3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data

BMC Systems Biology

Yufeng Jane Tseng¹  Tze-Feng Tian¹  Tien-Chueh Kuo² 

[1] Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;The Metabolomics Core Laboratory, Center of Genomic Medicine, , Taipei, Taiwan

关键词: Analysis;    Metabolomics;    Proteomics;    Transcriptomics;    Systems biology;    Omics integration;    Visualization;   

Others  :  1142621
DOI  :  10.1186/1752-0509-7-64

 received in 2012-09-28, accepted in 2013-07-17,  发布年份 2013

【 摘 要 】

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 webcite.

【 授权许可】

   
2013 Kuo et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Kolbe A, Oliver SN, Fernie AR, Stitt M, van Dongen JT, Geigenberger P: Combined transcript and metabolite profiling of Arabidopsis leaves reveals fundamental effects of the thiol-disulfide status on plant metabolism. Plant Physiol 2006, 141:412-422.
• [2]Cho K, Shibato J, Agrawal GK, Jung YH, Kubo A, Jwa NS, Tamogami S, Satoh K, Kikuchi S, Higashi T, et al.: Integrated transcriptomics, proteomics, and metabolomics analyses to survey ozone responses in the leaves of rice seedling. J Proteome Res 2008, 7:2980-2998.
• [3]Heijne WH, Kienhuis AS, van Ommen B, Stierum RH, Groten JP: Systems toxicology: applications of toxicogenomics, transcriptomics, proteomics and metabolomics in toxicology. Expert Rev Proteomics 2005, 2:767-780.
• [4]Ferrara CT, Wang P, Neto EC, Stevens RD, Bain JR, Wenner BR, Ilkayeva OR, Keller MP, Blasiole DA, Kendziorski C, et al.: Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling. PLoS Genet 2008, 4:e1000034.
• [5]Carrari F, Baxter C, Usadel B, Urbanczyk-Wochniak E, Zanor MI, Nunes-Nesi A, Nikiforova V, Centero D, Ratzka A, Pauly M, et al.: Integrated analysis of metabolite and transcript levels reveals the metabolic shifts that underlie tomato fruit development and highlight regulatory aspects of metabolic network behavior. Plant Physiol 2006, 142:1380-1396.
• [6]Xu EY, Perlina A, Vu H, Troth SP, Brennan RJ, Aslamkhan AG, Xu Q: Integrated pathway analysis of rat urine metabolic profiles and kidney transcriptomic profiles to elucidate the systems toxicology of model nephrotoxicants. Chem Res Toxicol 2008, 21:1548-1561.
• [7]Nam H, Chung BC, Kim Y, Lee K, Lee D: Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification. Bioinformatics 2009, 25:3151-3157.
• [8]Su G, Burant CF, Beecher CW, Athey BD, Meng F: Integrated metabolome and transcriptome analysis of the NCI60 dataset. BMC Bioinforma 2011, 12(1):36. BioMed Central Full Text
• [9]Gehlenborg N, O'Donoghue SI, Baliga NS, Goesmann A, Hibbs MA, Kitano H, Kohlbacher O, Neuweger H, Schneider R, Tenenbaum D, Gavin AC: Visualization of omics data for systems biology. Nat Methods 2010, 7:S56-68.
• [10]Junker BH, Klukas C, Schreiber F: VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinforma 2006, 7:109. BioMed Central Full Text
• [11]Hu Z, Hung JH, Wang Y, Chang YC, Huang CL, Huyck M, DeLisi C: VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res 2009, 37:W115-121.
• [12]Karnovsky A, Weymouth T, Hull T, Tarcea VG, Scardoni G, Laudanna C, Sartor MA, Stringer KA, Jagadish HV, Burant C, et al.: Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 2012, 28:373-380.
• [13]Pavlopoulos G, O'Donoghue S, Satagopam V, Soldatos T, Pafilis E, Schneider R: Arena3D: visualization of biological networks in 3D. BMC Syst Biol 2008, 2:104-104. BioMed Central Full Text
• [14]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13:2498-2504.
• [15]McGuffin MJ, Jurisica I: Interaction techniques for selecting and manipulating subgraphs in network visualizations. IEEE Trans Vis Comput Graph 2009, 15:937-944.
• [16]Barsky A, Munzner T, Gardy J, Kincaid R: Cerebral: visualizing multiple experimental conditions on a graph with biological context. IEEE Trans Vis Comput Graph 2008, 14:1253-1260.
• [17]Bader GD, Cary MP, Sander C: Pathguide: a pathway resource list. Nucleic Acids Res 2006, 34:D504-506.
• [18]Arakawa K, Kono N, Yamada Y, Mori H, Tomita M: KEGG-based pathway visualization tool for complex omics data. In Silico Biol 2005, 5:419-423.
• [19]Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27-30.
• [20]Garcia-Alcalde F, Garcia-Lopez F, Dopazo J, Conesa A: Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 2011, 27:137-139.
• [21]Neuweger H, Persicke M, Albaum SP, Bekel T, Dondrup M, Huser AT, Winnebald J, Schneider J, Kalinowski J, Goesmann A: Visualizing post genomics data-sets on customized pathway maps by ProMeTra-aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example. BMC Syst Biol 2009, 3:82. BioMed Central Full Text
• [22]Tokimatsu T, Sakurai N, Suzuki H, Ohta H, Nishitani K, Koyama T, Umezawa T, Misawa N, Saito K, Shibata D: KaPPA-view: a web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol 2005, 138:1289-1300.
• [23]Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M: MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 2004, 37:914-939.
• [24]Symons S, Zipplies C, Battke F, Nieselt K: Integrative systems biology visualization with MAYDAY. J Integr Bioinform 2010, 7:115.
• [25]Lüdemann A, Weicht D, Selbig J, Kopka J: PaVESy: Pathway Visualization and Editing System. Bioinformatics 2004, 20:2841-2844.
• [26]Ingenuity Pathway Analysis http://www.ingenuity.com/ webcite
• [27]Hoffmann R, Valencia A: A gene network for navigating the literature. Nat Genet 2004, 36:664-664.
• [28]iHOP http://www.ihop-net.org webcite
• [29]Romero P, Wagg J, Green M, Kaiser D, Krummenacker M, Karp P: Computational prediction of human metabolic pathways from the complete human genome. Genome Biology 2004, 6:R2. BioMed Central Full Text
• [30]da Huang W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009, 4:44-57.
• [31]Maglott D, Ostell J, Pruitt KD, Tatusova T: Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 2011, 39:D52-57.
• [32]Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, et al.: UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 2004, 32:D115-119.
• [33]Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research 2009, 37:W623-W633.
• [34]R Development Core Team: R: A Language and Environment for Statistical Computing. Austria: Vienna; 2010.
• [35]Warnes GR, Includes R soruce code and/or documentation contributed by (in alphabetical order): Bolker B, Bonebakker L, Gentleman R, Liaw WHA, Lumley T, Maechler M, Magnusson A, Moeller S, Schwartz M, Venables B: gplots: Various R programming tools for plotting data. 2010.
• [36]Zhang H, Yang Y: An algorithm for thorough background subtraction from high-resolution LC/MS data: application for detection of glutathione-trapped reactive metabolites. J Mass Spectrom 2008, 43:1181-1190.
• [37]Google Chart Tools https://developers.google.com/chart/ webcite
• [38]Zheng P-Z, Wang K-K, Zhang Q-Y, Huang Q-H, Du Y-Z, Zhang Q-H, Xiao D-K, Shen S-H, Imbeaud S, Eveno E, et al.: Systems analysis of transcriptome and proteome in retinoic acid/arsenic trioxide-induced cell differentiation/apoptosis of promyelocytic leukemia. Proceedings of the National Academy of Sciences of the United States of America 2005, 102:7653-7658.
• [39]Xia J, Wishart DS: MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 2010, 26:2342-2344.
• [40]Rivera OJ, Song CS, Centonze VE, Lechleiter JD, Chatterjee B, Roy AK: Role of the Promyelocytic Leukemia Body in the Dynamic Interaction between the Androgen Receptor and Steroid Receptor Coactivator-1 in Living Cells. Molecular Endocrinology 2003, 17:128-140.
• [41]Ueda T, Mawji NR, Bruchovsky N, Sadar MD: Ligand-independent Activation of the Androgen Receptor by Interleukin-6 and the Role of Steroid Receptor Coactivator-1 in Prostate Cancer Cells. Journal of Biological Chemistry 2002, 277:38087-38094.
• [42]Agoulnik IU, Vaid A, Bingman WE, Erdeme H, Frolov A, Smith CL, Ayala G, Ittmann MM, Weigel NL: Role of SRC-1 in the Promotion of Prostate Cancer Cell Growth and Tumor Progression. Cancer Research 2005, 65:7959-7967.
• [43]Harris MN, Ozpolat B, Abdi F, Gu S, Legler A, Mawuenyega KG, Tirado-Gomez M, Lopez-Berestein G, Chen X: Comparative proteomic analysis of all-trans-retinoic acid treatment reveals systematic posttranscriptional control mechanisms in acute promyelocytic leukemia. Blood 2004, 104:1314-1323.
• [44]Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, Kanai A, Hirasawa T, Naba M, Hirai K, Hoque A, et al.: Multiple High-Throughput Analyses Monitor the Response of E. coli to Perturbations 2007, 316:593-597.
• [45]Trauger SA, Kalisak E, Kalisiak J, Morita H, Weinberg MV, Menon AL, Ii Poole FL, Adams MWW, Siuzdak G: Correlating the Transcriptome, Proteome, and Metabolome in the Environmental Adaptation of a Hyperthermophile. Journal of Proteome Research 2008, 7:1027-1035.