The use of a self-organizing feature map for the treatment of the results of RAPD and ISSR analyses in studies on the genomic polymorphism in the genus Capsicum L.

Abstract

The results of studies based on multilocus molecular analyses, including random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR), and amplified fragment length polymorphism (AFLP) analyses, are usually presented in the form of images (electrophoregrams, photographs, etc.). The interpretation of this information is complicated, labor-consuming, and subjective. Artificial neural networks (ANNs), which are ideal “image processors,” may be useful when solving such tasks. The possibility of using ANNs for the treatment of the results of RAPD and ISSR analyses has been studied. The RAPD and ISSR fragment spectra of the genus Capsicum L. (peppers) were used in this study. The results of clustering the accessions studied by means of the unweighted pair-group method with arithmetic averages (UPGMA), which is often used for phylogenetic constructions based on RAPD and ISSR data, serve as expert estimates. Fundamentally new methods of genetic polymorphism estimation using ANN technologies, namely, self-organizing feature maps (SOFMs) have been developed. The results show that the clusters obtained with the use of UPGMA and SOFM coincide by more than 90%; taking into account that ANNs can deal with high noise levels and incomplete or contradictory data, the approach proposed may prove to be efficient.