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Network-Based Approaches for Multi-omics Integration

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Computational Methods and Data Analysis for Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2104))

Abstract

Network-based approach is rapidly emerging as a promising strategy to integrate and interpret different -omics datasets, including metabolomics. The first section of this chapter introduces the current progresses and main concepts in multi-omics integration. The second section provides an overview of the public resources available for creation of biological networks. The third section describes three common application scenarios including subnetwork identification, network-based enrichment analysis, and systems metabolomics. The section four introduces the concept of hierarchical community network analysis. The section five discusses different tools for network visualization. The chapter ends with a future perspective on multi-omics integration.

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References

  1. Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18(1):83

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Coleman WB (2017) Next-generation breast cancer omics. Am J Pathol 187(10):2130–2132

    Article  PubMed  Google Scholar 

  3. Mach N, Ramayo-Caldas Y, Clark A, Moroldo M, Robert C, Barrey E et al (2017) Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses. BMC Genomics 18(1):187

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Villar M, Ayllon N, Alberdi P, Moreno A, Moreno M, Tobes R et al (2015) Integrated metabolomics, transcriptomics and proteomics identifies metabolic pathways affected by Anaplasma phagocytophilum infection in tick cells. Mol Cell Proteomics 14(12):3154–3172

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rinschen MM, Ivanisevic J, Giera M, Siuzdak G (2019) Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol 20:353–367

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yan J, Risacher SL, Shen L, Saykin AJ (2018) Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Brief Bioinform 19(6):1370–1381

    CAS  PubMed  Google Scholar 

  7. Casci T (2012) Bioinformatics: next-generation omics. Nat Rev Genet 13(6):378

    Article  CAS  PubMed  Google Scholar 

  8. Rattray NJ, Deziel NC, Wallach JD, Khan SA, Vasiliou V, Ioannidis JP et al (2018) Beyond genomics: understanding exposotypes through metabolomics. Hum Genomics 12(1):4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D (2015) Methods of integrating data to uncover genotype–phenotype interactions. Nat Rev Genet 16(2):85

    Article  CAS  PubMed  Google Scholar 

  10. Chong J, Xia J (2017) Computational approaches for integrative analysis of the metabolome and microbiome. Metabolites 7(4):E62

    Article  PubMed  CAS  Google Scholar 

  11. Gligorijevic V, Przulj N (2015) Methods for biological data integration: perspectives and challenges. J R Soc Interface 12(112):20150571

    Article  PubMed  PubMed Central  Google Scholar 

  12. Meng C, Zeleznik OA, Thallinger GG, Kuster B, Gholami AM, Culhane AC (2016) Dimension reduction techniques for the integrative analysis of multi-omics data. Brief Bioinform 17(4):628–641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C, Castellani G et al (2016) Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17(2):S15

    Article  CAS  Google Scholar 

  14. Huang S, Chaudhary K, Garmire LX (2017) More is better: recent progress in multi-omics data integration methods. Front Genet 8:84

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tini G, Marchetti L, Priami C, Scott-Boyer M-P (2019) Multi-omics integration—a comparison of unsupervised clustering methodologies. Brief Bioinform 20(4):1269–1279

    Article  PubMed  Google Scholar 

  16. Barabasi AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12(1):56–68

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Vidal M, Cusick ME, Barabasi AL (2011) Interactome networks and human disease. Cell 144(6):986–998

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mitra K, Carvunis AR, Ramesh SK, Ideker T (2013) Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14(10):719–732

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P et al (2017) The Reactome pathway knowledgebase. Nucleic Acids Res 46 (Database issue):D481–D487

    Google Scholar 

  21. Caspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M et al (2017) The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res 46 (Database issue):D633–D639

    PubMed Central  Google Scholar 

  22. King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA et al (2015) BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44 (Database issue):D515–D522

    PubMed  PubMed Central  Google Scholar 

  23. Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Dräger A, Mih N et al (2018) Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36(3):272–281

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N et al (2017) WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res 46 (Database issue):D661–D667

    PubMed Central  Google Scholar 

  25. Altman T, Travers M, Kothari A, Caspi R, Karp PD (2013) A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinformatics 14(1):112

    Article  PubMed  PubMed Central  Google Scholar 

  26. Alcántara R, Axelsen KB, Morgat A, Belda E, Coudert E, Bridge A et al (2011) Rhea—a manually curated resource of biochemical reactions. Nucleic Acids Res 40(D1):D754–D760

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Davis AP, Grondin CJ, Johnson RJ, Sciaky D, McMorran R, Wiegers J et al (2018) The comparative toxicogenomics database: update 2019. Nucleic Acids Res 47(D1):D948–D954

    Article  PubMed Central  CAS  Google Scholar 

  28. Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M (2015) STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44 (Database issue):D380–D384

    PubMed  PubMed Central  Google Scholar 

  29. Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C et al (2011) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40 (Database issue):D841–D846

    PubMed  PubMed Central  Google Scholar 

  30. Chatr-Aryamontri A, Oughtred R, Boucher L, Rust J, Chang C, Kolas NK et al (2017) The BioGRID interaction database: 2017 update. Nucleic Acids Res 45 (Database issue):D369–D379

    Article  CAS  PubMed  Google Scholar 

  31. Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E et al (2011) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40 (Database issue):D857–D861

    PubMed  PubMed Central  Google Scholar 

  32. Breuer K, Foroushani AK, Laird MR, Chen C, Sribnaia A, Lo R et al (2013) InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation. Nucleic Acids Res 41 (Database issue):D1228–D1233

    Article  CAS  PubMed  Google Scholar 

  33. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J et al (2014) STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43 (Database issue):D447–D452

    PubMed  PubMed Central  Google Scholar 

  34. Khan A, Fornes O, Stigliani A, Gheorghe M, Castro-Mondragon JA, van der Lee R et al (2018) JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res 46 (Database issue):D260–D266

    Article  CAS  PubMed  Google Scholar 

  35. Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4:e05005

    Article  PubMed Central  Google Scholar 

  36. Han H, Shim H, Shin D, Shim JE, Ko Y, Shin J et al (2015) TRRUST: a reference database of human transcriptional regulatory interactions. Sci Rep 5:11432

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Chou C-H, Shrestha S, Yang C-D, Chang N-W, Lin Y-L, Liao K-W et al (2017) miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 46 (Database issue):D296–D302

    PubMed Central  Google Scholar 

  38. Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I et al (2017) DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Res 46 (Database issue):D239–D245

    PubMed Central  Google Scholar 

  39. Shoemaker RH (2006) The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6(10):813–823

    Article  CAS  PubMed  Google Scholar 

  40. Tomczak K, Czerwińska P, Wiznerowicz M (2015) The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol 19(1A):A68

    Google Scholar 

  41. The Integrative HMP (iHMP) Research Network Consortium (2014) The Integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16(3):276–289

    Article  CAS  Google Scholar 

  42. Laakso M, Kuusisto J, Stančáková A, Kuulasmaa T, Pajukanta P, Lusis AJ et al (2017) The metabolic syndrome in men study: a resource for studies of metabolic and cardiovascular diseases. J Lipid Res 58(3):481–493

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Tadaka S, Saigusa D, Motoike IN, Inoue J, Aoki Y, Shirota M et al (2017) jMorp: Japanese multi Omics reference panel. Nucleic Acids Res 46(D1):D551–D557

    Article  PubMed Central  CAS  Google Scholar 

  44. Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M et al (2011) The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet 7(2):e1002003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Perez-Riverol Y, Bai M, da Veiga Leprevost F, Squizzato S, Park YM, Haug K et al (2017) Discovering and linking public omics data sets using the omics discovery index. Nat Biotechnol 35(5):406–409

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Yugi K, Kubota H, Hatano A, Kuroda S (2016) Trans-omics: how to reconstruct biochemical networks across multiple ‘omic’layers. Trends Biotechnol 34(4):276–290

    Article  CAS  PubMed  Google Scholar 

  47. Zhou G, Xia J (2018) OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space. Nucleic Acids Res 46(W1):W514–W522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Creixell P, Reimand J, Haider S, Wu G, Shibata T, Vazquez M et al (2015) Pathway and network analysis of cancer genomes. Nat Methods 12(7):615–621

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Akhmedov M, Kedaigle A, Chong RE, Montemanni R, Bertoni F, Fraenkel E et al (2017) PCSF: an R-package for network-based interpretation of high-throughput data. PLoS Comput Biol 13(7):e1005694

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Tuncbag N, Gosline SJ, Kedaigle A, Soltis AR, Gitter A, Fraenkel E (2016) Network-based interpretation of diverse high-throughput datasets through the omics integrator software package. PLoS Comput Biol 12(4):e1004879

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Ideker T, Ozier O, Schwikowski B, Siegel AF (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(suppl_1):S233–S240

    Article  PubMed  Google Scholar 

  52. Khurana V, Peng J, Chung CY, Auluck PK, Fanning S, Tardiff DF et al (2017) Genome-scale networks link neurodegenerative disease genes to α-synuclein through specific molecular pathways. Cell Syst 4(2):157–170. e14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sychev ZE, Hu A, DiMaio TA, Gitter A, Camp ND, Noble WS et al (2017) Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism. PLoS Pathog 13(3):e1006256

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Beisser D, Klau GW, Dandekar T, Müller T, Dittrich MT (2010) BioNet: an R-package for the functional analysis of biological networks. Bioinformatics 26(8):1129–1130

    Article  CAS  PubMed  Google Scholar 

  55. Alcaraz N, List M, Dissing-Hansen M, Rehmsmeier M, Tan Q, Mollenhauer J et al (2016) Robust de novo pathway enrichment with KeyPathwayMiner 5. F1000Res 5:1531

    Article  PubMed  PubMed Central  Google Scholar 

  56. Anvar MS, Minuchehr Z, Shahlaei M, Kheitan S (2018) Gastric cancer biomarkers; a systems biology approach. Biochem Biophys Rep 13:141–146

    Google Scholar 

  57. Jha AK, Huang SC-C, Sergushichev A, Lampropoulou V, Ivanova Y, Loginicheva E et al (2015) Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42(3):419–430

    Article  CAS  PubMed  Google Scholar 

  58. Chen X, Liu M-X, Yan G-Y (2012) Drug–target interaction prediction by random walk on the heterogeneous network. Mol BioSyst 8(7):1970–1978

    Article  CAS  PubMed  Google Scholar 

  59. Liu Y, Zeng X, He Z, Zou Q (2017) Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM transactions on computational biology. Bioinformatics 14(4):905–915

    Google Scholar 

  60. Chen X, You Z-H, Yan G-Y, Gong D-W (2016) IRWRLDA: improved random walk with restart for lncRNA-disease association prediction. Oncotarget 7(36):57919

    PubMed  PubMed Central  Google Scholar 

  61. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim JS et al (2009) A novel signaling pathway impact analysis. Bioinformatics 25(1):75–82

    Article  CAS  PubMed  Google Scholar 

  63. Alexeyenko A, Lee W, Pernemalm M, Guegan J, Dessen P, Lazar V et al (2012) Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC Bioinformatics 13(1):226

    Article  PubMed  PubMed Central  Google Scholar 

  64. Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A (2012) EnrichNet: network-based gene set enrichment analysis. Bioinformatics 28(18):i451–i457

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Dettmer K, Aronov PA, Hammock BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26(1):51–78

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. da Silva RR, Dorrestein PC, Quinn RA (2015) Illuminating the dark matter in metabolomics. Proc Natl Acad Sci U S A 112(41):12549–12550

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382

    Article  CAS  PubMed  Google Scholar 

  68. Li S, Park Y, Duraisingham S, Strobel FH, Khan N, Soltow QA et al (2013) Predicting network activity from high throughput metabolomics. PLoS Comput Biol 9(7):e1003123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Xu X, Araki K, Li S, Han JH, Ye L, Tan WG et al (2014) Autophagy is essential for effector CD8(+) T cell survival and memory formation. Nat Immunol 15(12):1152–1161

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Li S, Todor A, Luo R (2016) Blood transcriptomics and metabolomics for personalized medicine. Comput Struct Biotechnol J 14:1–7

    Article  PubMed  CAS  Google Scholar 

  71. Stewart CJ, Embleton ND, Marrs ECL, Smith DP, Fofanova T, Nelson A et al (2017) Longitudinal development of the gut microbiome and metabolome in preterm neonates with late onset sepsis and healthy controls. Microbiome 5(1):75

    Article  PubMed  PubMed Central  Google Scholar 

  72. Huan T, Forsberg EM, Rinehart D, Johnson CH, Ivanisevic J, Benton HP et al (2017) Systems biology guided by XCMS online metabolomics. Nat Methods 14(5):461–462

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G et al (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46(W1):W486–W494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Pirhaji L, Milani P, Leidl M, Curran T, Avila-Pacheco J, Clish CB et al (2016) Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods 13(9):770

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Rohart F, Gautier B, Singh A, Lê Cao K-A (2017) mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol 13(11):e1005752

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Li S, Sullivan NL, Rouphael N, Yu T, Banton S, Maddur MS et al (2017) Metabolic phenotypes of response to vaccination in humans. Cell 169(5):862–877. e17

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Gardinassi LG, Arévalo-Herrera M, Herrera S, Cordy RJ, Tran V, Smith MR et al (2018) Integrative metabolomics and transcriptomics signatures of clinical tolerance to Plasmodium vivax reveal activation of innate cell immunity and T cell signaling. Redox Biol 17:158–170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Pavlopoulos GA, Malliarakis D, Papanikolaou N, Theodosiou T, Enright AJ, Iliopoulos I (2015) Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future. Gigascience 4(1):38

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Alcaraz N, Pauling J, Batra R, Barbosa E, Junge A, Christensen AG et al (2014) KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape. BMC Syst Biol 8(1):99

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Kutmon M, van Iersel MP, Bohler A, Kelder T, Nunes N, Pico AR et al (2015) PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol 11(2):e1004085

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Luo W, Pant G, Bhavnasi YK, Blanchard SG Jr, Brouwer C (2017) Pathview web: user friendly pathway visualization and data integration. Nucleic Acids Res 45(W1):W501–W508

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Garcia-Alcalde F, Garcia-Lopez F, Dopazo J, Conesa A (2010) Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 27(1):137–139

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Kuo T-C, Tian T-F, Tseng YJ (2013) 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol 7(1):64

    Article  PubMed  PubMed Central  Google Scholar 

  85. Sommer B, Baaden M, Krone M, Woods A (2018) From virtual reality to immersive analytics in Bioinformatics. J Integr Bioinform 15(2):20180043

    Article  PubMed Central  Google Scholar 

  86. Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC et al (2018) Multi-Omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol 14(6):e8124

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal, Complex Systems 1695(5):1–9

    Google Scholar 

  88. Hagberg A, Swart P, S Chult D (2008) Exploring network structure, dynamics, and function using NetworkX (No. LA-UR-08-05495; LA-UR-08-5495). Los Alamos National Lab.(LANL), Los Alamos, NM (United States)

    Google Scholar 

  89. Bastian M, Heymann S, & Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: Third international AAAI conference on weblogs and social media

    Google Scholar 

  90. Wanichthanarak K, Fan S, Grapov D, Barupal DK, Fiehn O, Orešič M (2017) Metabox: a toolbox for metabolomic data analysis, interpretation and integrative exploration. Plos One 12(1):e0171046

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Gao J, Tarcea VG, Karnovsky A, Mirel BR, Weymouth TE, Beecher CW, Cavalcoli JD, Athey BD, Omenn GS, Burant CF, Jagadish HV (2010) Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks. Bioinformatics 26(7):971–973

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work has been funded in part by the US National Institutes of Health via grants UH2 AI132345 (Li), R01 GM124061 (Yu), U2C ES030163 (Jones, Li, Morgan, Miller), U01 CA235493 (Li, Xia, Siuzdak), Genome Canada, Genome Quebec, and Canada Research Chairs program.

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Correspondence to Jianguo Xia .

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Zhou, G., Li, S., Xia, J. (2020). Network-Based Approaches for Multi-omics Integration. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_23

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  • DOI: https://doi.org/10.1007/978-1-0716-0239-3_23

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