PRGPred: A platform for prediction of domains of resistance gene analogue (RGA) in Arecaceae developed using machine learning algorithms
2015
MATHODIYIL S. MANJULA | KAITHERI E. RACHANA | SUDALAIMUTHU NAGANEESWARAN | NAMBISAN HEMALATHA | ANITHA KARUN | MULIYAR K. RAJESH
Plant disease resistance genes (R-genes) are responsible for initiation of defense mechanism against various phytopathogens. The majority of plant R-genes are members of very large multi-gene families, which encode structurally related proteins containing nucleotide binding site domains (NBS) and C-terminal leucine rich repeats (LRR). Other classes possess' an extracellular LRR domain, a transmembrane domain and sometimes, an intracellular serine/threonine kinase domain. R-proteins work in pathogen perception and/or the activation of conserved defense signaling networks. In the present study, sequences representing resistance gene analogues (RGAs) of coconut, arecanut, oil palm and date palm were collected from NCBI, sorted based on domains and assembled into a database. The sequences were analyzed in PRINTS database to find out the conserved domains and their motifs present in the RGAs. Based on these domains, we have also developed a tool to predict the domains of palm R-genes using various machine learning algorithms. The model files were selected based on the performance of the best classifier in training and testing. All these information is stored and made available in the online ‘PRGpred' database and prediction tool.
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