AIMS Genetics, 2017, 4(2): 138-165. doi: 10.3934/genet.2017.2.138.

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In-silico based identification and functional analyses of miRNAs and their targets in Cowpea (Vigna unguiculata L.)

Department of Botany, University of Balochistan, Sariab Road, Quetta, Pakistan

Academic Editor:William Cho

Cowpea (Vigna unguiculata L.) is an important leguminous plant and a good diet due to presence of carbohydrate and high protein contents. Currently, only few cowpea microRNAs (miRNAs) are reported. This study is intended to identify and functionally analyze new miRNAs and their targets in cowpea. An in-silico based homology search approach was applied and a total of 46 new miRNAs belonging to 45 families were identified and functionally annotated from the cowpea expressed sequence tags (ESTs). All these potential miRNAs are reported here for the first time in cowpea. The 46 new miRNAs were also observed with stable hairpin structures with minimum free energy, ranging from −10 to −132 kcal mol−1 with an average of −40 kcal mol−1. The length of new cowpea miRNAs are ranged from 18 to 26 nt with an average of 21 nt. The cowpea miRNA-vun-mir4414, is found as pre-miRNA cluster for the first time in cowpea. Furthermore, a set of 138 protein targets were also identified for these newly identified 46 cowpea miRNAs. These targets have significant role in various biological processes, like metabolism, transcription regulation as transcription factor, cell transport, signal transduction, growth & development and structural proteins. These findings are the significant basis to utilize and manage this important leguminous plant-cowpea for better nutritional properties and tolerance for biotic and abiotic stresses.
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Keywords microRNAs; conserved nature; cowpea (Vigna unguiculata L.); homology search

Citation: Zareen Gul, Muhammad Younas Khan Barozai, Muhammad Din. In-silico based identification and functional analyses of miRNAs and their targets in Cowpea (Vigna unguiculata L.). AIMS Genetics, 2017, 4(2): 138-165. doi: 10.3934/genet.2017.2.138

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