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Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies

Zhang, Weiwen, Li, Feng, Nie, Lei·2010·Microbiology
DOI 10.1099/mic.0.034793-0
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Abstract

Recent advances in various ‘omics’ technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular ‘omics’ platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single ‘omics’ analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-‘omics’ approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous ‘omics’ datasets in various microbial systems and the results have demonstrated that the multi-‘omics’ approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental ‘omics’ approaches, recent application of the integrated ‘omics’ for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated ‘omics’ analyses. Online databases and bioinformatic infrastructure available for integrated ‘omics’ analyses are also briefly discussed.

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Research works printed but not linked: 143

  1. Adler, 2008, EGGanim: pathway animations for high-throughput data, Bioinformatics, 24, 588, 10.1093/bioinformatics/btm581 · DOI
  2. Alter, 2004, Integrative analysis of genomescale data by using pseudoinverse projection predicts novel correlation between DNA replication and RNA transcription, Proc Natl Acad Sci U S A, 101, 16577, 10.1073/pnas.0406767101 · DOI
  3. Askenazi, 2003, Integrating transcriptional and metabolite profiles to direct the engineering of lovastatin-producing fungal strains, Nat Biotechnol, 21, 150, 10.1038/nbt781 · DOI · Paper page
  4. Aubert, 1998, Characterization of the cytochromes c from Desulfovibrio desulfuricans G201, Biochem Biophys Res Commun, 242, 213, 10.1006/bbrc.1997.7852 · DOI
  5. Baggerman, 2005, Gel-based versus gel-free proteomics: a review, Comb Chem High Throughput Screen, 8, 669, 10.2174/138620705774962490 · DOI · Paper page
  6. Brown, 2006, Molecular dynamics of the Shewanella oneidensis response to chromate stress, Mol Cell Proteomics, 5, 1054, 10.1074/mcp.M500394-MCP200 · DOI · Paper page
  7. Bruscella, 2008, The use of chromatin immunoprecipitation to define PpsR binding activity in Rhodobacter sphaeroides 2.4.1, J Bacteriol, 190, 6817, 10.1128/JB.00719-08 · doi:10.1128/jb.00719-08
  8. Budde, 2006, Adaptation of Bacillus subtilis to growth at low temperature: a combined transcriptomic and proteomic appraisal, Microbiology, 152, 831, 10.1099/mic.0.28530-0 · DOI
  9. Butland, 2008, eSGA: E. coli synthetic genetic array analysis, Nat Methods, 5, 789, 10.1038/nmeth.1239 · DOI · Paper page
  10. Cardenas, 2008, New tools for discovering and characterizing microbial diversity, Curr Opin Biotechnol, 19, 544, 10.1016/j.copbio.2008.10.010 · DOI · Paper page
  11. Cascante, 2008, Metabolomics and fluxomics approaches, Essays Biochem, 45, 67, 10.1042/bse0450067 · DOI
  12. Cash, 2000, Proteomics in medical microbiology, Electrophoresis, 21, 1187, 10.1002/(SICI)1522-2683(20000401)21:6<1187::AID-ELPS1187>3.0.CO;2-F · doi:10.1002/(sici)1522-2683(20000401)21:6<1187::aid-elps1187>3.0.co;2-f
  13. Castrillo, 2003, An optimized protocol for metabolome analysis in yeast using direct infusion electrospray mass spectrometry, Phytochemistry, 62, 929, 10.1016/S0031-9422(02)00713-6 · DOI · Paper page
  14. Chen, 2007, Proteomic and transcriptomic analysis of extracellular proteins and mRNA levels in Thermobifida fusca grown on cellobiose and glucose, J Bacteriol, 189, 6260, 10.1128/JB.00584-07 · DOI
  15. Corbin, 2003, Toward a protein profile of Escherichia coli: comparison to its transcription profile, Proc Natl Acad Sci U S A, 100, 9232, 10.1073/pnas.1533294100 · DOI · Paper page
  16. De Keersmaecker, 2006, Integration of omics data: how well does it work for bacteria?, Mol Microbiol, 62, 1239, 10.1111/j.1365-2958.2006.05453.x · DOI
  17. Depuydt, 2009, An integrated genomics approach to define niche establishment by Rhodococcus fascians, Plant Physiol, 149, 1366, 10.1104/pp.108.131805 · DOI
  18. Díaz-Mejía, 2009, Computational and experimental approaches to chart the Escherichia coli cell-envelope-associated proteome and interactome, FEMS Microbiol Rev, 33, 66, 10.1111/j.1574-6976.2008.00141.x · DOI
  19. Dunn, 2008, Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes, Phys Biol, 5, 11001, 10.1088/1478-3975/5/1/011001 · DOI · Paper page
  20. Dunn, 2005, Measuring the metabolome: current analytical technologies, Analyst, 130, 606, 10.1039/b418288j · DOI · Paper page
  21. Elias, 2009, Expression profiling of hypothetical genes in Desulfovibrio vulgaris leads to improved functional annotation, Nucleic Acids Res, 37, 2926, 10.1093/nar/gkp164 · DOI
  22. Engels, 2008, The global repressor SugR controls expression of genes of glycolysis and of the l-lactate dehydrogenase LdhA in Corynebacterium glutamicum, J Bacteriol, 190, 8033, 10.1128/JB.00705-08 · doi:10.1128/jb.00705-08
  23. Fagan, 2007, A multivariate analysis approach to the integration of proteomic and gene expression data, Proteomics, 7, 2162, 10.1002/pmic.200600898 · DOI
  24. Fan, 2001, Variable selection via nonconcave penalized likelihood and its oracle properties, J Am Stat Assoc, 96, 1348, 10.1198/016214501753382273 · DOI · Paper page
  25. Faria-Campos, 2007, BNDb – Biomolecules Nucleus Database: an integrated proteomics and transcriptomics database, Genet Mol Res, 6, 937
  26. Fiehn, 2001, Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks, Comp Funct Genomics, 2, 155, 10.1002/cfg.82 · DOI · Paper page
  27. Forster, 2002, A functional genomics approach using metabolomics and in silico pathway analysis, Biotechnol Bioeng, 79, 703, 10.1002/bit.10378 · DOI
  28. Fraser, 2005, Application of microbial genomic science to advanced therapeutics, Annu Rev Med, 56, 459, 10.1146/annurev.med.56.062904.144853 · DOI
  29. Frias-Lopez, 2008, Microbial community gene expression in ocean surface waters, Proc Natl Acad Sci U S A, 105, 3805, 10.1073/pnas.0708897105 · DOI · Paper page
  30. Gao, 2006, Knock-out of SO1377 gene, which encodes the member of a conserved hypothetical bacterial protein family COG2268, results in alteration of iron metabolism, increased spontaneous mutation and hydrogen peroxide sensitivity in Shewanella oneidensis MR-1, BMC Genomics, 7, 76, 10.1186/1471-2164-7-76 · doi:10.1186/1471-2164-7-76
  31. Garcia, 2008, Separation and mass spectrometry in microbial metabolomics, Curr Opin Microbiol, 11, 233, 10.1016/j.mib.2008.04.002 · DOI
  32. Gilbert, 2008, Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities, PLoS One, 3, e3042, 10.1371/journal.pone.0003042 · DOI · Paper page
  33. Gingras, 2005, Advances in protein complex analysis using mass spectrometry, J Physiol, 563, 11, 10.1113/jphysiol.2004.080440 · DOI · Paper page
  34. Gonzalo-Asensio, 2008, PhoP: a missing piece in the intricate puzzle of Mycobacterium tuberculosis virulence, PLoS One, 3, e3496, 10.1371/journal.pone.0003496 · DOI · Paper page
  35. Graham, 2007, Microbial proteomics: a mass spectrometry primer for biologists, Microb Cell Fact, 6, 26, 10.1186/1475-2859-6-26 · DOI
  36. Grainger, 2004, Genomic studies with Escherichia coli MelR protein: applications of chromatin immunoprecipitation and microarrays, J Bacteriol, 186, 6938, 10.1128/JB.186.20.6938-6943.2004 · DOI
  37. Greenbaum, 2002, Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts, Bioinformatics, 18, 585, 10.1093/bioinformatics/18.4.585 · DOI · Paper page
  38. Gygi, 1999, Correlation between protein and mRNA abundance in yeast, Mol Cell Biol, 19, 1720, 10.1128/MCB.19.3.1720 · DOI · Paper page
  39. Haqqani, 2008, Quantitative protein profiling by mass spectrometry using label-free proteomics, Methods Mol Biol, 439, 241, 10.1007/978-1-59745-188-8_17 · DOI
  40. Hegde, 2003, Interplay of transcriptomics and proteomics, Curr Opin Biotechnol, 14, 647, 10.1016/j.copbio.2003.10.006 · DOI · Paper page
  41. Herring, 2005, Immobilization of Escherichia coli RNA polymerase and location of binding sites by use of chromatin immunoprecipitation and microarrays, J Bacteriol, 187, 6166, 10.1128/JB.187.17.6166-6174.2005 · DOI
  42. Hirai, 2005, Elucidation of gene-to-gene and metabolite-to-gene networks in Arabidopsis by integration of metabolomics and transcriptomics, J Biol Chem, 280, 25590, 10.1074/jbc.M502332200 · DOI · Paper page
  43. Hoefgen, 2008, Metabolomics integrated with transcriptomics: assessing systems response to sulfur-deficiency stress, Physiol Plant, 132, 190, 10.1111/j.1399-3054.2007.01012.x · DOI
  44. Horak, 2002, Global analysis of gene expression in yeast, Funct Integr Genomics, 2, 171, 10.1007/s10142-002-0065-3 · DOI
  45. Ideker, 2001, Integrated genomic and proteomic analyses of a systematically perturbed metabolic network, Science, 292, 929, 10.1126/science.292.5518.929 · DOI · Paper page
  46. Ishii, 2009, Multi-omics data-driven systems biology of E. coli, 41, 10.1007/978-1-4020-9394-4_3 · DOI
  47. Ishii, 2007, Multiple high-throughput analyses monitor the response of E. coli to perturbations, Science, 316, 593, 10.1126/science.1132067 · DOI · Paper page
  48. James, 1997, Protein identification in the post-genome era: the rapid rise of proteomics, Q Rev Biophys, 30, 279, 10.1017/S0033583597003399 · DOI · Paper page
  49. Jayapal, 2008, Uncovering genes with divergent mRNA-protein dynamics in Streptomyces coelicolor, PLoS One, 3, e2097, 10.1371/journal.pone.0002097 · DOI
  50. Jennings, 2009, Proteomic and transcriptomic analyses reveal genes upregulated by cis-dichloroethene in Polaromonas sp, strain JS666. Appl Environ Microbiol, 75, 3733, 10.1128/AEM.00031-09 · DOI
  51. Jones, 2004, An object model and database for functional genomics, Bioinformatics, 20, 1583, 10.1093/bioinformatics/bth130 · doi:10.1093/bioinformatics/bth130
  52. Joyce, 2006, The model organism as a system: integrating ‘omics’ data sets, Nat Rev Mol Cell Biol, 7, 198, 10.1038/nrm1857 · DOI · Paper page
  53. Jurgen, 2001, Proteome and transcriptome based analysis of Bacillus subtilis cells overproducing an insoluble heterologous protein, Appl Microbiol Biotechnol, 55, 326, 10.1007/s002530000531 · doi:10.1007/s002530000531
  54. Kagnoff, 2001, Analysis of host responses to microbial infection using gene expression profiling, Curr Opin Microbiol, 4, 246, 10.1016/S1369-5274(00)00198-3 · DOI
  55. Kandpal, 2009, The era of omics unlimited, Biotechniques, 46, 351, 10.2144/000113137 · doi:10.2144/000113137
  56. Kim, 2008, Predicting the interactome of Xanthomonas oryzae pathovar oryzae for target selection and DB service, BMC Bioinformatics, 9, 41, 10.1186/1471-2105-9-41 · DOI
  57. Krogan, 2006, Global landscape of protein complexes in the yeast Saccharomyces cerevisiae, Nature, 440, 637, 10.1038/nature04670 · DOI · Paper page
  58. Kromer, 2004, In-depth profiling of lysine-producing Corynebacterium glutamicum by combined analysis of the transcriptome, metabolome, and fluxome, J Bacteriol, 186, 1769, 10.1128/JB.186.6.1769-1784.2004 · DOI · Paper page
  59. Kyrpides, 2009, Fifteen years of microbial genomics: meeting the challenges and fulfilling the dream, Nat Biotechnol, 27, 627, 10.1038/nbt.1552 · DOI
  60. Lacerda, 2009, Environmental proteomics: applications of proteome profiling in environmental microbiology and biotechnology, Brief Funct Genomic Proteomic, 8, 75, 10.1093/bfgp/elp005 · DOI
  61. Lê Cao, 2008, A sparse PLS for variable selection when integrating omics data, Stat Appl Genet Mol Biol, 7
  62. Lê Cao, 2009, Sparse canonical methods for biological data integration: application to a cross-platform study, BMC Bioinformatics, 10, 34, 10.1186/1471-2105-10-34 · DOI · Paper page
  63. Lee, 2003, Global analyses of transcriptomes and proteomes of a parent strain and an l-threonine-overproducing mutant strain, J Bacteriol, 185, 5442, 10.1128/JB.185.18.5442-5451.2003 · DOI
  64. Lin, 2007, Systems biology approach to integrative comparative genomics, Expert Rev Proteomics, 4, 107, 10.1586/14789450.4.1.107 · DOI
  65. Mader, 2002a, Transcriptome and proteome analysis of Bacillus subtilis gene expression modulated by amino acid availability, J Bacteriol, 184, 4288, 10.1128/JB.184.15.4288-4295.2002 · DOI
  66. Mader, 2002b, Bacillus subtilis functional genomics: genome-wide analysis of the DegS-DegU regulon by transcriptomics and proteomics, Mol Genet Genomics, 268, 455, 10.1007/s00438-002-0774-2 · DOI
  67. Maraziotis, 2007, Growing functional modules from a seed protein via integration of protein interaction and gene expression data, BMC Bioinformatics, 8, 408, 10.1186/1471-2105-8-408 · DOI
  68. Martínez-Cruz, 2003, GARBAN: genomic analysis and rapid biological annotation of cDNA microarray and proteomic data, Bioinformatics, 19, 2158, 10.1093/bioinformatics/btg291 · DOI
  69. May, 2009, ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii, BMC Genomics, 10, 209, 10.1186/1471-2164-10-209 · DOI
  70. Medini, 2008, Microbiology in the post-genomic era, Nat Rev Microbiol, 6, 419, 10.1038/nrmicro1901 · DOI · Paper page
  71. Mehra, 2003, Insights into the relation between mRNA and protein expression patterns, I. Theoretical considerations. Biotechnol Bioeng, 84, 822, 10.1002/bit.10860 · DOI
  72. Misra, 2005, EchoBASE: an integrated post-genomic database for Escherichia coli, Nucleic Acids Res, 33, D329, 10.1093/nar/gki028 · DOI
  73. Missiuro, 2009, Information flow analysis of interactome networks, PLOS Comput Biol, 5, e1000350, 10.1371/journal.pcbi.1000350 · DOI · Paper page
  74. Mogilevskaya, 2009, Kinetic modeling as a tool to integrate multilevel dynamic experimental data, Methods Mol Biol, 563, 197, 10.1007/978-1-60761-175-2_11 · doi:10.1007/978-1-60761-175-2_11
  75. Mootha, 2003a, Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria, Cell, 115, 629, 10.1016/S0092-8674(03)00926-7 · DOI · Paper page
  76. Mootha, & other authors (2003b). Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics, Proc Natl Acad Sci U S A, 100, 605, 10.1073/pnas.242716699 · DOI · Paper page
  77. Morgenthal, 2006, Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation, Biosytems, 82, 108, 10.1016/j.biosystems.2005.05.017 · DOI
  78. Morgenthal, 2007, Integrative profiling of metabolites and proteins: improving pattern recognition and biomarker selection for systems level approaches, Methods Mol Biol, 358, 57, 10.1007/978-1-59745-244-1_4 · doi:10.1007/978-1-59745-244-1_4
  79. Mukhopadhyay, 2006, Salt stress in Desulfovibrio vulgaris Hildenborough: an integrated genomics approach, J Bacteriol, 188, 4068, 10.1128/JB.01921-05 · DOI · Paper page
  80. Mukhopadhyay, 2007, Cell-wide responses to low-oxygen exposure in Desulfovibrio vulgaris Hildenborough, J Bacteriol, 189, 5996, 10.1128/JB.00368-07 · DOI
  81. Nie, 2006a, Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis, Genetics, 174, 2229, 10.1534/genetics.106.065862 · DOI · Paper page
  82. Nie, 2006b, Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: a multiple regression to identify sources of variations, Biochem Biophys Res Commun, 339, 603, 10.1016/j.bbrc.2005.11.055 · DOI · Paper page
  83. Nie, 2006c, Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins, Bioinformatics, 22, 1641, 10.1093/bioinformatics/btl134 · DOI
  84. Nie, 2007, Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications, Crit Rev Biotechnol, 27, 63, 10.1080/07388550701334212 · DOI · Paper page
  85. Nie, 2008, Statistical application and challenges in global gel-free proteomic analysis by mass spectrometry, Crit Rev Biotechnol, 28, 297, 10.1080/07388550802543158 · DOI
  86. Nierman, 2000, Microbial genome sequencing 2000: new insights into physiology, evolution and expression analysis, Res Microbiol, 151, 79, 10.1016/S0923-2508(00)00125-X · doi:10.1016/s0923-2508(00)00125-x
  87. Norbeck, 2006, Proteomic approaches to bacterial differentiation, J Microbiol Methods, 67, 473, 10.1016/j.mimet.2006.04.024 · doi:10.1016/j.mimet.2006.04.024
  88. Nunez, 2006, DNA microarray and proteomic analyses of the RpoS regulon in Geobacter sulfurreducens, J Bacteriol, 188, 2792, 10.1128/JB.188.8.2792-2800.2006 · DOI
  89. O'Farrell, 1975, High resolution two-dimensional electrophoresis of proteins, J Biol Chem, 250, 4007, 10.1016/S0021-9258(19)41496-8 · DOI · Paper page
  90. Oliver, 1998, Systematic functional analysis of the yeast genome, Trends Biotechnol, 16, 373, 10.1016/S0167-7799(98)01214-1 · DOI · Paper page
  91. Ou, 2005, Integrative genomic, transcriptional, and proteomic diversity in natural isolates of the human pathogen Burkholderia pseudomallei, J Bacteriol, 187, 4276, 10.1128/JB.187.12.4276-4285.2005 · DOI
  92. Ozsolak, 2009, Direct RNA sequencing, Nature, 461, 814, 10.1038/nature08390 · DOI · Paper page
  93. Park, 2005, Global physiological understanding and metabolic engineering of microorganisms based on omics studies, Appl Microbiol Biotechnol, 68, 567, 10.1007/s00253-005-0081-z · doi:10.1007/s00253-005-0081-z
  94. Parrish, 2007, A proteome-wide protein interaction map for Campylobacter jejuni, Genome Biol, 8, R130, 10.1186/gb-2007-8-7-r130 · DOI · Paper page
  95. Pir, 2006, Integrative investigation of metabolic and transcriptomic data, BMC Bioinformatics, 7, 203, 10.1186/1471-2105-7-203 · DOI
  96. Raamsdonk, 2001, A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations, Nat Biotechnol, 19, 45, 10.1038/83496 · DOI · Paper page
  97. Ren, 2000, Genome-wide location and function of DNA-binding proteins, Science, 290, 2306, 10.1126/science.290.5500.2306 · DOI · Paper page
  98. Rocha, 2008, The organization of the bacterial genome, Annu Rev Genet, 42, 211, 10.1146/annurev.genet.42.110807.091653 · DOI · Paper page
  99. Rodriguez-Llorente, 2009, The Symbiosis Interactome: a computational approach reveals novel components, functional interactions and modules in Sinorhizobium meliloti, BMC Syst Biol, 3, 63, 10.1186/1752-0509-3-63 · DOI
  100. Scherl, 2005, Correlation of proteomic and transcriptomic profiles of Staphylococcus aureus during the post-exponential phase of growth, J Microbiol Methods, 60, 247, 10.1016/j.mimet.2004.09.017 · DOI
  101. Scherl, 2006, Exploring glycopeptide-resistance in Staphylococcus aureus: a combined proteomics and transcriptomics approach for the identification of resistance-related markers, BMC Genomics, 7, 296, 10.1186/1471-2164-7-296 · DOI
  102. Schilling, 1999, Toward metabolic phenomics: analysis of genomic data using flux balances, Biotechnol Prog, 15, 288, 10.1021/bp9900357 · DOI · Paper page
  103. Schmid, 2007, The anatomy of microbial cell state transitions in response to oxygen, Genome Res, 17, 1399, 10.1101/gr.6728007 · DOI
  104. Schoolnik, 2001, The accelerating convergence of genomics and microbiology, Genome Biol, 2, 10.1186/gb-2001-2-4-reports4009 · doi:10.1186/gb-2001-2-4-reports4009
  105. Sharan, 2006, Modeling cellular machinery through biological network comparison, Nat Biotechnol, 24, 427, 10.1038/nbt1196 · DOI · Paper page
  106. Singh, 2006, Transcriptomics, proteomics and interactomics: unique approaches to track the insights of bioremediation, Brief Funct Genomic Proteomic, 4, 355, 10.1093/bfgp/eli006 · DOI
  107. Soga, 2003, Quantitative metabolome analysis using capillary electrophoresis mass spectrometry, J Proteome Res, 2, 488, 10.1021/pr034020m · DOI · Paper page
  108. Sonck, 2009, The proteome of Salmonella typhimurium grown under in vivo-mimicking conditions, Proteomics, 9, 565, 10.1002/pmic.200700476 · DOI
  109. Stedtfeld, 2008, Development and experimental validation of a predictive threshold cycle equation for quantification of virulence and marker genes by high-throughput nanoliter-volume PCR on the OpenArray platform, Appl Environ Microbiol, 74, 3831, 10.1128/AEM.02743-07 · DOI
  110. Steinfath, 2007, Integrated data analysis for genome-wide research, EXS, 97, 309
  111. Suzuki, 2006, The heat shock response of Synechocystis sp. PCC 6803 analysed by transcriptomics and proteomics, J Exp Bot, 57, 1573, 10.1093/jxb/erj148 · DOI
  112. Tang, 2009a, Advances in analysis of microbial metabolic fluxes via 13C isotopic labeling, Mass Spectrom Rev, 28, 362, 10.1002/mas.20191 · DOI
  113. Tang, 2009b, Metabolic flux analysis of Shewanella spp. reveals evolutionary robustness in central carbon metabolism, Biotechnol Bioeng, 102, 1161, 10.1002/bit.22129 · DOI
  114. Ter Kuile, 2001, Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway, FEBS Lett, 500, 169, 10.1016/S0014-5793(01)02613-8 · DOI · Paper page
  115. Tong, 2004, Global mapping of the yeast genetic interaction network, Science, 303, 808, 10.1126/science.1091317 · DOI · Paper page
  116. Torres-García, 2009, Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins, Bioinformatics, 25, 1905, 10.1093/bioinformatics/btp325 · DOI
  117. Toya, 2007, Direct measurement of isotopomer of intracellular metabolites using capillary electrophoresis time-of-flight mass spectrometry for efficient metabolic flux analysis, J Chromatogr A, 134, 10.1016/j.chroma.2007.04.011 · doi:10.1016/j.chroma.2007.04.011
  118. Trauger, 2008, Correlating the transcriptome, proteome, and metabolome in the environmental adaptation of a hyperthermophile, J Proteome Res, 7, 1027, 10.1021/pr700609j · DOI
  119. Tweeddale, 1998, Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool (metabolome) analysis, J Bacteriol, 180, 5109, 10.1128/JB.180.19.5109-5116.1998 · DOI · Paper page
  120. Tweeddale, 1999, Assessing the effect of reactive oxygen species on Escherichia coli using a metabolome approach, Redox Rep, 4, 237, 10.1179/135100099101534954 · DOI
  121. Typas, 2008, High-throughput, quantitative analyses of genetic interactions in E. coli, Nat Methods, 5, 781, 10.1038/nmeth.1240 · DOI · Paper page
  122. Urbanczyk-Wochniak, 2003, Parallel analysis of transcript and metabolic profiles: a new approach in systems biology, EMBO Rep, 4, 989, 10.1038/sj.embor.embor944 · DOI · Paper page
  123. Urbanczyk-Wochniak, 2007, Integrating profiling data: using linear correlation to reveal coregulation of transcript and metabolites, Methods Mol Biol, 358, 77, 10.1007/978-1-59745-244-1_5 · doi:10.1007/978-1-59745-244-1_5
  124. Uyar, 2009, Differential binding profiles of StpA in wild-type and hns mutant cells: a comparative analysis of cooperative partners by chromatin immunoprecipitation-microarray analysis, J Bacteriol, 191, 2388, 10.1128/JB.01594-08 · DOI
  125. van der Werf, 2005, Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets, J Ind Microbiol Biotechnol, 32, 234, 10.1007/s10295-005-0231-4 · DOI
  126. Wan, 2004, Transcriptomic and proteomic characterization of the Fur modulon in the metal-reducing bacterium Shewanella oneidensis, J Bacteriol, 186, 8385, 10.1128/JB.186.24.8385-8400.2004 · DOI
  127. Ward, 2005, How genomics has affected the concept of microbiology, Curr Opin Microbiol, 8, 564, 10.1016/j.mib.2005.08.011 · DOI
  128. Washburn, 2003, Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae, Proc Natl Acad Sci U S A, 100, 3107, 10.1073/pnas.0634629100 · DOI · Paper page
  129. Waters, 2006, Data merging for integrated microarray and proteomic analysis, Brief Funct Genomic Proteomic, 5, 261, 10.1093/bfgp/ell019 · DOI
  130. Watson, 2005, ProGenExpress: visualization of quantitative data on prokaryotic genomes, BMC Bioinformatics, 6, 98, 10.1186/1471-2105-6-98 · doi:10.1186/1471-2105-6-98
  131. Webb-Robertson, 2009, A Bayesian integration model of high-throughput proteomics and metabolomics data for improved early detection of microbial infections, Pac Symp Biocomput, 451
  132. Weckwerth, 2005, Metabolomics: from pattern recognition to biological interpretation, Drug Discov Today, 10, 1551, 10.1016/S1359-6446(05)03609-3 · DOI · Paper page
  133. Weckwerth, 2004, Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks, Proteomics, 4, 78, 10.1002/pmic.200200500 · DOI · Paper page
  134. Wienkoop, 2008, Integration of metabolomic and proteomic phenotypes, Mol Cell Proteomics, 7, 1725, 10.1074/mcp.M700273-MCP200 · DOI
  135. Wilkins, 1996, From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis, Biotechnology (N Y), 14, 61
  136. Wolff, 2006, Gel-free and gel-based proteomics in Bacillus subtilis: a comparative study, Mol Cell Proteomics, 5, 1183, 10.1074/mcp.M600069-MCP200 · DOI
  137. Yan, 2008, Quantitative proteomic analysis to profile dynamic changes in the spatial distribution of cellular proteins, Methods Mol Biol, 432, 389, 10.1007/978-1-59745-028-7_26 · DOI
  138. Yang, 2009, Transcriptomic and metabolomic profiling of Zymomonas mobilis during aerobic and anaerobic fermentations, BMC Genomics, 10, 34, 10.1186/1471-2164-10-34 · DOI
  139. Ye, 2001, Applications of DNA microarrays in microbial systems, J Microbiol Methods, 47, 257, 10.1016/S0167-7012(01)00308-6 · DOI · Paper page
  140. Yoon, 2003, Combined transcriptome and proteome analysis of Escherichia coli during high cell density culture, Biotechnol Bioeng, 81, 753, 10.1002/bit.10626 · DOI
  141. Zhang, 2006a, A proteomic view of Desulfovibrio vulgaris metabolism as determined by liquid chromatography coupled with tandem mass spectrometry, Proteomics, 6, 4286, 10.1002/pmic.200500930 · DOI
  142. Zhang, 2006b, Global transcriptomic analysis of Desulfovibrio vulgaris on different electron donors, Antonie Van Leeuwenhoek, 89, 221, 10.1007/s10482-005-9024-z · DOI
  143. Zhao, 2008, Insights into environmental bioremediation by microorganisms through functional genomics and proteomics, Proteomics, 8, 874, 10.1002/pmic.200701005 · DOI

Cited by

See all 494

Metadata

Authors
Zhang, W., Li, F., & Nie, L.
Venue
Microbiology
Publisher
Microbiology Society
Published
2010-02-01
Volume
156
Issue
2
Pages
287-301
ISSN
  • 1350-0872 (ISSN-L)
  • 1465-2080
DOI
10.1099/mic.0.034793-0
OpenAlex
W2171214502
Cited by
494
References
8
FWCI
9.96
Type
journal article
Language
en
Open access
closed

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This page
CoChat. (2026). Citation check: Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. https://app.cochat.ai/papers/10.1099/mic.0.034793-0
The paper
Zhang, W., Li, F., & Nie, L. (2010). Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. Microbiology. https://doi.org/10.1099/mic.0.034793-0

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.