Innovative Databases in Ecomonitoring Information Systems: Images of Genetic Codes as Keys
Main Article Content
Keywords
information and computer technology, information system, image, database, coding
Abstract
Introduction: Images of biological objects are used very often now for the creation of novel information systems for biology and medicine. But the sphere of their use, tasks which are possible to solve with them, can be successfully expanded.
Problem statement: Contemporary biomedical relational databases (DBs) very often include images. But additionally, images themselves can play important functional roles in DBs.
Purpose: To use images of fishes’ genetic codes fragments as keys for the construction of relational DB; this has to ensure the reliability of biomedical information storage better than in prototypes, and to provide better data integrity.
Methods: DB Design, object-oriented system analysis for DBs design in an optimal way, ER diagrams design.
Results: An algorithm for the construction of relational DBs with images, other biomedical information, analytical approaches and recommendations for doing this in an optimal way were presented. The main attention was paid to the creation and application of the most functionally high-quality codes for keys in DB (including primary keys). To perform this function, usages of codes based on images of fishes’ genetic codes fragments were proposed. The example for such a task solution, described in this article, was the creation of DBs with information about fishes (or other aquatic organisms) and chemical inorganic environmental pollutants which affected them.
Conclusions: The results of the use of images of genetic codes fragments as keys for the construction of DBs with ecological data in an information system for environmental monitoring were presented. Through the high level of individualization of the data in a system with such keys the maintenance of species-specific information is substantiated. The work has theoretical and practical values. It may also be applied in an academician process for teaching students.
References
2. Klyuchko OM. Information computer technologies for using in biotechnology: electronic medical information systems. Biotechnologia Acta. 2018;11(3):5-26. https://doi.org/10.15407/biotech11.03.005.
3. Klyuchko OM, Buchatsky LP, Rud YuP, Melezhyk OV. Creation of fish databases for electronic interactive map: tables and keys. Fisheries Science of Ukraine. 2019;50(4):37-57. https://doi.org/10.15407/fsu2019.04.037.
4. Klyuchko OM, Klyuchko ZF. Electronic information systems for monitoring of populations and migrations of insects. Biotechnologia Acta. 2018;11(5):5-25. https://doi.org/10.15407/biotech11.05.005.
5. Eronen L, Toivonen H. Biomine: predicting links between biological entities using network models of heterogeneous databases. Bioinformatics. 2012;13:119. https://doi.org/10.1186/1471-2105-13-119.
6. Chen B-S, Yang S-K, Lan C-Y, Chuang Y-J. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining. Medical Genomics. 2008;1:46. https://doi.org/10.1186/1755-8794-1-46.
7. Pornputtapong N, Wanichthanarak K, Nilsson A, Nookaew I, Nielsen J. A dedicated database system for handling multi-level data in systems biology. Source Code for Biology and Medicine. 2014;9:17. https://doi.org/10.1186/1751-0473-9-17.
8. Stobbe MD, Swertz MA, Thiele I, Rengaw T, van Kampen AHC, Moerland PD. Consensus and conflict cards for metabolic pathway databases. Systems Biology. 2013;7:50. https://doi.org/10.1186/1752-0509-7-50.
9. van Ommen B, Bouwman J, Dragsted LO, et al. Challenges of molecular nutrition research 6: the nutritional phenotype database to store, share and evaluate nutritional systems biology studies. Genes & Nutrition. 2010;5:167. https://doi.org/10.1007/s12263-010-0167-9.
10. Momin AA, James BP, Motter TC, Kadara HN, Powis G, Wistuba II. Integrating whole transcriptome sequence data and public databases for analysis of somatic mutations in tumors. Genome Biology. 2011;12(1):44. https://doi.org/10.1186/gb-2011-12-s1-p44.
11. Stobbe MD, Houten SM, Jansen GA, van Kampen AHC, Moerland PD. Critical assessment of human metabolic pathway databases: a stepping stone for future integration. Systems Biology. 2011;5:165. https://doi.org/10.1186/1752-0509-5-165.
12. Chowbina SR, Wu X, Zhang F, et al. HPD: an online integrated human pathway database enabling systems biology studies. Bioinformatics. 2009;10(11):S5. https://doi.org/10.1186/1471-2105-10-S11-S5.
13. Goldstein AM. The NCBI Databases: an Evolutionist’s Perspective. Evolution: Education and Outreach. 2010;3:258. https://doi.org/10.1007/s12052-010-0258-5.
14. Albà M. Links to molecular biology databases. Genome Biology. 2000;1-4. https://doi.org/10.1186/gb-2000-1-1-reports235.
15. Klyuchko ZF. To the knowledge of owlet moths (Lepidoptera: Noctuidae) of the Sumy Region [Ukraine]. Kharkov Entomological Society Gazette 2004;11(1-2):86-88.
16. Walsh JR, Sen TZ, Dickerson JA. A computational platform to maintain and migrate manual functional annotations for BioCyc databases. Systems Biology. 2014;8:115.
17. Del Rio A, Barbosa AJM, Caporuscio F. Use of large multiconformational databases with structure-based pharmacophore models for fast screening of commercial compound collections. Journal of Cheminformatics. 2011;3(1):P27. https://doi.org/10.1186/1758-2946-3-S1-P27.
18. Maier CV, Long JG, Hemminger BM, Giddings MC. Ultra-Structure database design methodology for managing systems biology data and analyses. Bioinformatics. 2009;10:254. https://doi.org/10.1186/1471-2105-10-254.
19. Tan TW, Xie C, De Silva M, et al. Simple re-instantiation of small databases using cloud computing. Genomics. 2013;14(5):5-13. https://doi.org/10.1186/1471-2164-14-S5-S13.
20. Bouzaglo D, Chasida I, Tsur EE. Distributed retrieval engine for the development of cloud-deployed biological databases. BioData Mining. 2018;11:26. https://doi.org/10.1186/s13040-018-0185-5.
21. Klyuchko OM, Buchatsky LP, Melezhyk OV. Fish information databases construction: data preparation and object-oriented system analysis. Fisheries Science of Ukraine. 2019;49(3):32-47. https://doi.org/10.15407/fsu2019.03.032.
22. Klyuchko ZF, Kononenko VS, Mikkola K. Systematic list of moths (Lepidoptera, Noctuidae) of the Daurian Reserve. Insects of Dauria and adjacent territories. Collection of scientific papers. 1992;1:31-46.
23. User Reference for Fisheries Improvement ProjectsDatabase (FIP-DB) and Query Viewer. https://ru.scribd.com/document/385739269/Readme-File-for-FIP-DB#download. Accessed December 27, 2021.
24. Froese R, Pauli D. FishBase 2000: Concepts, designs and data sources. ICLARM. Los Banos, Philippines; 2000.
25. Arnot JA, Mackay D, Parkerton TF, Bonnell M. A database of fish biotransformation rates for organic chemicals. Environmental Toxicology and Chemistry. 2008;27(11):2263–2270. https://doi.org/10.1897/08-058.1.
26. Tedesco PA, Beauchard О, Bigorne R, et al. A global database on freshwater fish species occurrence in drainage basins. Scientific Data. 2017;4:170141. https://doi.org/10.1038/sdata.2017.141.
27. Klyuchko O.M., Biletsky A.Y., Navrotskyi D.A. Method of application of biotechnical monitoring system with expert subsystem and biosensor. Patent UA 131863 U. G01N33/00, C12Q 1/02, C12N 15/00. u201804663. Publ: 02.11.2019, Bull. 3, 7 p.
28. Klyuchko O.M. Method for monitoring of chemicals influence on bioorganisms in few time intervals. Patent UA 134575 U. G01N33/00, C12N 15/00, A61P 39/00. u201812443 – Publ: 05.27.2019, Bull. 10, 12 p.
29. Daoliang L, Zetian F, Yanqing D. Fish-Expert: a web-based expert system for fish disease diagnosis. Expert Systems with Applications. 2002;23:311–320. https://doi.org/10.1016/S0957-4174(02)00050-7.
30. Klyuchko O.M., Biletsky A.Ya., Navrotskyi D.O. Method of bio-sensor test system application. Patent UA 129923 U. G01N33/00, G01N33/50, C12Q 1/02. u201802896 – Publ: 11.26.2018, Bull. 22, 7 p.
31. Klyuchko OM. On the mathematical methods in biology and medicine. Biotechnologia Acta. 2017;10(3):31–40. https://doi.org/10.15407/biotech10.03.031.
32. Klyuchko OM. Some trends in mathematical modeling for biotechnology. Biotechnologia Acta. 2018;11(1):39–57. https://doi.org/10.15407/biotech11.01.039.
33. Klyuchko OM. Application of artificial neural networks method in biotechnology. Biotechnologia Acta. 2017;10(4):5–13. https://doi.org/10.15407/biotech10.04.005.
34. Gonchar OO, Maznychenko AV, Klyuchko OM, et al. C60 Fullerene Reduces 3-Nirtopropionic Acid-Induced Oxidative Stress Disorders and Mitochondrial Dysfunction in Rats by Modulation of P53, Bcl-2 and Nrf2 Targeted Proteins. International Journal of Molecular Sciences. 2021;22(11):5444-5468. https://doi.org/10.3390/ijms22115444.
35. Microsoft Academy: Methods and means of software engineering. URL: https://www.intuit.ru/studies/courses/2190/237/lecture/6124. Accessed December 24, 2021).
36. Harrington JL. Object-oriented database design clearly explained. USA: Academic Press; 2005.
37. Van der Laan R, Eschmeyer WN, Fricke R. Family-group names of Recent fishes. Zootaxa Monograph. 2014;3882(1):1-230. https://doi.org/10.11646/zootaxa.3882.1.1.
38. Movchan YV. Fishes of Ukraine (taxonomy, nomenclature, remarks). Collection of works of Zoological Museum. 2009; 40:47-87.
39. Fricke R, Eschmeyer WN, van der Laan R. Eschmeyer’s catalog of fishes: genera, species. http://researcharchive.calacademy.org/research/ichthyology/catalog. Accessed December 27, 2021.
40. Di Génova AD, Aravena A, Zapata L, González M, Maass A, Iturra L. SalmonDB: a bioinformatics resource for Salmo salar and Oncorhynchus mykiss. Database (Oxford). 2011; bar050. https://doi.org/10.1093/database/bar050.
41. Klyuchko O.M., Biletsky A.Ya., Lizunova A.G. Method of application of monitoring system with databases and keys in symbolic records of genetic codes of fish and other aquatic organisms. Patent UA 143919 U. C12Q 1/02, G01N33 / 00, G01N33 / 50, G06F 11/20. u201910770 – Publ: 08.25.2020, Bull. 16, 12 p.
42. Klyuchko O.M., Biletsky A.Ya., Lizunova A.G. Method of applying monitoring system with databases and keys in form of images of genetic codes of fish and other aquatic organisms. Patent UA 143918 U. A61B 5/04, G01N33 / 00, G06F 13/00, G06F 16/00. u201910769 – Publ: 08.25.2020, Bull. 16, 12 p.
43. Klyuchko O.M., Biletsky A.Ya., Lizunova A.G. Method of use of monitoring system with databases and keys in form of images of genetic codes of biological organisms. Patent UA 143926 U. A61B5/04, G01N33/ 00, G06F33 / 00, G06F 16/00. u201911292 – Publ: 08.25.2020, Bull. 16, 12 p.
44. Franchuk GM, Isaenko VM. Ecology, aviation and cosmos. Kyiv; 2005: 456.
45. Cyprinus carpio isolate SPL01 chromosome A17, ASM1834038v1, whole genome shotgun sequence. https://www.ncbi.nlm.nih.gov/nuccore/NC_056588.1?from=25191087&to=25198977&report=genbank&strand=true. Accessed January 23, 2021.
46. Barbus borysthenicus isolate PK-977 cytochrome b (cytb) gene, partial cds; mitochondrial gene for mitochondrial product. https://www.ncbi.nlm.nih.gov/nuccore/AY331026.1. Accessed January 23, 2021.
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Olena M. Klyuchko, National Aviation University, Kyiv, Ukraine
Ph.D. (biophysics, biology), M.D. (information technologies), Associate Professor of Electronics, Robotics, Monitoring and IoT (ERMIT) Department, of the Faculty of Aeronavigation, Electronics and Telecommunications (FAET)
Olga V. Melezhyk, Open International University of Human Development “Ukraine”, Kyiv, Ukraine
Associate Professor of Biological Faculty