Correction to: CompNet: A GUI based tool for comparison of multiple biological interaction networks (BMC Bioinformatics (2016) 17 (185) DOI: 10.1186/s12859-016-1013-x);CompNet: A GUI based tool for comparison of multiple biological interaction networks

BMC Bioinformatics

Volumn 17, Issue 1, 2016, Pages

  Kuntal, Bhusan K ^a   Dutta, Anirban ^a   Mande, Sharmila S ^a  

^a TATA CONSULTANCY SERVICES   (India)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL SYSTEMS; GENE EXPRESSION; GRAPHICAL USER INTERFACES; PROTEINS; VISUALIZATION;

BIOLOGICAL INTERACTIONS; COMMUNITY COMPOSITION; COMPLEX BIOLOGICAL SYSTEMS; ENVIRONMENTAL CONDITIONS; INTERACTION PATTERN; INTERACTIVE VISUALIZATIONS; MYCOBACTERIUM TUBERCULOSIS; NETWORK VISUALIZATION;

DATA MINING;

GENE EXPRESSION PROFILING; HUMAN; MYCOBACTERIUM TUBERCULOSIS; PHYSIOLOGY; PROTEIN ANALYSIS; PROTEIN PROTEIN INTERACTION; SOFTWARE;

GENE EXPRESSION PROFILING; HUMANS; MYCOBACTERIUM TUBERCULOSIS; PROTEIN INTERACTION MAPPING; PROTEIN INTERACTION MAPS; SOFTWARE;

EID: 84964325461     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-019-3168-8     Document Type: Erratum

References (61)

1. Tomar N, De RK. Comparing methods for metabolic network analysis and an application to metabolic engineering. Gene. 2013;521:1-14.
2. Ebenhöh O, Handorf T, Heinrich R. A cross species comparison of metabolic network functions. Genome Inform. 2005;16:203-13.
3. Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538-50.
4. Ghosh TS, Sen Gupta S, Bhattacharya T, Yadav D, Barik A, Chowdhury A, et al. Gut microbiomes of Indian children of varying nutritional status. PLoS One. 2014;9:e95547.
5. Ganju P, Nagpal S, Mohammed MH, Nishal Kumar P, Pandey R, Natarajan VT, et al. Microbial community profiling shows dysbiosis in the lesional skin of Vitiligo subjects. Sci Rep. 2016;6:18761.
6. Kuntal BK, Ghosh TS, Mande SS. Community-analyzer: a platform for visualizing and comparing microbial community structure across microbiomes. Genomics. 2013;102:409-18.
7. Xia J, Benner MJ, Hancock REW. NetworkAnalyst - integrative approaches for protein-protein interaction network analysis and visual exploration. Nucl Acids Res. 2014;42(Web Server issue):W167-74.
8. Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2005;122:957-68.
9. Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol. 2008;9:770-80.
10. Das C, Dutta A, Rajasingh H, Mande SS. Understanding the sequential activation of Type III and Type VI Secretion Systems in Salmonella typhimurium using Boolean modeling. Gut Pathog. 2013;5:28.
11. Liang Z, Xu M, Teng M, Niu L. NetAlign: a web-based tool for comparison of protein interaction networks. Bioinformatics. 2006;22:2175-7.
12. Dost B, Shlomi T, Gupta N, Ruppin E, Bafna V, Sharan R. QNet: a tool for querying protein interaction networks. J Comput Biol. 2008;15:913-25.
13. Chen M, Hofestädt R. PathAligner: metabolic pathway retrieval and alignment. Appl Bioinformatics. 2004;3:241-52.
14. Tian Y, McEachin RC, Santos C, States DJ, Patel JM. SAGA: a subgraph matching tool for biological graphs. Bioinformatics. 2007;23:232-9.
15. Lena PD, Wu G, Martelli PL, Casadio R, Nardini C. MIMO: an efficient tool for molecular interaction maps overlap. BMC Bioinformatics. 2013;14:159.
16. Assenov Y, Ramírez F, Schelhorn S-E, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics. 2008;24:282-4.
17. Yu H, Zhu X, Greenbaum D, Karro J, Gerstein M. TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics. Nucleic Acids Res. 2004;32:328-37.
18. 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.
19. Doncheva NT, Assenov Y, Domingues FS, Albrecht M. Topological analysis and interactive visualization of biological networks and protein structures. Nat Protoc. 2012;7:670-85.
20. Hao Y, Merkoulovitch A, Vlasblom J, Pu S, Turinsky AL, Roudeva D, et al. OrthoNets: simultaneous visual analysis of orthologs and their interaction neighborhoods across different organisms. Bioinformatics. 2011;27:883-4.
21. Wilkinson L. Exact and approximate area-proportional circular Venn and Euler diagrams. IEEE Trans Vis Comput Graph. 2012;18:321-31.
22. Woźniak M, Tiuryn J, Dutkowski J. MODEVO: exploring modularity and evolution of protein interaction networks. Bioinformatics. 2010;26:1790-1.
23. Cytoscape App Store - Venn and Euler Diagrams. http://apps.cytoscape.org/apps/vennandeulerdiagrams. Accessed 15 Mar 2015.
24. Cytoscape App Store - PINA4MS. http://apps.cytoscape.org/apps/pina4ms. Accessed 15 Mar 2015.
25. Cytoscape App Store - NetTwo. http://apps.cytoscape.org/apps/nettwo. Accessed 15 Mar 2015.
26. Cytoscape App Store - NetworkEvolution. http://apps.cytoscape.org/apps/networkevolution. Accessed 15 Mar 2015.
27. Micale G, Continella A, Ferro A, Giugno R, Pulvirenti A. GASOLINE: a Cytoscape app for multiple local alignment of PPI networks. F1000Res. 2014;3:140.
28. Hao D, Li C. The dichotomy in degree correlation of biological networks. PLoS One. 2011;6:e28322.
29. Jeong H, Mason SP, Barabási AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411:41-2.
30. Manimaran P, Hegde SR, Mande SC. Prediction of conditional gene essentiality through graph theoretical analysis of genome-wide functional linkages. Mol Biosyst. 2009;5:1936-42.
31. Yellaboina S, Goyal K, Mande SC. Inferring genome-wide functional linkages in E. coli by combining improved genome context methods: comparison with high-throughput experimental data. Genome Res. 2007;17:527-35.
32. Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, et al. Using graph theory to analyze biological networks. BioData Min. 2011;4:10.
33. Karim AF, Chandra P, Chopra A, Siddiqui Z, Bhaskar A, Singh A, et al. Express path analysis identifies a tyrosine kinase Src-centric network regulating divergent host responses to Mycobacterium tuberculosis infection. J Biol Chem. 2011;286:40307-19.
34. Csardi G, Nepusz T. The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006.
35. Sprinthall R C. Basic statistical analysis. 9th ed. 2011. New York: Pearson Education.
36. Levandowsky M, Winter D. Distance between Sets. Nature. 1971;234:34-5.
37. Felsenstein J. PHYLIP - Phylogeny inference package (Version 3.2). Cladistics. 1989;5:164-6.
38. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003;31:258-61.
39. GeneCards - The Human Gene Compendium v3.12. 2015. http://www.genecards.org. Accessed 15 Mar 2015.
40. Phani NM, Guddattu V, Bellampalli R, Seenappa V, Adhikari P, Nagri SK, et al. Population specific impact of genetic variants in KCNJ11 gene to type 2 diabetes: a case-control and meta-analysis study. PLoS One. 2014;9:e107021.
41. Burg ED, Remillard CV, Yuan JX-J. K+ channels in apoptosis. J Membr Biol. 2006;209:3-20.
42. Mita AC, Mita MM, Nawrocki ST, Giles FJ. Survivin: key regulator of mitosis and apoptosis and novel target for cancer therapeutics. Clin Cancer Res. 2008;14:5000-5.
43. Jia L, Li B, Yu H. The Bub1-Plk1 kinase complex promotes spindle checkpoint signalling through Cdc20 phosphorylation. Nat Commun. 2016;7:10818.
44. Kanatsuna N, Taneera J, Vaziri-Sani F, Wierup N, Larsson HE, Delli A, et al. Autoimmunity against INS-IGF2 protein expressed in human pancreatic islets. J Biol Chem. 2013;288:29013-23.
45. Liu Z, Gan L, Zhou Z, Jin W, Sun C. SOCS3 promotes inflammation and apoptosis via inhibiting JAK2/STAT3 signaling pathway in 3T3-L1 adipocyte. Immunobiology. 2015;220:947-53.
46. Wang K, Brems JJ, Gamelli RL, Holterman A-X. Foxa2 may modulate hepatic apoptosis through the cIAP1 pathway. Cell Signal. 2013;25:867-74.
47. Rivera A, Mavila A, Bayless KJ, Davis GE, Maxwell SA. Cyclin A1 is a p53-induced gene that mediates apoptosis, G2/M arrest, and mitotic catastrophe in renal, ovarian, and lung carcinoma cells. Cell Mol Life Sci. 2006;63:1425-39.
48. Rodriguez R, Meuth M. Chk1 and p21 cooperate to prevent apoptosis during DNA replication fork stress. Mol Biol Cell. 2006;17:402-12.
49. Duursma A, Agami R. p53-dependent regulation of Cdc6 protein stability controls cellular proliferation. Mol Cell Biol. 2005;25:6937-47.
50. Pediconi N, Ianari A, Costanzo A, Belloni L, Gallo R, Cimino L, et al. Differential regulation of E2F1 apoptotic target genes in response to DNA damage. Nat Cell Biol. 2003;5:552-8.
51. Rana S, Besson G, Cook DG, Rucker J, Smyth RJ, Yi Y, et al. Role of CCR5 in infection of primary macrophages and lymphocytes by macrophage-tropic strains of human immunodeficiency virus: resistance to patient-derived and prototype isolates resulting from the delta ccr5 mutation. J Virol. 1997;71:3219-27.
52. Oshiro S, Fukushima T, Tomonaga M, Black KL. Antitumor activity and modified immunoregulation associated with IFN-gamma treatment of RG2 gliomas. Anticancer Res. 1999;19:5029-36.
53. Jin W, Dong C. IL-17 cytokines in immunity and inflammation. Emerg Microbes Infect. 2013;2:e60.
54. Clauset A, Newman MEJ, Moore C. Finding community structure in very large networks. Physical Review E. 2004;70:1-6.
55. Voevodski K, Teng S-H, Xia Y. Finding local communities in protein networks. BMC Bioinformatics. 2009;10:297.
56. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44-57.
57. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1-13.
58. Danelishvili L, McGarvey J, Li Y-J, Bermudez LE. Mycobacterium tuberculosis infection causes different levels of apoptosis and necrosis in human macrophages and alveolar epithelial cells. Cell Microbiol. 2003;5:649-60.
59. Zhang M, Gong J, Lin Y, Barnes PF. Growth of virulent and avirulent Mycobacterium tuberculosis strains in human macrophages. Infect Immun. 1998;66:794-9.
60. Freeman S, Post FA, Bekker L-G, Harbacheuski R, Steyn LM, Ryffel B, et al. Mycobacterium tuberculosis H37Ra and H37Rv differential growth and cytokine/chemokine induction in murine macrophages in vitro. J Interferon Cytokine Res. 2006;26:27-33.
61. Welker D, Demchak B. Porting and using PanGIA for Cytoscape 3: challenges and solutions. F1000Res 2014;3:150-156.