Analysis of some conserved miRNAs in hazelnut (Corylus avellena L. and Corylus colurna L.) by real-time PCR

Year 2021, Volume: 39 Issue: 2, 170 - 176, 02.06.2021

Büşra Yirmibeş Hatice Nur Aydın Zehra Ömeroğlu Ulu Salih Ulu Nehir Özdemir Özgentürk

Abstract

Hazelnut is an important plant species which is used in food industry, dye industry, woodchopping and stock farming and it has also benefits for health due to nutrient component. Economically valuable Corylus avellena and Corylus colurna used as rootstock are the most common cultivars. Although many studies have been made about microRNA in plants so far, there are few studies in hazelnut. miRNAs are 18-25 nucleotide, short and single strand non-coding RNAs. miRNAs called as post-transcriptional gene regulators cause repress or cleavage of their target mRNA. In particularly in plants, they cause cleavage of mRNA and so play role in developmental process, response process to biotic and abiotic stresses like drought, salt, cold or UV. Conserved miRNAs are miRNAs which have same function in different plant species and are conserved from very old times to the present. In this study, we aimed that analyzing of some conserved miRNAs (miR159, miR160, miR171, miR396, miR2919 and miR8123) in hazelnut (Corylus avellena L. and Corylus colurna L.) by Real-Time PCR. We found which these conserved miRNAs are present in both hazelnut species.

Keywords

Conserved microRNA, hazelnut, Corylus avellena L., Corylus colurna L., real-time PCR, cDNA

References

• [1] Başaran Filik, Ü., Filik, T., & Gerek, Ö. N. A hysteresis model for fixed and sun tracking solar PV power generation systems. Energies; 2018, p. 603.
• [2] Alzahrani, A., Shamsi, P., Dagli, C. and Ferdowsi, M. Solar irradiance forecasting using deep neural networks. Procedia Computer Science; 2017, p. 304-313.
• [3] Benali, L., Notton, G., Fouilloy, A., Voyant, C., & Dizene, R. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components. Renewable Energy; 2019, p. 871–884.
• [4] Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. Time series analysis: forecasting and control. John Wiley & Sons; 2015.
• [5] Chaturvedi, D. K. Solar Power Forecasting: A Review, 145(6); 2016, p. 28–50.
• [6] Collares-Pereira, M., & Rabl, A. The average distribution of solar radiationcorrelations between diffuse and hemispherical and between daily and hourly insolation values. Solar Energy, 22(2), 1979, p. 155–164.
• [7] Gueymard, C. Mean daily averages of beam radiation received by tilted surfaces as affected by the atmosphere. Solar Energy, 37(4), p. 1986, 261–267.
• [8] Http-2. date of access: July 5, 2019, from http://www.visualgenedeveloper.net/; 2019.
• [9] Jain, P. C. Comparison of techniques for the estimation of daily global irradiation and a new technique for the estimation of hourly global irradiation. Solar & Wind Technology, 1(2), 1984, p. 123–134.
• [10] Jain, P. C. Estimation of monthly average hourly global and diffuse irradiation. Solar & Wind Technology, 5(1), 1988, p. 7–14.
• [11] Kalogirou, S. A. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews, 5(4), 2001, p. 373–401.
• [12] Liu, B. Y., & Jordan, R. C. The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Solar Energy, 4(3), 1960, p. 1–19.
• [13] Ryu, S., Noh, J., & Kim, H. Deep neural network-based demand side short term load forecasting. Energies, 10(1), 3, 2017.
• [14] Ayvazoğluyüksel, Ö. and Filik Başaran Ü. Estimation methods of global solar radiation, cell temperature and solar power forecasting: A review and case study in Eskişehir. Renewable and Sustainable Energy Reviews, 91, 2018, p. 639-653
• [15] Atique, S., Noureen, S., Roy, V., Subburaj, V., Bayne, S., & Macfie, J. Forecasting of total daily solar energy generation using ARIMA: A case study. In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), 2019, p. 114-119.
• [16] Sivhugwana, K. S., & Ranganai, E. Intelligent techniques, harmonically coupled and SARIMA models in forecasting solar radiation data: A hybridisation approach. Journal of Energy in Southern Africa, 31(3), 2020, p. 14-37.
• [17] Craggs, C., Conway, E. and Pearsall N. M. Stochastic modelling of solar irradiance on horizontal and vertical planes at a northerly location. Renewable Energy 18; 1999, p. 445-463
• [18] Soubdhan, T., Voyant, C., & Lauret, P. Influence of Global Solar Radiation Typical Days on Forecasting Models Error. The Third Southern African Solar Energy Conference (SASEC2015); 2015.
• [19] Sun, Y. Deep Neural Network Regression and Sobol Sensitivity Analysis for Daily Solar Energy Prediction Given Weather Data. Purdue University; 2018.
• [20] Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., & Fouilloy, A. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 2017, p. 569–582.