Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM)
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Abstract. Weerakoon SR, Somaratne S. 2021. Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM). Asian J Agric 5: 35-43. Weedy rice (WR) (Oryza sativa f. spontanea) has become a major threat in rice cultivation. Discrimination of WR from cultivated rice is difficult since agro-morphology of WR and cultivated rice are overlapping. Molecular markers are useful and can be an informative tool for estimating genetic diversity and relationships in closely related WR eco-types. Self-Organizing Maps (SOM) is an interesting and promising classification tool employing an innovative and data-driven classification method based on unsupervised artificial neural networks. The present study focused on exploring the potential use of SOM to classify WR populations of different eco-climatic zones in Sri Lanka using agro-morphological and molecular data. Separate SOMs for each set of variables, agro-morphological and molecular data were developed. The best SOM was chosen based on the error performance. Findings of SOM analyses showed that certain morphological characters (seedling height, leaf blade width, leaf blade length, culm strength, panicle shattering, seed coat color and leaf angle) and certain molecular characters detected from SSR primers (RM 11, RM 21, RM 14, and RM 280) are important in separation of different WR eco-types satisfactorily. SOM clustering of cultivated, wild, and WR eco-types indicated specific patterns of grouping with respect to climatic conditions of the country. WR eco-types in dry zone and wet zone of the country are closely related to Oryza nivara and O. rufipogon respectively.
2017-01-01
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References
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21. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69.22.
22. Kohonen, T. (1998).The self-organizing map, Neurocomputing, 21, 1-6.
23. Kohonen, T. (2001). Software Tools for SOM. Self-organizing maps, 30 : 3: 311-328.
24. Agarwal P. & Skupin, A. Self-Organising Maps: Applications in Geographic Information Science,Wiley-Blackwell, Chichester, England, 2008.
25. Brosse, S., Giraudel, J.L. & Lek, S. (2001). Utilization of non-supervised neural networks and principal component analysis to study fish assemblages. Ecological Modelling,146, 159-166.
26. Vesanto, J. & Alhoniemi, E. (2000.) Clustering of the Self-Organizing Map. IEEE on Neural Networks, 11, 586-600.
27. Lek, S. & Gue gan, J.F. (2000). Arti?cial Neuronal Networks: application to ecology and evolution. Springer-Verlag, Berlin, Germany.
28. Fernandez, C., Soria, E., Martin, J.D.&Serrano, A.J. (2006). "Neural Networks for animal science applications: Two case studies," Expert Systems with Applications,31, 444-450.
29. Schreer, J.F., O'Hara Hines, R.J. & Kovacs, K.M. (1998). Classification of Dive Profiles: A Comparison of Statistical Clustering Techniques and Unsupervised Artificial Neural Networks, Journal of Agriculture , Biological, and Environmental Statistics,3, 383-404.
30. Li, S.T. (2002). A web-aware interoperable data mining system, Expert Systems with Applications, vol. 22.
31. Moshou, D., Vrindts E., Ketelaere B., Baerdemaeker J. & Ramon, H. (2001). A neural network based plant classifier, Computers and Electronics in Agriculture
vol. 31,1, 15–16.
32. Hemming, J. & Rath, T. (2001). “Computer-vision-based weed identification under field conditions using controlled lighting”, Journal of Agricultural Engineering Research. vol. 78, 3, 233-243.
33.Walley, W.J., Martin, R.W. & O’Connor, M.A. (2000). Self-organizing maps for the classi?cation of river quality from biological and environmental data. In: Denzer, R., Swayne, D.A., Purvis, M., Schimak, G. (Eds.), Environmental Software Systems: Environmental Information and Decision Support. Kluwer Academic, Dordrecht, pp. 27–41.
34. PGRC (1999). Characterization Catalogue of Rice (Oryza sativa) Department of Agriculture., Ministry of Agriculture and Lands, Sri Lanka.
35. Schuelke, M. (2000). An economic method for the fluorescent labeling of PCR fragments. Nature Biotechnology.18:2, 233-234.
36. Oja, M., Kaski, S. &Kohonen, T. (2002).Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001 Addendum. Neural Computing Surveys, 3, 1-156.
37. Kohonen, T. (2002).Self-Organizing Maps. Springer Series in Information Sciences, Springer-Verlag, Berlin Heidelberg 30: 3rd ed.
38. Kaski, S. (1996). Exploratory Data Analysis by the Self-Organizing Map: Structure of Welfare and Poverty in the World. In Neural Networks in Financial Engineering. Proceedings of the Third International Conference on Neural Networks in the Capital Markets, Singapore, 498-507.
39. Deboeck, G.& Kohonen, T. (1998).Visual Explorations in Finance with Self-Organizing Maps. London: Springer-Verlag.
40.Vesanto, J. (2000). Using SOM in Data Mining," Thesis for the degree of Licentiate of Science in Technology. Espoo, Finland.
41.Kiviluoto, K.(1996)."Topology Preservation in Self-Organizing Maps." Proceedings of International Conference on Neural Networks (ICNN), 294-299.
42. Arsuaga, U.E.& Diaz, M.F. (2005). Topology Preservation in SOM, International Journal of Applied Mathematics and Computer Sciences 1;1, 19-22.
43. Tamayo, P. , Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S, Dmitrovsky, E., Lander E.S. & Golub, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation . Proc. Natl. Acad. Sci. USA Vol. 96, pp. 2907–2912.
44. Mele P. M. & Crowley D. E. (2008) Application of self-organizing maps for assessing soil biological quality. Agriculture, Ecosystems & Environment 126, 139-52.
45. Chen, L.J., Lee, D.S., Song, Z.P., Suh, H.S. & Lu, B.R. (2004).Gene Flow from Cultivated Rice (Oryza sativa) to its Weedy and Wild Relatives. Annals of Botany 93, 67-73.
2. Mortimer, M., Pandey, S. & Piggin, C. (2000). Weedy rice approaches to ecological appraisal and implications for research priorities. In: Baki BB, Chin DV, Mortimer M, eds. Proceedings of Wild and Weedy Rice in Rice Ecosystems in Asia. A review, Los Banes, Philippines: International Rice Research Institute. 97-105.
3. Chauhan, B.S.& Johnson, D.E. (2010).Weedy rice (Oryza sativa L.). I. Grain characteristics and growth response to competition of weedy rice variants of five Asian countries Weed Science 58,374-380.
4. Chauhan, B.S. (2012).Weedy rice (Oryza sativa). II. Response of weedy rice to seed burial and flooding depth. Weed Science 60, 385-388.
5. Azmi, M. & Karim, S.M.R. (2008). Weedy Rice: Biology, Ecology and Management. Malaysian Agricultural Research and Development Institute (MARDI), Kuala Lumpur, Malaysia.
6 . Londo J. P. and Schaal B. A. 2007. Origins and population genetics of weedy red rice in the USA. Mol. Ecol. 16: 4523–4535.
7.Abeysekara, A.S.K., Nugaliyadda, L., Herath, H.M.S., Wickrame, U.B.& Iqbal, Y.B. (2010).Weedy Rice: a threat to direct seeded rice cultivation in Sri Lanka. Rice Congress 2010, PGRC, Gannoruwa.17-18.
8. Marambe, B. & Amarasinghe, L.(2000).Weedy rice in Sri Lanka. In Wild and Weedy Rice in Rice Eco systems in Asia- A Review. (Eds. B.B. Baki, D.V Chin, A.M. Motimer), International Rice Research Institute Philippines. 79-82.
9. Karunarathna, K.D.K., Weerakoon, S.R., Somaratne, S. & Weerasena, O.V.D.S.J. (2014).Molecular and Agro-morphological affinities among weedy, wild and cultivated rice varieties in different climatic zones of Sri Lanka. OUSL Annual Academic Sessions, 2014: 319-323.
10.Sun, C.Q., Wang, X.K., Li, Z.C., Yoshimura, A. &Iwata, N. (2001).Comparison of the genetic diversity of common wild rice (Oryza rufipogon Griff.) and cultivated rice (O. sativa L.) using RFLP markers. Theoretical and Applied Genetics, 102: 1,157-162.
11. Qian, W., Ge, S. & Hong, D.Y. (2001).Genetic variation within and among populations of a wild rice Oryza granulata from China detected by RAPD and ISSR markers. Theoretical and Applied Genetics, 102:2:440-449.
12. Ipek, M., Ipek, A., Almquis, T.S.G.& Simon, P.W. (2005).Demonstration of linkage and development of the first low-density genetic map of garlic, based on AFLP markers. Theoretical and Applied Genetics, 110: 2, 228-236.
13. Yan, W.G., Rutger, J.N., Bryant, R.J., Bockelman, H.E., Fjellstrom, R.G., Chen, M.H., Tai, T.H. & McClung, A.M. (2007). Development and evaluation of a core subset of the USDA rice germplasm collection. Crop Science, 47: 2, 869-876.
14. Hayashi, K., Hashimoto, N., Daigen, M.& Ashikawa, I. (2004).Development of PCR-based SNP markers for rice blast resistance genes at the Piz locus. Theoretical and Applied Genetics,108:7, 1212- 1220.
15. Gwag, J.G., Dixit, A., Park, Y.J., MA, K.H., Kwon, S.J., Cho, G.T., Lee, G.A., Lee, S.Y., Kang, H.K. and Lee, S.H. (2010). Assessment of genetic diversity and population structure in mungbean. Genes & Genomics, vol. 32, no. 4, p. 299-308.
16. Zhang, P., LI, J.Q., Li, X.L., Liu, X.D., Zhao, X.J. &Lu, Y.G. (2011). Population structure and genetic diversity in a rice core collection (Oryza sativa L.) investigated with SSR markers. PLoS ONE, 6:12, 27565.
17. McKhann, H.I, Camilleri, C., Berard, A., Bataillon, T., David, J.L., Reboud, X., lecorre, V., Caloustian, C., Gut, I.G. & Brunel, D. (2004). Nested core collections maximizing genetic diversity in Arabidopsis thaliana. The Plant Journal, 38:1, 193-202.
18.Upadhyaya, H.D., Gowda, C.L.L., Pundir, R.P.S., Reddy, V.G. & Singh S. (2006). Development of core subset of finger millet germplasm using geographical origin and data on 14 quantitative traits. Genetic Resources and Crop Evolution, 53: 4, 679-685.
19. Dallwitz, M.J. (1980). "A general system for coding taxonomic descriptions," Taxon, 29, 41–46.
20. Haykin, S. (1999). .Neural Networks : A Comprehensive Foundation, 2 ed. Upper Saddle River, NJ: Prentice Hall.
21. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69.22.
22. Kohonen, T. (1998).The self-organizing map, Neurocomputing, 21, 1-6.
23. Kohonen, T. (2001). Software Tools for SOM. Self-organizing maps, 30 : 3: 311-328.
24. Agarwal P. & Skupin, A. Self-Organising Maps: Applications in Geographic Information Science,Wiley-Blackwell, Chichester, England, 2008.
25. Brosse, S., Giraudel, J.L. & Lek, S. (2001). Utilization of non-supervised neural networks and principal component analysis to study fish assemblages. Ecological Modelling,146, 159-166.
26. Vesanto, J. & Alhoniemi, E. (2000.) Clustering of the Self-Organizing Map. IEEE on Neural Networks, 11, 586-600.
27. Lek, S. & Gue gan, J.F. (2000). Arti?cial Neuronal Networks: application to ecology and evolution. Springer-Verlag, Berlin, Germany.
28. Fernandez, C., Soria, E., Martin, J.D.&Serrano, A.J. (2006). "Neural Networks for animal science applications: Two case studies," Expert Systems with Applications,31, 444-450.
29. Schreer, J.F., O'Hara Hines, R.J. & Kovacs, K.M. (1998). Classification of Dive Profiles: A Comparison of Statistical Clustering Techniques and Unsupervised Artificial Neural Networks, Journal of Agriculture , Biological, and Environmental Statistics,3, 383-404.
30. Li, S.T. (2002). A web-aware interoperable data mining system, Expert Systems with Applications, vol. 22.
31. Moshou, D., Vrindts E., Ketelaere B., Baerdemaeker J. & Ramon, H. (2001). A neural network based plant classifier, Computers and Electronics in Agriculture
vol. 31,1, 15–16.
32. Hemming, J. & Rath, T. (2001). “Computer-vision-based weed identification under field conditions using controlled lighting”, Journal of Agricultural Engineering Research. vol. 78, 3, 233-243.
33.Walley, W.J., Martin, R.W. & O’Connor, M.A. (2000). Self-organizing maps for the classi?cation of river quality from biological and environmental data. In: Denzer, R., Swayne, D.A., Purvis, M., Schimak, G. (Eds.), Environmental Software Systems: Environmental Information and Decision Support. Kluwer Academic, Dordrecht, pp. 27–41.
34. PGRC (1999). Characterization Catalogue of Rice (Oryza sativa) Department of Agriculture., Ministry of Agriculture and Lands, Sri Lanka.
35. Schuelke, M. (2000). An economic method for the fluorescent labeling of PCR fragments. Nature Biotechnology.18:2, 233-234.
36. Oja, M., Kaski, S. &Kohonen, T. (2002).Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001 Addendum. Neural Computing Surveys, 3, 1-156.
37. Kohonen, T. (2002).Self-Organizing Maps. Springer Series in Information Sciences, Springer-Verlag, Berlin Heidelberg 30: 3rd ed.
38. Kaski, S. (1996). Exploratory Data Analysis by the Self-Organizing Map: Structure of Welfare and Poverty in the World. In Neural Networks in Financial Engineering. Proceedings of the Third International Conference on Neural Networks in the Capital Markets, Singapore, 498-507.
39. Deboeck, G.& Kohonen, T. (1998).Visual Explorations in Finance with Self-Organizing Maps. London: Springer-Verlag.
40.Vesanto, J. (2000). Using SOM in Data Mining," Thesis for the degree of Licentiate of Science in Technology. Espoo, Finland.
41.Kiviluoto, K.(1996)."Topology Preservation in Self-Organizing Maps." Proceedings of International Conference on Neural Networks (ICNN), 294-299.
42. Arsuaga, U.E.& Diaz, M.F. (2005). Topology Preservation in SOM, International Journal of Applied Mathematics and Computer Sciences 1;1, 19-22.
43. Tamayo, P. , Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S, Dmitrovsky, E., Lander E.S. & Golub, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation . Proc. Natl. Acad. Sci. USA Vol. 96, pp. 2907–2912.
44. Mele P. M. & Crowley D. E. (2008) Application of self-organizing maps for assessing soil biological quality. Agriculture, Ecosystems & Environment 126, 139-52.
45. Chen, L.J., Lee, D.S., Song, Z.P., Suh, H.S. & Lu, B.R. (2004).Gene Flow from Cultivated Rice (Oryza sativa) to its Weedy and Wild Relatives. Annals of Botany 93, 67-73.