- -

The challenge of managing the evolution of genomics data over time: a conceptual model-based approach

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

The challenge of managing the evolution of genomics data over time: a conceptual model-based approach

Mostrar el registro completo del ítem

García-Simón, A.; Costa-Sánchez, M.; León-Palacio, A.; Pastor López, O. (2022). The challenge of managing the evolution of genomics data over time: a conceptual model-based approach. BMC Bioinformatics. 23:1-32. https://doi.org/10.1186/s12859-022-04944-z

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/195316

Ficheros en el ítem

Metadatos del ítem

Título: The challenge of managing the evolution of genomics data over time: a conceptual model-based approach
Autor: García-Simón, Alberto Costa-Sánchez, Mireia León-Palacio, Ana Pastor López, Oscar
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
[EN] Background Precision medicine is a promising approach that has revolutionized disease prevention and individualized treatment. The DELFOS oracle is a model-driven genomics platform that aids clinicians in identifying ...[+]
Palabras clave: Bioinformatics , Temporal dimension , Conceptual models , SILE method , DELFOS oracle
Derechos de uso: Reconocimiento (by)
Fuente:
BMC Bioinformatics. (issn: 1471-2105 )
DOI: 10.1186/s12859-022-04944-z
Editorial:
Springer (Biomed Central Ltd.)
Versión del editor: https://doi.org/10.1186/s12859-022-04944-z
Coste APC: 1650
Código del Proyecto:
info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//PDC2021-121243-I00//PLATAFORMA DELFOS: SISTEMA DE INFORMACIÓN PARA LA GESTIÓN DE VARIACIONES GENOMICAS/
info:eu-repo/grantAgreement/UPV//20220366/
info:eu-repo/grantAgreement/GVA//CIPROM%2F2021%2F023/
info:eu-repo/grantAgreement/AVI//INNEST%2F2021%2F57/
info:eu-repo/grantAgreement/CIUCSD//ACIF%2F2021%2F117/
Agradecimientos:
This work has been developed with the financial support of the Generalitat Valenciana and the Valencian Innovation Agency under the projects MICIN/AEI/ 10.13039/501100011033, PDC2021-121243-I00, CIPROM/2021/023, 2021/AP2021-05, ...[+]
Tipo: Artículo

References

Duffy DJ. Problems, challenges and promises: perspectives on precision medicine. Brief Bioinform. 2016;17(3):494–504. https://doi.org/10.1093/bib/bbv060.

Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Bio. 2017;18(1):83. https://doi.org/10.1186/s13059-017-1215-1.

Stephens ZD. Big data: astronomical or genomical? PLOS Biol. 2015;13(7):1002195. https://doi.org/10.1371/journal.pbio.1002195. [+]
Duffy DJ. Problems, challenges and promises: perspectives on precision medicine. Brief Bioinform. 2016;17(3):494–504. https://doi.org/10.1093/bib/bbv060.

Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Bio. 2017;18(1):83. https://doi.org/10.1186/s13059-017-1215-1.

Stephens ZD. Big data: astronomical or genomical? PLOS Biol. 2015;13(7):1002195. https://doi.org/10.1371/journal.pbio.1002195.

Vihinen M. Problems in variation interpretation guidelines and in their implementation in computational tools. Mol Genet Genomic Med. 2020;8(9):1206. https://doi.org/10.1002/mgg3.1206.

Rigden DJ, Fernández XM. The 2021 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res. 2021;49(D1):1–9. https://doi.org/10.1093/nar/gkaa1216.

Galvão J, et al. Automating data integration in adaptive and data-intensive information systems. In: Information systems. Lecture notes in business information processing. Cham: Springer; 2020. pp. 20–34 https://doi.org/10.1007/978-3-030-63396-7_2.

Palacio AL, López OP. Smart data for genomic information systems: the SILE method. Complex Syst Inf Model Q. 2018;17:1–23. https://doi.org/10.7250/csimq.2018-17.01.

Costa M, León A, Pastor O. The importance of the temporal dimension in identifying relevant genomic variants: a case study. In: Grossmann G, Ram S, editors. Advances in conceptual modeling. Lecture notes in computer science. Cham: Springer; 2020. p. 51–60. https://doi.org/10.1007/978-3-030-65847-2_5.

Mersch JA. Prevalence of variant reclassification following hereditary cancer genetic testing. JAMA. 2018;320(12):1266–74. https://doi.org/10.1001/jama.2018.13152.

Landrum MJ, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):1062–7. https://doi.org/10.1093/nar/gkx1153.

Hunt SE, et al. Ensembl variation resources. Database. 2018. https://doi.org/10.1093/database/bay119.

Buniello A, et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):1005–12. https://doi.org/10.1093/nar/gky1120.

Tate JG, et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 2019;47(D1):941–7. https://doi.org/10.1093/nar/gky1015.

Walsh R, et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med. 2017;19(2):192–203. https://doi.org/10.1038/gim.2016.90.

Plon SE, Rehm HL. The Ancestral pace of variant eeclassification. JNCI J Natl Cancer Inst. 2018;110(10):1133–4. https://doi.org/10.1093/jnci/djy075.

Slavin TP, et al. The effects of genomic germline variant reclassification on clinical cancer care. Oncotarget. 2019;10(4):417–23. https://doi.org/10.18632/oncotarget.26501.

Wong EK, et al. Perceptions of genetic variant reclassification in patients with inherited cardiac disease. Eur J Hum Genet. 2019;27(7):1134–42. https://doi.org/10.1038/s41431-019-0377-6.

Campuzano O, et al. Reanalysis and reclassification of rare genetic variants associated with inherited arrhythmogenic syndromes. EBioMedicine. 2020;54:102732. https://doi.org/10.1016/j.ebiom.2020.102732.

Miller DT, et al. ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2021. https://doi.org/10.1038/s41436-021-01172-3.

Palacio AL, López ÓP, Ródenas JCC. A method to identify relevant genome data: conceptual modeling for the medicine of precision. In: Trujillo J, Davis KC, Du X, Li Z, Ling TW, Li G, Lee ML, editors. Conceptual modeling - 37th international conference, ER 2018, Proceedings. Lecture notes in computer science, vol. 11157. Springer; 2018. pp. 597–609. https://doi.org/10.1007/978-3-030-00847-5_44

Ceri S, Bernasconi A, Canakoglu A, Gulino A, Kaitoua A, Masseroli M, Nanni L, Pinoli P. Overview of GeCo: a project for exploring and integrating signals from the genome. In: Kalinichenko L, Manolopoulos Y, Malkov O, Skvortsov N, Stupnikov S, Sukhomlin V, editors. Data analytics and management in data intensive domains. Communications in computer and information science. Cham: Springer; 2018. p. 46–57. https://doi.org/10.1007/978-3-319-96553-6_4.

Bernasconi A, Canakoglu A, Pinoli P, Ceri S. Empowering virus sequence research through conceptual modeling. In: Dobbie G, Frank U, Kappel G, Liddle SW, Mayr HC, editors. Conceptual modeling. Lecture notes in computer science. Cham: Springer; 2020. p. 388–402. https://doi.org/10.1007/978-3-030-62522-1_29.

Reyes Román JF, Marco Palomares A, García Simón A, Pastor O. A model-based application for the effective and efficient management of data associated with Retina-Macula Pathology. In: Augusto A, Gill A, Nurcan S, Reinhartz-Berger I, Schmidt R, Zdravkovic J, editors. Enterprise, business-process and information systems modeling. Lecture notes in business information processing. Cham: Springer; 2021. p. 366–79. https://doi.org/10.1007/978-3-030-79186-5_24.

García S. A, Iñiguez-Jarrín C, Lopez O, Gonzalez-Ibea D, Pérez-Román E, Borredà C, Terol J, Ibanez V, Talón M. Applying user centred design to improve the design of genomic user interfaces, 2021;25–35. https://www.scitepress.org/Link.aspx?doi=10.5220/0010187800250035 Accessed 09 Nov 2021.

Smith B, et al. The OBO foundry: coordinated evolution of ontologies to support biomedical data integration. Berlin: Nature Publishing Group; 2007. https://doi.org/10.1038/nbt1346.

Yon Rhee S, Wood V, Dolinski K, Draghici S. Use and misuse of the gene ontology annotations. Berlin: Nature Publishing Group; 2008. https://doi.org/10.1038/nrg2363.

Smith B, Williams J, Schulze-Kremer S. The ontology of the gene ontology. In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 2003;2003, 609–613.

Ashburner M, et al. Creating the gene ontology resource: design and implementation. Genome Res. 2001;11(8):1425–33. https://doi.org/10.1101/gr.180801.

Jacobsen A, de Miranda Azevedo R, Juty N, Batista D, Coles S, Cornet R, Courtot M, Crosas M, Dumontier M, Evelo CT, Goble C, Guizzardi G, Hansen KK, Hasnain A, Hettne K, Heringa J, Hooft RWW, Imming M, Jeffery KG, Kaliyaperumal R, Kersloot MG, Kirkpatrick CR, Kuhn T, Labastida I, Magagna B, McQuilton P, Meyers N, Montesanti A, van Reisen M, Rocca-Serra P, Pergl R, Sansone S-A, da Silva Santos LOB, Schneider J, Strawn G, Thompson M, Waagmeester A, Weigel T, Wilkinson MD, Willighagen EL, Wittenburg P, Roos M, Mons B, Schultes E. FAIR principles: interpretations and implementation considerations. Data Intell. 2020;2(1–2):10–29. https://doi.org/10.1162/dint_r_00024.

Guizzardi G. Ontology, ontologies and the “I’’ of FAIR. Data Intell. 2020;2(1–2):181–91. https://doi.org/10.1162/dint_a_00040.

García S A, Casamayor JC. Towards the generation of a species-independent conceptual schema of the genome. In: Grossmann G, Ram S, editors. Advances in conceptual modeling. Cham: Springer; 2020. p. 61–70.

García SA, Casamayor JC. On how to generalize species-specific conceptual schemes to generate a species-independent conceptual schema of the genome. BMC Bioinform. 2021;22(13):353. https://doi.org/10.1186/s12859-021-04237-x.

García SA, Casamayor JC, Pastor O. ISGE: a conceptual model-based method to correctly manage genome data. In: Nurcan S, Korthaus A, editors. Intelligent information systems. Cham: Springer; 2021. p. 47–54.

León A, García SA, Costa M, Vañó Ribelles A, Pastor O. Evolution of an adaptive information system for precision medicine. In: Nurcan S, Korthaus A, editors. Intelligent information systems. Lecture notes in business information processing. Cham: Springer; 2021. https://doi.org/10.1007/978-3-030-79108-7_1.

Costa M. Diseño y Desarrollo de una Plataforma para la Gestión de Datos Genómicos: Oráculo Genómico de Delfos. Universitat Politècnica de València. 2021. http://hdl.handle.net/10251/173220.

Spreeuwenberg S, Henao P. AIX: artificial intelligence needs eXplanation: why and how transparency increases the success of AI solutions, 2019.

Iñiguez-Jarrin C. GenomIUm: a pattern based method for designing user interfaces for genomic data access. PhD Thesis, Universitat Politècnica de València, 2019.

Good BM, Ainscough BJ, McMichael JF, Su AI, Griffith OL. Organizing knowledge to enable personalization of medicine in cancer. Genome Biol. 2014;15(8):438. https://doi.org/10.1186/s13059-014-0438-7.

García Simón A, Costa Sánchez M, Pastor O. Characterization and treatment of the temporal dimension of genomic variations: a conceptual model-based approach. In: Reinhartz-Berger I, Sadiq S, editors. Advances in conceptual modeling. Lecture notes in Computer Science. Cham: Springer; 2021. p. 104–13. https://doi.org/10.1007/978-3-030-88358-4_9.

Wieringa RJ. Design science methodology for information systems and software engineering. Cham: Springer; 2014.

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem