Using biological networks to integrate, visualize and analyze genomics data

Genetics Selection Evolution

Volumn 48, Issue 1, 2016, Pages

  Charitou, Theodosia ^a,b,c   Bryan, Kenneth ^a   Lynn, David J ^a,d  

^a SOUTH AUSTRALIAN HEALTH AND MEDICAL RESEARCH INSTITUTE   (Australia)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

^c TEAGASC   (Ireland)

^d FLINDERS UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; BIOLOGICAL ANALYSIS; CONCEPTUAL FRAMEWORK; DATA SET; ECOSYSTEM STRUCTURE; GENE; GENOMICS; MUTATION; PHENOTYPE; TOPOLOGY;

ANIMAL; BIOLOGY; FACTUAL DATABASE; GENE ONTOLOGY; GENE REGULATORY NETWORK; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; PHENOTYPE; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM;

ANIMALS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; GENE ONTOLOGY; GENE REGULATORY NETWORKS; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84977616154     PISSN: 0999193X     EISSN: 12979686     Source Type: Journal    
DOI: 10.1186/s12711-016-0205-1     Document Type: Review

References (81)

1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25-9.
2. Rhee SY, Wood V, Dolinski K, Draghici S. Use and misuse of the gene ontology annotations. Nat Rev Genet. 2008;9:509-15.
3. Jantzen SG, Sutherland BJ, Minkley DR, Koop BF. GO trimming: systematically reducing redundancy in large gene ontology datasets. BMC Res Notes. 2011;4:267.
4. Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8:e1002375.
5. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27-30.
6. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014;42:D472-7.
7. Kutmon M, Riutta A, Nunes N, Hanspers K, Willighagen EL, Bohler A, et al. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 2016;44:D488-94.
8. Kelder T, Pico AR, Hanspers K, van Iersel MP, Evelo C, Conklin BR. Mining biological pathways using WikiPathways web services. PLoS One. 2009;4:e6447.
9. Lynn DJ, Winsor GL, Chan C, Richard N, Laird MR, Barsky A, et al. InnateDB: facilitating systems-level analyses of the mammalian innate immune response. Mol Syst Biol. 2008;4:218.
10. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4:P3.
11. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545-50.
12. Foroushani AB, Brinkman FS, Lynn DJ. Pathway-GPS and SIGORA: identifying relevant pathways based on the over-representation of their gene-pair signatures. Peer J. 2013;1:e229.
13. Breuer K, Foroushani AK, Laird MR, Chen C, Sribnaia A, Lo R, et al. InnateDB: systems biology of innate immunity and beyond-recent updates and continuing curation. Nucleic Acids Res. 2013;41:D1228-33.
14. Gardy JL, Lynn DJ, Brinkman FS, Hancock RE. Enabling a systems biology approach to immunology: focus on innate immunity. Trends Immunol. 2009;30:249-62.
15. Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56-68.
16. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2:2366-82.
17. Rolland T, Tasan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, et al. A proteome-scale map of the human interactome network. Cell. 2014;159:1212-26.
18. Koh GC, Porras P, Aranda B, Hermjakob H, Orchard SE. Analyzing protein-protein interaction networks. J Proteome Res. 2012;11:2014-31.
19. Kholodenko B, Yaffe MB, Kolch W. Computational approaches for analyzing information flow in biological networks. Sci Signal. 2012;5:re1.
20. Orchard S. Molecular interaction databases. Proteomics. 2012;12:1656-62.
21. Orchard S, Kerrien S, Abbani S, Aranda B, Bhate J, Bidwell S, et al. Protein interaction data curation: the international molecular exchange (IMEx) consortium. Nat Methods. 2012;9:345-50.
22. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447-52.
23. Aranda B, Blankenburg H, Kerrien S, Brinkman FS, Ceol A, Chautard E, et al. PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat Methods. 2011;8:528-9.
24. Morris JH, Knudsen GM, Verschueren E, Johnson JR, Cimermancic P, Greninger AL, et al. Affinity purification-mass spectrometry and network analysis to understand protein-protein interactions. Nat Protoc. 2014;9:2539-54.
25. Parrish JR, Gulyas KD, Finley RL Jr. Yeast two-hybrid contributions to interactome mapping. Curr Opin Biotechnol. 2006;17:387-93.
26. Villaveces JM, Jimenez RC, Porras P, Del-Toro N, Duesbury M, Dumousseau M, et al. Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study. Database (Oxford). 2015;2015:bau131.
27. Davis MJ, Shin CJ, Jing N, Ragan MA. Rewiring the dynamic interactome. Mol BioSyst. 2012;8:2054-66.
28. Orchard S, Salwinski L, Kerrien S, Montecchi-Palazzi L, Oesterheld M, Stumpflen V, et al. The minimum information required for reporting a molecular interaction experiment (MIMIx). Nat Biotechnol. 2007;25:894-8.
29. Lynn DJ, Chan C, Naseer M, Yau M, Lo R, Sribnaia A, et al. Curating the innate immunity interactome. BMC Syst Biol. 2010;4:117.
30. Lawless N, Reinhardt TA, Bryan K, Baker M, Pesch B, Zimmerman D, et al. MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model. G3 (Bethesda). 2014;4:957-71.
31. Frias S, Bryan K, Brinkman FS, Lynn DJ. CerebralWeb: a Cytoscape.js plug-into visualize networks stratified by subcellular localization. Database (Oxford). 2015;15:bav041.
32. Barsky A, Gardy JL, Hancock RE, Munzner T. Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics. 2007;23:1040-2.
33. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498-504.
34. Royer L, Reimann M, Andreopoulos B, Schroeder M. Unraveling protein networks with power graph analysis. PLoS Comput Biol. 2008;4:e1000108.
35. Theocharidis A, van Dongen S, Enright AJ, Freeman TC. Network visualization and analysis of gene expression data using BioLayout Express (3D). Nat Protoc. 2009;4:1535-50.
36. Xia J, Gill EE, Hancock RE. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10:823-44.
37. Kerrien S, Orchard S, Montecchi-Palazzi L, Aranda B, Quinn AF, Vinod N, et al. Broadening the horizon-level 2.5 of the HUPO-PSI format for molecular interactions. BMC Biol. 2007;5:44.
38. Barabasi AL. Scale-free networks: a decade and beyond. Science. 2009;325:412-3.
39. Goux WJ, Hooker TM Jr. Letter: contribution of tyrosine residues to the optical activity of ribonuclease S. J Am Chem Soc. 1975;97:1605-6.
40. Goymer P. Network biology: Why do we need hubs? Nat Rev Genet. 2008;9:650.
41. He X, Zhang J. Why do hubs tend to be essential in protein networks? PLoS Genet. 2006;2:e88.
42. Zotenko E, Mestre J, O'Leary DP, Przytycka TM. Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality. PLoS Comput Biol. 2008;4:e1000140.
43. Albert R, Jeong H, Barabasi AL. Error and attack tolerance of complex networks. Nature. 2000;406:378-82.
44. Dyer MD, Murali TM, Sobral BW. The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog. 2008;4:e32.
45. Schleker S, Trilling M. Data-warehousing of protein-protein interactions indicates that pathogens preferentially target hub and bottleneck proteins. Front Microbiol. 2013;4:51.
46. de Chassey B, Navratil V, Tafforeau L, Hiet MS, Aublin-Gex A, Agaugue S, et al. Hepatitis C virus infection protein network. Mol Syst Biol. 2008;4:230.
47. Jonsson PF, Bates PA. Global topological features of cancer proteins in the human interactome. Bioinformatics. 2006;22:2291-7.
48. Wachi S, Yoneda K, Wu R. Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics. 2005;21:4205-8.
49. Schaefer MH, Serrano L, Andrade-Navarro MA. Correcting for the study bias associated with protein-protein interaction measurements reveals differences between protein degree distributions from different cancer types. Front Genet. 2015;6:260.
50. Killick KE, Magee DA, Park SD, Taraktsoglou M, Browne JA, Conlon KM, et al. Key hub and bottleneck genes differentiate the macrophage response to virulent and attenuated mycobacterium bovis. Front Immunol. 2014;5:422.
51. Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004;5:101-13.
52. Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol. 2007;3:e59.
53. Hahn MW, Kern AD. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol. 2005;22:803-6.
54. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 1998;393:440-2.
55. Hartwell LH, Hopfield JJ, Leibler S, Murray AW. From molecular to modular cell biology. Nature. 1999;402:C47-52.
56. O'Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature. 2012;485:246-50.
57. Lage K, Greenway SC, Rosenfeld JA, Wakimoto H, Gorham JM, Segre AV, et al. Genetic and environmental risk factors in congenital heart disease functionally converge in protein networks driving heart development. Proc Natl Acad Sci USA. 2012;109:14035-40.
58. Freeman TC, Ivens A, Baillie JK, Beraldi D, Barnett MW, Dorward D, et al. A gene expression atlas of the domestic pig. BMC Biol. 2012;10:90.
59. Canovas A, Reverter A, DeAtley KL, Ashley RL, Colgrave ML, Fortes MR, et al. Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle. PLoS One. 2014;9:e102551.
60. Malik A, Lee EJ, Jan AT, Ahmad S, Cho KH, Kim J, et al. Network analysis for the identification of differentially expressed hub genes using myogenin knock-down muscle satellite cells. PLoS One. 2015;10:e0133597.
61. Lin CY, Chin CH, Wu HH, Chen SH, Ho CW, Ko MT. Hubba: hub objects analyzer - a framework of interactome hubs identification for network biology. Nucleic Acids Res. 2008;36:W438-43.
62. Mitra K, Carvunis AR, Ramesh SK, Ideker T. Integrative approaches for finding modular structure in biological networks. Nat Rev Genet. 2013;14:719-32.
63. Xia J, Benner MJ, Hancock RE. NetworkAnalyst-integrative approaches for protein-protein interaction network analysis and visual exploration. Nucleic Acids Res. 2014;42:W167-74.
64. Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002;18:S233-40.
65. Ghiassian SD, Menche J, Barabasi AL. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLoS Comput Biol. 2015;11:e1004120.
66. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015;347:1257601.
67. Przytycka TM, Singh M, Slonim DK. Toward the dynamic interactome: it's about time. Brief Bioinform. 2010;11:15-29.
68. Couzens AL, Knight JD, Kean MJ, Teo G, Weiss A, Dunham WH, et al. Protein interaction network of the mammalian Hippo pathway reveals mechanisms of kinase-phosphatase interactions. Sci Signal. 2013;6:rs15.
69. Jager S, Cimermancic P, Gulbahce N, Johnson JR, McGovern KE, Clarke SC, et al. Global landscape of HIV-human protein complexes. Nature. 2012;481:365-70.
70. Sahni N, Yi S, Taipale M, Fuxman Bass JI, Coulombe-Huntington J, Yang F, et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell. 2015;161:647-60.
71. Schokker D, de Koning DJ, Rebel JM, Smits MA. Shift in chicken intestinal gene association networks after infection with Salmonella. Comp Biochem Physiol Part D Genomics Proteomics. 2011;6:339-47.
72. Kovacs IA, Barabasi AL. Network science: Destruction perfected. Nature. 2015;524:38-9.
73. Kim YA, Przytycki JH, Wuchty S, Przytycka TM. Modeling information flow in biological networks. Phys Biol. 2011;8:035012.
74. Stojmirovic A, Yu YK. Information flow in interaction networks II: channels, path lengths, and potentials. J Comput Biol. 2012;19:379-403.
75. Stojmirovic A, Yu YK. ITM Probe: analyzing information flow in protein networks. Bioinformatics. 2009;25:2447-9.
76. Stojmirovic A, Bliskovsky A, Yu YK. CytoITMprobe: a network information flow plugin for Cytoscape. BMC Res Notes. 2012;5:237.
77. Ryan CJ, Cimermancic P, Szpiech ZA, Sali A, Hernandez RD, Krogan NJ. High-resolution network biology: connecting sequence with function. Nat Rev Genet. 2013;14:865-79.
78. Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature. 2004;430:88-93.
79. Agarwal S, Deane CM, Porter MA, Jones NS. Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks. PLoS Comput Biol. 2010;6:e1000817.
80. Chang X, Xu T, Li Y, Wang K. Dynamic modular architecture of protein-protein interaction networks beyond the dichotomy of 'date' and 'party' hubs. Sci Rep. 2013;3:1691.
81. Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, Camacho DM, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9:796-804.