoshiyuki Ogata", "Eisuke Chikayama", "Yusuke Morioka", "R. Craig Everroad", "Amiu Shino", "Akihiro Matsushima", "Hideaki Haruna", "Shigeharu Moriya", "Tetsuro Toyoda", "Jun Kikuchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0030263.g001", "stats"=>{"downloads"=>0, "page_views"=>46, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_ECOMICS_schematic_concept_for_analysis_of_relationships_between_the_environment_and_omics_datasets_/356686", "title"=>"The ECOMICS schematic concept for analysis of relationships between the environment and omics datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:21:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/686394"], "description"=>"<p>(A) Screenshot of the FT2DB interface. This program allows for the simultaneous submission of several 1D or 2D NMR spectra and the user can specify the chemical shift range and the resolution of binning. (B) Representative output data from FT2DB. These data include pie charts for easy visualization and text data suitable for copying into a text editor for downstream analyses, such as with HetMap. (C) Resulting pie charts for the time series of 1D <sup>1</sup>H NMR spectra of marine plankton. Chemical shifts are graded from red (−1 ppm) to green (9 ppm) and the fractions in spectrum intensity are shown in the pie charts. Spectra are labeled to the right with the sampling day. The raw NMR spectra are also shown. These examples are available at the website as are example 2D NMR spectra.</p>", "links"=>[], "tags"=>["nmr"], "article_id"=>356877, "categories"=>["Biotechnology", "Biochemistry", "Plant Biology", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Yoshiyuki Ogata", "Eisuke Chikayama", "Yusuke Morioka", "R. Craig Everroad", "Amiu Shino", "Akihiro Matsushima", "Hideaki Haruna", "Shigeharu Moriya", "Tetsuro Toyoda", "Jun Kikuchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0030263.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_FT2DB_for_processing_NMR_spectra_/356877", "title"=>"FT2DB for processing NMR spectra.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:22:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/686725"], "description"=>"<p>(A) Self-correlation heatmap calculated by inputting 23 lignocellulose HSQC spectra into HetMap. A Pearson product-moment correlation coefficient with cut of |<i>r</i>|>0.75 was employed in this study. This heatmap shows ROI data matrices aligning from lignin aromatics, lignin, aliphatics to hemicelluloses sugars and methyl groups, and each molecules exhibited different colors along with vertical and horizontal bars. Expansion of lignin aromatics (guaiacyl, syringyl) versus aliphatics (β-O-4 hydroxyphenyl/guaiacyl) heatmap with showing calculated correlations in these chemical structures (B), as well as methoxy versus guaiacyl (C) and acetyl versus xylopyranose (D), respectively.</p>", "links"=>[], "tags"=>["biomass", "nmr"], "article_id"=>357206, "categories"=>["Biotechnology", "Biochemistry", "Plant Biology", "Genetics", "Biological Sciences", "Ecology"], "users"=>["Yoshiyuki Ogata", "Eisuke Chikayama", "Yusuke Morioka", "R. Craig Everroad", "Amiu Shino", "Akihiro Matsushima", "Hideaki Haruna", "Shigeharu Moriya", "Tetsuro Toyoda", "Jun Kikuchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0030263.g006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_STOCSY_type_analysis_of_biomass_NMR_spectra_/357206", "title"=>"STOCSY-type analysis of biomass NMR spectra.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 09:24:32"}

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