Proposal of a Relational Database (SQL) for Zoological Research of Epigeic Synusion

  • Vladimír Langraf Department of Zoology and Anthropology, Constantine the Philosopher University in Nitra, Slovak Republic
  • Kornélia Petrovičová Department of Environment and Zoology, Slovak University of Agriculture in Nitra, Slovak Republic
  • Stanislav David Department of Ecology and Environmental Sciences, Constantine the Philosopher University in Nitra, SlovakRepublic
  • Zuzana Krumpálová Department of Ecology and Environmental Sciences, Constantine the Philosopher University in Nitra, SlovakRepublic
  • Adrián Purkart Department of Zoology, Comenius University, Slovak Republic
  • Janka Schlarmannová Department of Zoology and Anthropology, Constantine the Philosopher University in Nitra, Slovak Republic
Keywords: Big data, SQL, SSMS, biology, zoology, epigeic groups

Abstract

In recent years, developments in the field of molecular biology and genetics have led to the increase in biological information stored in databases. The same increase in the volume of information occurred in the field of zoology, but the development of databases was not addressed in this area. We prepared a relational database and its diagram in the Microsoft SQL Server Management Studio (SSMS) database program. Our results represent experience with construction of a new database design for the zoology field with a focus on research of epigeic groups. The structure of the database will help with meta-analyzes with the objective to identify zoological and ecological relationships and responses to anthropic intervention.

References

http://www.sopsr.sk/web/ [online, accessed 15 June 2021].

https://ibot.sav.sk/cdf/ [online, accessed 15 June 2021].

https://pladias.cz/en [online, accessed 15 June 2021].

https://www.biolib.cz/ [online, accessed 15 June 2021].

Altschul, S., Gish, W., Miller, W., et al. Basic Local Alignment Search Tool. Journal of Molecular Biology 215 (1990), 403-410.

Benham, S., et al. Taxus baccata in Europe: Distribution, habitat, usage and threats. Publications Office of the EU: Luxembourg, 2016.

Benson, D., Karsch-Mizrachi, I., Lipman, D., et al. GenBank. Nucleic Acids Res 28 (2000), 15-18.

Benson, D., Karsch-Mizrachi, I., Lipman, D., et al. GenBank. Nucleic Acids Res 42 (2014), 7-32.

Bernstein, F., Koetzle, T., Williams, G., et al. The protein data bank: A computer-based archival le for macromolecular structures. Journal of Molecular Biology 112, 3 (1977), 535-542.

Birney, E., and Clamp, M. Biological database design and implementation. Briefings in Bioinformatics 5, 1 (2004), 31-38.

Bourne, P. Will a biological database be different from a biological journal? PLOS Computational Biology 1, 3 (2005).

Bourne, P. E., et al. Macromolecular crystallographic information le. In Macromolecular Crystallography Part B, vol. 277 of Methods in Enzymology. Academic Press, 1997, pp. 571-590.

Bradley, A. R., Rose, A. S., Pavelka, A., et al. Mmtf-an efficient le format for the transmission, visualization, and analysis of macromolecular structures. PLOS Computational Biology 13 (2017), 1-16.

Burge, S. W., et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Research 41, D1 (2012), D226-D232.

Clarkson, C., et al. In vitro antiplasmodial activity of medicinal plants native to or naturalised in south africa. Journal of ethnopharmacology 92 (2004), 177-91.

Dalmaris, E., et al. Dataset of targeted metabolite analysis for ve taxanes of hellenic taxus baccata l. populations. Data 5, 1 (2020).

Davidson, S., Overton, C., Tannen, V., and Wong, L. Biokleisli: A Digital Library for Biomedical Researchers. International Journal on Digital Libraries 1 (1997), 36-53.

Dawson, W., and Kawai, G. Modeling the chain entropy of biopolymers: Unifying two different random walk models under one framework. Journal of Computer Science & Systems Biology 2 (2009), 1-23.

de Lorenzo, V., et al. The power of synthetic biology for bioproduction, remediation and pollution control. EMBO reports 19, 4 (2018), e45658.

Duggirala, S. Newsql databases and scalable in-memory analytics. In A Deep Dive into NoSQL Databases: The Use Cases and Applications, P. Raj and G. C. Deka, Eds., vol. 109 of Advances in Computers. Elsevier, 2018, pp. 49-76.

Duigou, T., du Lac, M., Carbonell, P., and Faulon, J.-L. RetroRules: a database of reaction rules for engineering biology. Nucleic Acids Research 47, D1 (2018), D1229-D1235.

Fabricant, D., and Farnsworth, N. The value of plants used in traditional medicine for drug discovery. Environmental Health Perspectives 109 (2001), 69-75.

Fazekas, D., et al. SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks. BMC Syst Biol 7 (2013), 7.

Feld, C., et al. Indicators for biodiversity and ecosystem services: towards an improved framework for ecosystems assessment. Biodivers Conserv 19 (2010), 2895-2919.

Gharajeh, M. Biological big data analytics. In A Deep Dive into NoSQL Databases: The Use Cases and Applications, P. Raj and G. C. Deka, Eds., vol. 109 of Advances in Computers. Elsevier, 2018, pp. 1-48.

Gharajeh, M. S. A Learning Analytics Approach for Job Scheduling on Cloud Servers. Springer International Publishing, Cham, 2017, pp. 269-302.

Heink, U., and Kowarik, I. What criteria should be used to select biodiversity indicators? Biodivers Conserv 19 (2010), 3769-3797.

Hoskeri, J., Krishna, V., and Amruthavalli, C. Functional annotation of conserved hypothetical proteins in rickettsia massiliae mtu5. Journal of Computer Science & Systems Biology 3 (2010), 50-52.

Hudson, L. N., et al. The predicts database: a global database of how local terrestrial biodiversity responds to human impacts. Ecology and Evolution 4, 24 (2014), 4701-4735.

Kashyap, H., et al. Big data analytics in bioinformatics: A machine learning perspective. arXiv 1506.05101 (2015).

Kinjo, A., et al. Protein data bank japan (pdbj): updated user interfaces, resource description framework, analysis tools for large structures. Nucleic Acids Research 45 (2017), D282-D288.

Microsoft. Microsoft SQL Server. 2017: (RTM) - 14.0.1000.169 (X64) Aug 22 2017 17:04:49 Copyright (C) 2017 Microsoft Corporation Express Edition (64-bit) on Windows 10 Home 10.0 [X64] (Build 18362:).

Nielsen, J., and Keasling, J. D. Engineering cellular metabolism. Cell 164 (2016), 1185-1197.

Pearson, W., and Lipman, D. Improved Tools for Biological Sequence Comparison. Proceedings of the National Academy of Science USA 85 (1988), 2444-2448.

Pejic Bach, M., Bertoncel, T., Mesko, M., Susa Vugec, D., and Ivancic, L. Big data usage in european countries: Cluster analysis approach. Data 5, 1 (2020).

Ponten, F., Schwenk, J. M., Asplund, A., and Edqvist, P.-H. D. The human protein atlas as a proteomic resource for biomarker discovery. Journal of Internal Medicine 270, 5 (2011), 428-446.

Ragunath, P. K., Venkatesan, P., and Ravimohan, R. New curriculum design model for bioinformatics postgraduate program using systems biology approach. Journal of Computer Science & Systems Biology 2 (2009), 300-305.

Raj, P. A detailed analysis of nosql and newsql databases for bigdata analytics and distributed computing. In A Deep Dive into NoSQL Databases: The Use Cases and Applications, P. Raj and G. C. Deka, Eds., vol. 109 of Advances in Computers. Elsevier, 2018, pp. 1-48.

Rose, P. W., et al. The rcsb protein data bank: redesigned web site and web services. Nucleic Acids Research 39 (2011), D392-D401.

Shanthi, V., Ramanathan, K., and Sethumadhavan, R. Role of the cation-pi interaction in therapeutic proteins: A comparative study with conventional stabilizing forces. Journal of Computer Science & Systems Biology 2 (2009), 51-68.

Singh, S., et al. Comparative modeling study of the 3-d structure of small delta antigen protein of hepatitis delta virus. Journal of Computer Science & Systems Biology 3 (2010), 1-4.

Srinivasa, K., and Hiriyannaiah, S. Comparative study of different in-memory (no/new) sql databases. In A Deep Dive into NoSQL Databases: The Use Cases and Applications, P. Raj and G. C. Deka, Eds., vol. 109 of Advances in Computers. Elsevier, 2018, pp. 133-156.

Stratton, M., Campbell, P., and Futreal, P. The cancer genome. Nature 458 (2009), 719-724.

Toomula, N., Kumar, A., Kumar, D. S., and Bheemidi, V. S. Biological databases - integration of life science data. Journal of Computer Science & Systems Biology 4 (2012), 87-92.

Turner, V., Gantz, J., and Minton, S. The digital universe of opportunities: Rich data and the increasing value of the internet of things. Tech. rep., 2014.

Vaseeharan, B., and Sivakamavalli, J. In silico homology modeling of prophenoloxidase activating factor serine proteinase gene from the haemocytes of fenneropenaeus indicus. Journal of Proteomics & Bioinformatics 4 (2011), 53-57.

Velankar, S., et al. Pdbe: improved accessibility of macromolecular structure data from pdb and emdb. Nucleic Acids Research 44 (2016), D385-D395.

Wheeler, N., et al. Effects of genetic, epigenetic, and environmental factors on taxol content in taxus brevifolia and related species. Journal of natural products 55 (1992), 432-440.

Published
2021-06-21
How to Cite
[1]
Langraf, V., Petrovičová, K., David, S., Krumpálová, Z., Purkart, A. and Schlarmannová, J. 2021. Proposal of a Relational Database (SQL) for Zoological Research of Epigeic Synusion. MENDEL. 27, 1 (Jun. 2021), 23-28. DOI:https://doi.org/10.13164/mendel.2021.1.023.
Section
Articles