Advertisement

Ad-hoc Analysis of Genetic Pathways

  • Dominik Müller
Chapter
Part of the In-Memory Data Management Research book series (IMDM)

Abstract

Biological pathways describe different processes and relations within a cell and help to understand the human body. Therefore, they can aid in finding the cause of a genetic disease and thus support treatment decisions. However, identifying pathways affected by mutations based on their internal connections is a complex task. Today, most pathway databases offer only a single keyword search to find pathways. Only a small subset of the databases offer a more complex analysis, such as the ConsensusPathDB and hiPathDB, use an approach based on the relationships between genes. In this contribution, I propose a prototype for analyzing pathways based on their internal topology and relations. Over the course of several months, I aggregated the data of multiple pathway databases. Using in-memory database technology, the prototype traverses the underlying graph of these data to find affected pathways based on a set of genes. The possibility to traverse the pathway graph on the fly might help to find new relationships between diseases and pathways.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [165]
    Bader GD, Cary MP, Sander C (2006) Pathguide: A Pathway Resource List. Nucleic Acids Research 34(Suppl 1):D504–D506Google Scholar
  2. [166]
    Bastian M, Heymann S, Jacomy M (2009) Gephi: An Open Source Software for Exploring and Manipulating Networks. In: ICWSMGoogle Scholar
  3. [167]
    Bigg NL et al. (1976) Graph Theory 1736-1936. Oxford University Press on DemandGoogle Scholar
  4. [168]
    Bornhövd C et al. (2013) Flexible Information Management, Exploration, and Analysis in SAP HANA. In: Proceedings of the International Conference on Data Technologies and Applications, pp 15–28Google Scholar
  5. [169]
    Brandes U et al. (2010) Graph Markup Language (GraphML). In: Tamassia R (ed) Handbook of Graph Drawing and Visualization, CRC Press, chap 16, pp 517–541Google Scholar
  6. [170]
    Buerli M (2012) The Current State of Graph Databases. Technical report, Department of Computer Science, Cal Poly San Luis ObispoGoogle Scholar
  7. [171]
    Cattell R (2011) Scalable SQL and NoSQL Data Stores. ACM SIGMOD Record 39(4):12–27CrossRefGoogle Scholar
  8. [172]
    Cytoscape Consortium (2012) Cytoscape User Manual: Network Formats. http://wiki.cytoscape.org/Cytoscape_User_Manual/Network_Formats. Accessed Sep 23, 2013
  9. [173]
    Demir E et al. (2010) The BioPAX Community Standard for Pathway Data Sharing. Nature Biotechnology 28(9):935–942PubMedCrossRefGoogle Scholar
  10. [174]
    Draghici S et al. (2007) A Systems Biology Approach for Pathway Level Analysis. Genome Research 17(10):1537–1545PubMedCrossRefGoogle Scholar
  11. [175]
    Färber F et al. (2012) SAP HANA Database: Data Management for Modern Business Applications. Sigmod Record 40(4):45–51CrossRefGoogle Scholar
  12. [176]
    Fernald GH et al. (2011) Bioinformatics Challenges for Personalized Medicine. Bioinformatics Journal 27(13):1741–1748CrossRefGoogle Scholar
  13. [177]
    Friedman C et al. (2001) GENIES: A Natural-language Processing System for the Extraction of Molecular Pathways from Journal Articles. Bioinformatics Journal 17(Suppl 1):74–S82Google Scholar
  14. [178]
    Goh KI et al. (2007) The Human Disease Network. Proceedings of the National Academy of Sciences 104(21):8685–8690Google Scholar
  15. [179]
    Gray KA et al. (2013) Genenames.org: The HGNC Resources in 2013. Nucleic Acids Research 41(D1):D545–D552Google Scholar
  16. [180]
    Green, ML and Karp, PD (2006) The Outcomes of Pathway Database Computations depend on Pathway Ontology. Nucleic Acids Research 34(13):3687–3697PubMedCrossRefGoogle Scholar
  17. [181]
    Hecht R, Jablonski S (2011) NoSQL evaluation: A use case oriented survey. In: International Conference on Cloud and Service Computing (CSC), 2011, IEEE, pp 336–341Google Scholar
  18. [182]
    HuckaMet al. (2010) The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core. http://precedings.nature.com/documents/4959/version/1/files/npre20104959-1.pdf. Accessed Sep 23, 2013
  19. [183]
    Kamburov A et al. (2013) The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Research 41(D1):D793–D800Google Scholar
  20. [184]
    Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 28(1):27–30PubMedCrossRefGoogle Scholar
  21. [185]
    Kanehisa M et al. (2012) KEGG for Integration and Interpretation of Largescale Molecular Data Sets. Nucleic Acids Research 40(D1):D109–D114Google Scholar
  22. [186]
    Kanehisa Laboratories (2012) KEGG PATHWAY: Colorectal Cancer - Reference Pathway. http://www.genome.jp/kegg-bin/show_pathway?org_name=map&mapno=05210. Accessed Sep 23, 2013
  23. [187]
    Kelder T et al. (2012) WikiPathways: Building Research Communities on Biological Pathways. Nucleic Acids Research 40(D1):D1301–D1307Google Scholar
  24. [188]
    Kerrien S et al. (2007) Broadening the Horizon: Level 2.5 of the HUPO-PSI Format for Molecular Interactions. BMC biology 5(1):44PubMedCrossRefGoogle Scholar
  25. [189]
    Khatri P, Sirota M, Butte AJ (2012) Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges. PLoS Computational Biology 8(2)Google Scholar
  26. [190]
    Kirouac DC et al. (2012) Creating and Analyzing Pathway and Protein Interaction Compendia for Modelling Signal Transduction Networks. BMC Systems Biology 6(1):29PubMedCrossRefGoogle Scholar
  27. [191]
    Knöpfel A, Gröne B, Tabeling P (2005) Fundamental Modeling Concepts. Wiley, West Sussex UKGoogle Scholar
  28. [192]
    Kono N et al. (2009) Pathway Projector: Web-based Zoomable Pathway Browser using KEGG Atlas and Google Maps API. PLoS One 4(11)Google Scholar
  29. [193]
    Leavitt N (2010) Will NoSQL Databases live up to their Promise? IEEE Computer 43(2):12–14CrossRefGoogle Scholar
  30. [194]
    Matthews L et al. (2009) Reactome Knowledgebase of Human Biological Pathways and Processes. Nucleic Acids Research 37(suppl 1):D619–D622Google Scholar
  31. [195]
    Miller JJ (2013) Graph Database Applications and Concepts with Neo4. In: Proceedings of the 2013 Southern Association for Information SystemsGoogle Scholar
  32. [196]
    Neo4J Developers (2012) Neo4J. Graph NoSQL DatabaseGoogle Scholar
  33. [197]
    Nishimura D (2001) BioCarta. The Computer Software Journal for Scient 2(3):117–120Google Scholar
  34. [198]
    Peng G et al. (2009) Gene and Pathway-based Second-wave Analysis of Genome-wide Association Studies. European Journal of Human Genetics 18(1):111–117CrossRefGoogle Scholar
  35. [199]
    Pico AR et al. (2008) WikiPathways: Pathway Editing for the People. PLoS biology 6(7):184Google Scholar
  36. [200]
    Plattner H (2013) A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases. SpringerGoogle Scholar
  37. [201]
    Python Software Foundation (2013) 26.6. Timeit - Measure Execution Time of Small Code Snippets. http://docs.python.org/2/library/timeit.html. Accessed Sep 23, 2013
  38. [202]
    Ramanan VK et al. (2012) Pathway Analysis of Genomic Data: Concepts, Methods and Prospects for Future Development. Trends in Genetics 28(7):323–332PubMedCrossRefGoogle Scholar
  39. [203]
    Rodriguez MA (2013) Gremlin GitHub. http://github.com/tinkerpop/gremlin/wiki. Accessed Sep 23, 2013
  40. [204]
    Rodriguez MA, Neubauer P (2010) Constructions from Dots and Lines. Bulletin of the American Society for Information Science and Technology 36(6):35–41CrossRefGoogle Scholar
  41. [205]
    Romero P et al. (2004) Computational Prediction of Human Metabolic Pathways from the Complete Human Genome. Genome Biology 6(1):R2PubMedCrossRefGoogle Scholar
  42. [206]
    Rudolf M et al. (2013) The Graph Story of the SAP HANA Database. Proceedings of the 15 GI-Fachtagung Datenbanksysteme für Business, Technologie und WebGoogle Scholar
  43. [207]
    Safran M et al. (2010) GeneCards Version 3: The Human Gene Integrator. The Journal of Biological Databases and Curation 2010Google Scholar
  44. [208]
    Schaefer CF et al. (2009) PID: The Pathway Interaction Database. Nucleic Acids Research 37(suppl 1):D674–D679Google Scholar
  45. [209]
    Strauch C (2011) NoSQL Databases. http://www.christof-strauch.de/nosqldbs.pdf. Accessed Sep 23, 2013
  46. [210]
    Strömbäck L, Lambrix P (2005) Representations of Molecular Pathways: An Evaluation of SBML, PSI MI and BioPAX. Bioinformatics Journal 21(24):4401–4407CrossRefGoogle Scholar
  47. [211]
    Yu N et al. (2012) hiPathDB: A Human-integrated Pathway Database with Facile Visualization. Nucleic Acids Research 40(D1):D797–D802Google Scholar
  48. [212]
    Zhao J et al. (2011) Pathway-based Analysis using reduced Gene Subsets in Genome-wide Association Studies. BMC Bioinformatics 12(1):17PubMedCrossRefGoogle Scholar
  49. [213]
    Zvelebil M, Baum J (2007) Understanding Bioinformatics. Garland ScienceGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.PotsdamGermany

Personalised recommendations