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Genomic Tools*: Web-Applications Based on Conceptual Models for the Genomic Diagnosis

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Evaluation of Novel Approaches to Software Engineering (ENASE 2017)

Abstract

Although experts in the genomics field now work with bioinformatics tools (software) to generate genomic diagnoses, the fact is that these solutions do not fully meet their needs. From the perspective of Information Systems (IS), the real problems lie in the lack of an approach (i.e., Software Engineering techniques) that can generate correct structures for data management. Due to the problems of dispersion, heterogeneity and the inconsistency of the data, understanding the genomic domain is a huge challenge. To demonstrate the advantages of Conceptual Modeling (CM) in complex domains -such as genomics- we propose two web-based tools for genomic diagnosis that incorporates: (i) a Conceptual Model for the direct-to-consumer genetic tests (DCGT), and (ii) our Conceptual Model of the Human Genome (CMHG), both with the aim of taking advantage of Next-Generation Sequencing (NGS) for ensuring genomic diagnostics that help to maximize the Precision Medicine (PM).

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Notes

  1. 1.

    This type of analysis is available through direct sales systems in pharmacies or other health care bodies, but the Internet has become the main selling channel for direct-to-consumer genetic analyses [7].

  2. 2.

    www.tellmegen.com/.

  3. 3.

    https://www.imegen.es/.

  4. 4.

    https://software.broadinstitute.org/gatk/.

  5. 5.

    https://usegalaxy.org/.

  6. 6.

    http://geneslove.me/index.php.

  7. 7.

    http://www.pros.webs.upv.es/.

  8. 8.

    http://www.hgvs.org/mutnomen/.

References

  1. Buermans, H.P.J., den Dunnen, J.T.: Next generation sequencing technology: advances and applications. Biochimica et Biophysica Acta (BBA) – Mol. Basis Dis. 1842(10), 1932–1941 (2014). https://doi.org/10.1016/j.bbadis.2014.06.015

    Article  Google Scholar 

  2. Grosso, L.A.: Precision medicine and cardiovascular diseases. Rev. Colomb. Cardiol. 23(2), 73–76 (2016). https://doi.org/10.1007/978-3-540-39390-0

    Article  Google Scholar 

  3. Olivé, A.: Conceptual Modeling of Information Systems, pp. 1–445. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-39390-0

    Book  MATH  Google Scholar 

  4. 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, I.-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_31

    Chapter  Google Scholar 

  5. Reyes Román, J.F., Pastor, Ó., Valverde, F., Roldán, D.: How to deal with Haplotype data: an extension to the conceptual schema of the human genome. CLEI Electron. J. 19(3) (2016). http://dx.doi.org/10.19153/cleiej.19.3.2

  6. Object Management Group: Business Process Model and Notation (2016). http://www.bpmn.org/

  7. Romeo-Malanda, S.: Análisis genéticos directos al consumidor: cuestiones éticas y jurídicas (2009). http://www.institutoroche.es/legalactualidad/85/analisis

  8. Pastor López, O., Reyes Román, J.F., Valverde Giromé, F.: Conceptual Schema of the Human Genome (CSHG). Technical report (2016). http://hdl.handle.net/10251/67297

  9. Reyes Román, J.F., Pastor, O.: Use of GeIS for early diagnosis of alcohol sensitivity. In: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 3, pp. 284–289 (2016). https://doi.org/10.5220/0005822902840289

  10. Bornberg-Bauer, E., Paton, N.W.: Conceptual data modelling for bioinformatics. Briefings Bioinform. 3(2), 166–180 (2002). https://doi.org/10.1093/bib/3.2.166

    Article  Google Scholar 

  11. Ram, S., Wei, W.: Modeling the semantics of 3D protein structures. In: Conceptual Modeling–ER 2004, Proceedings, pp. 696–708 (2004). https://doi.org/10.1007/978-3-540-30464-7_52

    Google Scholar 

  12. Pastor, M.A., Burriel, V., Pastor, O.: Conceptual modeling of human genome mutations: a dichotomy between what we have and what we should have. In: BIOSTEC Bioinformatics 2010, pp. 160–166 (2010). ISBN 978-989-674-019-1

    Google Scholar 

  13. Reyes Román, J.F., Iñiguez-Jarrín, C., Pastor, O.: GenesLove.Me: a model-based web-application for direct-to-consumer genetic tests. In: Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering, pp. 133–143, Porto, Portugal, 28–29 April (2017). ISBN 978-989-758-250-9, https://doi.org/10.5220/0006340201330143

  14. Mardis, E.R.: The $1,000 genome, the $100,000 analysis? Genome Med. 2(11), 84 (2010)

    Article  Google Scholar 

  15. Reyes Román, J.F.: Integración de haplotipos al modelo conceptual del genoma humano utilizando la metodología SILE. Universitat Politècnica de València (2014). http://hdl.handle.net/10251/43776

  16. Aguilar Cartagena, A.: Medicina Personalizada, Medicina De Precisión, ¿Cuán Lejos Estamos De La Perfección? Carcinos 5, 1–2 (2015)

    Google Scholar 

  17. Grupo RETO Hermosillo, A.: El cáncer de mama (2016). http://gruporetohermosilloac.com/index.php

  18. Metzker, M.L.: Sequencing technologies - the next generation. Nat. Rev. Genet. 11(1), 31–46 (2010)

    Article  Google Scholar 

  19. Voelkerding, K.V., Dames, S.A., Durtschi, J.D.: Next-generation sequencing: from basic research to diagnostics. Clin. Chem. 55(4), 641–658 (2009)

    Article  Google Scholar 

  20. 23andMe: 23andMe (2016). https://www.23andme.com/

  21. 23andMe: How it works? (2016). https://www.23andme.com/howitworks/

  22. Cingolani, P.: snpEff: variant effect prediction (2012)

    Google Scholar 

  23. Wang, K., Li, M., Hakonarson, H.: ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38(16), e164–e164 (2010)

    Article  Google Scholar 

  24. McLaren, W., Gil, L., Hunt, S.E., Riat, H.S., Ritchie, G.R., Thormann, A., Cunningham, F.: The ensembl variant effect predictor. Genome Biol. 17(1), 122 (2016)

    Article  Google Scholar 

  25. Roldán, D., Pastor, O., Fernández, M.: An integration architecture framework for e-genomics services. In: IEEE RCIS (2014). https://doi.org/10.1109/rcis.2014.6861063

  26. U. S. National Library of Medicine: What is genetic testing? Genetics Home Reference (2017)

    Google Scholar 

  27. Chinosi, M., Trombetta, A.: BPMN: an introduction to the standard. Comput. Stand. Interfaces 34(1), 124–134 (2012)

    Article  Google Scholar 

  28. Reyes Román, J.F., León, A., Pastor, Ó.: Software engineering and genomics: the two sides of the same coin? In: Proceedings of the International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2017), pp. 1–6 (2017). https://doi.org/10.5220/0006368203010307

  29. Roldán M.D., Pastor López, Ó., Reyes Román, J.F.: E-genomic framework for delivering genomic services. An application to JABAWS. In: 9th RCIS (IEEE), pp. 516–517 (2015). https://doi.org/10.1109/RCIS.2015.7128915

  30. Muñoz, J., Llacer, M., Bonet, B.: Configuring ATL transformations in MOSKitt. In: Proceedings of the 2nd. International Workshop on Model Transformation with ATL (MtATL 2010), CEUR Workshop Proceedings (2010)

    Google Scholar 

  31. Burriel, V., Reyes Román, J.F., Heredia C.A., Iñiguez-Jarrín, C., León, A.: GeIS based on conceptual models for the risk assessment of neuroblastoma. In: 11th RCIS (IEEE), pp. 1–2 (2017). https://doi.org/10.1109/RCIS.2017.7956581

  32. National Center for Biotechnology Information (2017). https://www.ncbi.nlm.nih.gov/

  33. Sherry, S.T., Ward, M.H., Kholodov, M., Baker, J., Phan, L., Smigielski, E.M., Sirotkin, K.: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29(1), 308–311 (2001)

    Article  Google Scholar 

  34. Szabo, C., Masiello, A., Reyes Román, J.F., Brody, L.C.: The breast cancer information core: database design, structure, and scope. Hum. Mutat. 16(2), 123 (2000)

    Article  Google Scholar 

  35. Béroud, C., Collod-Béroud, G., Boileau, C., Soussi, T., Junien, C.: UMD (Universal mutation database): a generic software to build and analyze locus-specific databases. Hum. Mutat. 15(1), 86 (2000)

    Article  Google Scholar 

  36. Zhou, H., Yang, D., Xu, Y.: An ETL strategy for real-time data warehouse. In: Wang, Y., Li, T. (eds.) Practical Applications of Intelligent Systems. Advances in Intelligent and Soft Computing, vol. 124, pp. 329–336. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25658-5_41

    Chapter  Google Scholar 

  37. Claverie, J.M., Notredame, C.: Bioinformatics for Dummies. Wiley, Hoboken (2011)

    Google Scholar 

  38. Haupt, F., Karastoyanova, D., Leymann, F., Schroth, B.: A model-driven approach for REST compliant services. In: IEEE International Conference on Web Services (ICWS), pp. 129–136 (2014)

    Google Scholar 

  39. Tolhuis, B., Wesselink, J.J.: NA12878 Platinum Genome GENALICE MAP analysis report (2015)

    Google Scholar 

  40. León, A., Reyes, J., Burriel, V., Valverde, F.: Data quality problems when integrating genomic information. In: Link, S., Trujillo, J.C. (eds.) ER 2016. LNCS, vol. 9975, pp. 173–182. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47717-6_15

    Chapter  Google Scholar 

  41. de Galicia, C.A.: Ley 3/2001, reguladora del consentimiento informado y de la historia clínica de los pacientes (2001)

    Google Scholar 

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Acknowledgements

This work was supported by the MESCyT of the Dominican Republic and also by the Generalitat Valenciana through project IDEO (PROMETEOII/2014/039), the Spanish Ministry of Science and Innovation through Project DataME (ref: TIN2016-80811-P).

The authors are grateful to Jorge Guerola M., David Roldán Martínez, Alberto García S., Ana León Palacio, Francisco Valverde Girome, Ainoha Martín, Verónica Burriel Coll, Mercedes Fernández A., Carlos Iñiguez-Jarrín, Lenin Javier Serrano and Ma. José Villanueva for their valuable assistance.

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Correspondence to José F. Reyes Román .

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Reyes Román, J.F., Iñiguez-Jarrín, C., Pastor, Ó. (2018). Genomic Tools*: Web-Applications Based on Conceptual Models for the Genomic Diagnosis. In: Damiani, E., Spanoudakis, G., Maciaszek, L. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2017. Communications in Computer and Information Science, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-319-94135-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-94135-6_3

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