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. 2013 Jul 23;7:64.
doi: 10.1186/1752-0509-7-64.

3Omics: A Web-Based Systems Biology Tool for Analysis, Integration and Visualization of Human Transcriptomic, Proteomic and Metabolomic Data

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Free PMC article

3Omics: A Web-Based Systems Biology Tool for Analysis, Integration and Visualization of Human Transcriptomic, Proteomic and Metabolomic Data

Tien-Chueh Kuo et al. BMC Syst Biol. .
Free PMC article

Abstract

Background: Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment.

Results: 3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites. If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. 3Omics' coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics' phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets. The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature.

Conclusions: 3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis. The 3Omics software can be freely accessed at http://3omics.cmdm.tw.

Figures

Figure 1
Figure 1
3Omics User interface. (A) 3Omics implements seven inter-omic analyses: (a) Transcriptomics-Proteomics-Metabolomics, (b) Transcriptomics-Proteomics, (c) Proteomics-Metabolomics, (d) Treanscriptomics-Metabolomics, and intra-omics analyses, such as (e) Transcriptomics, (f) Proteomics, and (g) Metabolomics. Users select the desired analysis by selecting the corresponding icon. (B) Interface for the Transcriptomics-Proteomics analysis. (C) Interface for the Metabolomics analysis.
Figure 2
Figure 2
3Omics-generated Correlation network analysis. Features include the following: (A) toggling zoom/explore mode, saving as SVG format, downloading the full-size image and SIF files for Cytoscape import; (B) literature-derived edges are presented as dotted lines; (C) adjusting parameters to customize the correlation network.
Figure 3
Figure 3
3Omics-generated coexpression profile. An example coexpression profile for a Transcriptomics-Metabolomics analysis is shown. (A) Molecules in the largest cluster of the correlation analysis have highly similar expression profiles. (B) Molecules in the second-largest cluster also have highly similar expression profiles. Pink cells denote higher expression, and cyan cells denote lower expression. Row edges are color coded according to the omics data source type: green, transcriptomics; red, proteomics; and blue, metabolomics.
Figure 4
Figure 4
3Omics GO Enrichment analysis. GO terms with p-values less than 0.05 are displayed in a bar chart. Detailed results are divided into three sections corresponding to the three ontologies. Each section has a table summarizing the enriched terms with the mapped Entrez Gene IDs, the coverage of the input Gene IDs, and the p-values.
Figure 5
Figure 5
3Omics Phenotype analysis. Input transcriptomics data are used to query the internal 3Omics phenotype database. The matched gene-phenotype results are returned in a table. Each OMIM entry includes a hyperlink to the external OMIM database.
Figure 6
Figure 6
3Omics Pathway enrichment analysis. The enriched KEGG/HumanCyc pathways, ranked by probability from the hypergeometric test, are returned in a table. (A) Consistent with the original study results, the “glycine, serine and threonine metabolism pathway” (highlighted) is a top search hit, with a probability of 0.74 based on the KEGG pathway enrichment analysis. (B) The enriched pathways from the HumanCyc pathway enrichment are also consistent with the original study results and the KEGG Pathway enrichment and provide a significant amount of meaningful information.

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