STON: Exploring Biological Pathways Using the SBGN Standard and Graph Databases

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2016 Dec 7

PMID 27919219

Citations 9

Authors

Vasundra Toure

Alexander Mazein

Dagmar Waltemath

Irina Balaur

Mansoor Saqi

Ron Henkel

Johann Pellet

Charles Auffray

Affiliations

Soon will be listed here.

Abstract

Background: When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks.

Results: We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database.

Conclusion: STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.

Citing Articles

StonPy: a tool to parse and query collections of SBGN maps in a graph database.

Rougny A, Balaur I, Luna A, Mazein A Bioinformatics. 2023; 39(3).

PMID: 36897014 PMC: 10017094. DOI: 10.1093/bioinformatics/btad100.

Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.

Robin V, Bodein A, Scott-Boyer M, Leclercq M, Perin O, Droit A Front Mol Biosci. 2022; 9:962799.

PMID: 36158572 PMC: 9494275. DOI: 10.3389/fmolb.2022.962799.

An overview of graph databases and their applications in the biomedical domain.

Timon-Reina S, Rincon M, Martinez-Tomas R Database (Oxford). 2021; 2021.

PMID: 34003247 PMC: 8130509. DOI: 10.1093/database/baab026.

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks.

Rougny A, Toure V, Albanese J, Waltemath D, Shirshov D, Sorokin A Brief Bioinform. 2021; 22(5).

PMID: 33758926 PMC: 8425392. DOI: 10.1093/bib/bbab049.

Harmonizing semantic annotations for computational models in biology.

Neal M, Konig M, Nickerson D, Misirli G, Kalbasi R, Drager A Brief Bioinform. 2018; 20(2):540-550.

PMID: 30462164 PMC: 6433895. DOI: 10.1093/bib/bby087.

References

Sorokin A, Le Novere N, Luna A, Czauderna T, Demir E, Haw R . Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2. J Integr Bioinform. 2015; 12(2):264. DOI: 10.2390/biecoll-jib-2015-264. View

Zhang J, Wiemann S . KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor. Bioinformatics. 2009; 25(11):1470-1. PMC: 2682514. DOI: 10.1093/bioinformatics/btp167. View

Kanehisa M, Goto S . KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999; 28(1):27-30. PMC: 102409. DOI: 10.1093/nar/28.1.27. View

Czauderna T, Klukas C, Schreiber F . Editing, validating and translating of SBGN maps. Bioinformatics. 2010; 26(18):2340-1. PMC: 2935428. DOI: 10.1093/bioinformatics/btq407. View

Henkel R, Wolkenhauer O, Waltemath D . Combining computational models, semantic annotations and simulation experiments in a graph database. Database (Oxford). 2015; 2015. PMC: 4352687. DOI: 10.1093/database/bau130. View

Dogrusoz U, Cetintas A, Demir E, Babur O . Algorithms for effective querying of compound graph-based pathway databases. BMC Bioinformatics. 2009; 10:376. PMC: 2784781. DOI: 10.1186/1471-2105-10-376. View

Rohn H, Junker A, Hartmann A, Grafahrend-Belau E, Treutler H, Klapperstuck M . VANTED v2: a framework for systems biology applications. BMC Syst Biol. 2012; 6:139. PMC: 3610154. DOI: 10.1186/1752-0509-6-139. View

Fujita K, Ostaszewski M, Matsuoka Y, Ghosh S, Glaab E, Trefois C . Integrating pathways of Parkinson's disease in a molecular interaction map. Mol Neurobiol. 2013; 49(1):88-102. PMC: 4153395. DOI: 10.1007/s12035-013-8489-4. View

Mizuno S, Iijima R, Ogishima S, Kikuchi M, Matsuoka Y, Ghosh S . AlzPathway: a comprehensive map of signaling pathways of Alzheimer's disease. BMC Syst Biol. 2012; 6:52. PMC: 3411424. DOI: 10.1186/1752-0509-6-52. View

10.

Petersen B, Ropella G, Hunt C . Toward modular biological models: defining analog modules based on referent physiological mechanisms. BMC Syst Biol. 2014; 8:95. PMC: 4236728. DOI: 10.1186/s12918-014-0095-1. View

11.

Kuperstein I, Bonnet E, Nguyen H, Cohen D, Viara E, Grieco L . Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps. Oncogenesis. 2015; 4:e160. PMC: 4521180. DOI: 10.1038/oncsis.2015.19. View

12.

Summer G, Kelder T, Ono K, Radonjic M, Heymans S, Demchak B . cyNeo4j: connecting Neo4j and Cytoscape. Bioinformatics. 2015; 31(23):3868-9. PMC: 4653389. DOI: 10.1093/bioinformatics/btv460. View

13.

Lysenko A, Roznovat I, Saqi M, Mazein A, Rawlings C, Auffray C . Representing and querying disease networks using graph databases. BioData Min. 2016; 9:23. PMC: 4960687. DOI: 10.1186/s13040-016-0102-8. View

14.

Sharma A, Menche J, Huang C, Ort T, Zhou X, Kitsak M . A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum Mol Genet. 2015; 24(11):3005-20. PMC: 4447811. DOI: 10.1093/hmg/ddv001. View

15.

Pavlopoulos G, Secrier M, Moschopoulos C, Soldatos T, Kossida S, Aerts J . Using graph theory to analyze biological networks. BioData Min. 2011; 4:10. PMC: 3101653. DOI: 10.1186/1756-0381-4-10. View

16.

Hucka M, Nickerson D, Bader G, Bergmann F, Cooper J, Demir E . Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative. Front Bioeng Biotechnol. 2015; 3:19. PMC: 4338824. DOI: 10.3389/fbioe.2015.00019. View

17.

Have C, Jensen L . Are graph databases ready for bioinformatics?. Bioinformatics. 2013; 29(24):3107-8. PMC: 3842757. DOI: 10.1093/bioinformatics/btt549. View

18.

Johnson D, Connor A, McKeever S, Wang Z, Deisboeck T, Quaiser T . Semantically linking in silico cancer models. Cancer Inform. 2014; 13(Suppl 1):133-43. PMC: 4260769. DOI: 10.4137/CIN.S13895. View

19.

Merico D, Gfeller D, Bader G . How to visually interpret biological data using networks. Nat Biotechnol. 2009; 27(10):921-4. PMC: 4154490. DOI: 10.1038/nbt.1567. View

20.

Le Novere N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A . The Systems Biology Graphical Notation. Nat Biotechnol. 2009; 27(8):735-41. DOI: 10.1038/nbt.1558. View