Representing and querying disease networks using graph databases

BioData Mining

Volumn 9, Issue 1, 2016, Pages

  Lysenko, Artem ^a   RoznovǍţ, Irina A ^b   Saqi, Mansoor ^b   Mazein, Alexander ^b   Rawlings, Christopher J ^a   Auffray, Charles ^b  

^a ROTHAMSTED RESEARCH   (United Kingdom)

^b CNRS   (France)

Author keywords

Computational approach; Disease management platform; Graph database; Neo4j graph; Protein centric framework; Systems medicine

Indexed keywords

ASTHMA; CHRONIC OBSTRUCTIVE LUNG DISEASE; CONTROLLED STUDY; DATA ANALYSIS; DATA BASE; DATA MINING; ESSENTIAL HYPERTENSION; GENE; GENE ONTOLOGY; GRAPH DATABASE; HUMAN; INFORMATION PROCESSING; PRIORITY JOURNAL; REVIEW; SYSTEMS BIOLOGY; TUBERCULOSIS;

EID: 84979202506     PISSN: None     EISSN: 17560381     Source Type: Journal    
DOI: 10.1186/s13040-016-0102-8     Document Type: Review

References (37)

1. Auffray C, Charron D, Hood L. Predictive, preventive, personalized and participatory medicine: back to the future. Genome Med. 2010;2:57. doi: 10.1186/gm178.
2. Hood L, Tian Q. Systems approaches to biology and disease enable translational systems medicine. Genomics Proteomics Bioinformatics. 2012;10:181-5. doi: 10.1016/j.gpb.2012.08.004.
3. Callahan A, Cruz-Toledo J, Ansell P, Dumontier M. Bio2RDF release 2: improved coverage, interoperability and provenance of life science linked data. In: Cimiano P, Corcho O, Presutti V, et al., editors. Semantic Web Semant. Berlin Heidelberg: Big Data. Springer; 2013. p. 200-12.
4. Pareja-Tobes P, Tobes R, Manrique M, et al. Bio4j: a high-performance cloud-enabled graph-based data platform. bioRxiv 016758. 2015. doi: http://dx.doi.org/10.1101/016758.
5. Smoot ME, Ono K, Ruscheinski J, et al. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431-32. doi: 110.1093/bioinformatics/btq675.
6. Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. ICWSM. 2009;8:361-62.
7. Schult DA, Swart P. Exploring network structure, dynamics, and function using NetworkX. In Proceedings of the 7th Python in Science Conferences (SciPy 2008). 2008;2008:11-6.
8. Birkland A, Yona G. BIOZON: a system for unification, management and analysis of heterogeneous biological data. BMC Bioinformatics. 2006;7(1)1. doi: 10.1186/1471-2105-7-70.
9. Küntzer J, Blum T, Gerasch A, Backes C, Hildebrandt A, Kaufmann M, Lenhof HP. BN++-a biological information system. Journal of Integrative Bioinformatics. 2006;3(2)34.
10. Köhler J, Baumbach J, Taubert J, et al. Graph-based analysis and visualization of experimental results with ONDEX. Bioinforma Oxf Engl. 2006;22:1383-90. doi: 10.1093/bioinformatics/btl081.
11. Lysenko A, Lysenko A, Hindle MM, et al. Data integration for plant genomics-exemplars from the integration of Arabidopsis thaliana databases. Brief Bioinform. 2009;10:676-93. doi: 10.1093/bib/bbp047.
12. Eronen L, Toivonen H. Biomine: predicting links between biological entities using network models of heterogeneous databases. BMC Bioinformatics. 2012;13:119. doi: 10.1186/1471-2105-13-119.
13. Consortium TU. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204-12. doi: 10.1093/nar/gku989.
14. Consortium TGO. Gene ontology consortium: going forward. Nucleic Acids Res. 2015;43:D1049-56. doi: 10.1093/nar/gku1179.
15. Tatusova T, Ciufo S, Fedorov B, et al. RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 2014;42:D553-559. doi: 10.1093/nar/gkt1274.
16. Sayers EW, Barrett T, Benson DA, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2009;37:D5-D15. doi: 10.1093/nar/gkn741.
17. Bairoch A. The ENZYME database in 2000. Nucleic Acids Res. 2000;28:304-5.
18. Kerrien S, Aranda B, Breuza L, et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 2012;40:D841-846. doi: 10.1093/nar/gkr1088.
19. Croft D, Mundo AF, Haw R, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014;42:D472-477. doi: 10.1093/nar/gkt1102.
20. Bauer-Mehren A, Bundschus M, Rautschka M, et al. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PLoS One. 2011;6:e20284. doi: 10.1371/journal.pone.0020284.
21. Knox C, Law V, Jewison T, et al. DrugBank 3.0: a comprehensive resource for "Omics" research on drugs. Nucleic Acids Res. 2011;39:D1035-41. doi: 10.1093/nar/gkq1126.
22. Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347:1260419. doi: 10.1126/science.1260419.
23. Kaneko Y, Yatagai Y, Yamada H, et al. The search for common pathways underlying asthma and COPD. Int J Chron Obstruct Pulmon Dis. 2013;8:65-78. doi: 10.2147/COPD.S39617.
24. Voraphani N, Gladwin MT, Contreras AU, et al. An airway epithelial iNOS-DUOX2-thyroid peroxidase metabolome drives Th1/Th2 nitrative stress in human severe asthma. Mucosal Immunol. 2014;7:1175-85. doi: 10.1038/mi.2014.6.
25. Modena BD, Tedrow JR, Milosevic J, et al. Gene expression in relation to exhaled nitric oxide identifies novel asthma phenotypes with unique biomolecular pathways. Am J Respir Crit Care Med. 2014;190:1363-72. doi: 10.1164/rccm.201406-1099OC.
26. Ritchie ME, Phipson B, Wu D, et al. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res gkv007. doi: 10.1093/nar/gkv007.
27. Durrington HJ, Farrow SN, Loudon AS, Ray DW. The circadian clock and asthma. Thorax. 2014;69:90-2. doi: 10.1136/thoraxjnl-2013-203482.
28. Ko CH, Takahashi JS. Molecular components of the mammalian circadian clock. Hum Mol Genet. 2006;15:R271-7. doi: 10.1093/hmg/ddl207.
29. Lesk V, Taubert J, Rawlings C, et al. WIBL: Workbench for Integrative Biological Learning. J Integr Bioinforma. 2011;8:156. doi: 10.2390/biecoll-jib-2011-156.
30. Sternberg MJE, Tamaddoni-Nezhad A, Lesk VI, et al. Gene function hypotheses for the Campylobacter jejuni glycome generated by a logic-based approach. J Mol Biol. 2013;425:186-97. doi: 10.1016/j.jmb.2012.10.014.
31. Côté RA. Architecture of SNOMED. In: Orthner HF, Blum BI, editors. Implement. Health Care Inf. Syst. New York: Springer; 1989. p. 167-79.
32. Rogers FB. Communications to the Editor. Bull Med Libr Assoc. 1963;51:114-6.
33. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25-9. doi: 10.1038/75556.
34. Brinkman R, Courtot M, Derom D, et al. Modeling biomedical experimental processes with OBI. J Biomed Semant. 2010;1:S7+. doi: 10.1186/2041-1480-1-s1-s7.
35. Smith B, Ashburner M, Rosse C, et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol. 2007;25:1251-5. doi: 10.1038/nbt1346.
36. Noy NF, Shah NH, Whetzel PL, et al. (2009) BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res gkp440. doi: 10.1093/nar/gkp440.
37. Lassila O, Swick RR, Wide W, Consortium W. Resource Description Framework (RDF) model and syntax specification. 1998.