Advertisement

Genomic Data Management in Big Data Environments: The Colorectal Cancer Case

  • Ana León Palacio
  • Alicia García Giménez
  • Juan Carlos Casamayor Ródenas
  • José Fabián Reyes Román
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11158)

Abstract

If there is a domain where data management becomes an intensive Big Data issue, it is the genomic domain, due to the fact that the data generated day after day are exponentially increasing. A genomic data management strategy requires the use of a systematic method, intended to assure that the right data are identified, using the adequate data sources, and linking the selected information with a software platform based on conceptual models, which allows guaranteeing the implementation of genomic services with quality, efficient and valuable data. In this paper, we select the method called “SILE” –for Search, Identification, Load and Exploitation-, and we focus on validating its accuracy in the context of a concrete disease, the Colorectal Cancer. The main contribution of our work is to show how such methodological approach can be applied successfully in a real and complex clinical context, providing a working environment where Genomic Big Data are efficiently managed.

Keywords

SILE Genomics Big Data Data quality Colorectal cancer 

Notes

Acknowledgement

The authors would like to thank members of the PROS Research Centre Genome group for the fruitful discussions regarding the application of CM in the medicine field. This work has been supported by the Spanish Ministry of Science and Innovation through project DataME (ref: TIN2016-80811-P) and the Research and Development Aid Program (PAID-01-16) of the Universitat Politècnica de València under the FPI grant 2137.

References

  1. 1.
    van Dijk, E.L., Auger, H., Jaszczyszyn, Y., Thermes, C.: Ten years of next-generation sequencing technology, Trends Genet. 30(9), 418–426 (2014).  https://doi.org/10.1016/j.tig.2014.07.001CrossRefGoogle Scholar
  2. 2.
    Auffray, C., et al.: Making sense of big data in health research: towards an EU action plan. Genome Med. 8, 71 (2016).  https://doi.org/10.1186/s13073-016-0323-yCrossRefGoogle Scholar
  3. 3.
    Wylie, B., Psaty, B.M.: Personalized medicine in the era of genomics. Jama 298(14), pp. 1682–1684 (2007).  https://doi.org/10.1001/jama.298.14.1682CrossRefGoogle Scholar
  4. 4.
    Rigden, D.J., Fernández, M.X.: The 2018 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res. 46(D1), D1–D7 (2017).  https://doi.org/10.1093/nar/gkx1235CrossRefGoogle Scholar
  5. 5.
    Reyes Román, José F., Iñiguez-Jarrín, Carlos, Pastor, Óscar: Genomic Tools*: web-applications based on conceptual models for the genomic diagnosis. In: Damiani, Ernesto, Spanoudakis, George, Maciaszek, Leszek (eds.) ENASE 2017. CCIS, vol. 866, pp. 48–69. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-94135-6_3CrossRefGoogle Scholar
  6. 6.
    Reyes Román, J.F.: Diseño y Desarrollo de un Sistema de Información Genómica basado en un Modelo Conceptual Holístico del Genoma Humano. Universitat Politècnica de València (2018).  https://doi.org/10.4995/Thesis/10251/99565CrossRefGoogle Scholar
  7. 7.
    Reyes Román, J.F., Pastor, Ó., Casamayor, J.C., Valverde, F.: Applying conceptual modeling to better understand the human genome. In: Comyn-Wattiau, I., Tanaka, K., Song, l-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 404–412. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46397-1_31CrossRefGoogle Scholar
  8. 8.
    López, Ó.P., Palacio, A.L., Román, J.F.R., Casamayor, J.C.: Modeling life: a conceptual schema-centric approach to understand the genome. In: Cabot, J., Gómez, C., Pastor, O., Sancho, M., Teniente, E. (eds.) Conceptual Modeling Perspectives. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67271-7_3CrossRefGoogle Scholar
  9. 9.
    Reyes Román, J.F., Iñiguez-Jarrín, C., Pastor López, O.: GenesLove.Me: a model-based-web-Application for direct-To-consumer genetic tests, In: ENASE 2017 - Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering, pp. 133–143 (2017).  https://doi.org/10.5220/0006340201330143
  10. 10.
    Piñero, J., et al.: DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants, Nucleic acids research, gkw943 (2016).  https://doi.org/10.1093/nar/gkw943CrossRefGoogle Scholar
  11. 11.
    Landrum, M.J., et al.: ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46(D1), D1062–D1067 (2017).  https://doi.org/10.1093/nar/gkx1153CrossRefGoogle Scholar
  12. 12.
    Zerbino, D.R., et al.: Ensembl 2018. Nucleic Acids Res. 46(D1), D754–D761 (2017).  https://doi.org/10.1093/nar/gkx1098CrossRefGoogle Scholar
  13. 13.
    Ramos, E.M., et al.: Phenotype–Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. Eur. J. Hum. Genet. 22(1), 144 (2014).  https://doi.org/10.1038/ejhg.2013.96CrossRefGoogle Scholar
  14. 14.
    Sherry S. T., Ward M-H, Kholodov M., et al.: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29(1), 308–311 (2001)CrossRefGoogle Scholar
  15. 15.
    Richards, S., et al.: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).  https://doi.org/10.1038/gim.2015.30CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Research Center on Software Production Methods (PROS)Universitat Politècnica de ValènciaValenciaSpain
  2. 2.Department of Engineering SciencesUniversidad Central Del Este (UCE)San Pedro de MacorísDominican Republic

Personalised recommendations