Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/3654
Title: In silico identification of microRNAs and their target genes in watermelon (Citrullus lanatus)
Authors: Zakeela, M. C. A.
Safeena, M. I. S.
Komathy, T.
Keywords: In silico identification
Citrullus lanatus
miRNA
Stress response
Expressed sequence tag (EST)
Issue Date: Mar-2019
Publisher: Elsevier
Citation: Scientia Horticulturae, (252)27: 55-60.
Abstract: Watermelon (Citrullus lanatus) is an annual herbaceous crop, mainly grown for its fleshy fruit, which is an excellent source of vitamins and therefore fetches a huge market demand all over the world. Yield of watermelon can be improved by increasing the productivity that relies on plant growth, development and stress response. MicroRNAs (miRNAs), a group of short (∼22 nt), noncoding, endogenous and evolutionary conserved post-transcriptional regulatory RNAs, play a major role in various biological and metabolic processes associated with growth, development and stress response. In this study, we deployed a comparative genomics-based approach using expressed sequence tags (ESTs) to identify and characterize potential miRNAs and their target genes in watermelon. Three potential miRNAs (cla-miR-2515, cla-miR482d and cla-miR-8461) were predicted from 17,594 publicly available watermelon EST sequences, according to a series of filtering criteria used in the protocol. Moreover, 21 potential mRNA targets encoding for 19 different proteins linked to various metabolic processes such as inositol-3-phosphate synthase activity, metal ion binding and oxidoreductase activity were identified in watermelon genome, based on perfect or nearly perfect complementarity of miRNAs with their targeted mRNA sequences. Phylogenetic analysis of these miRNAs revealed that cla-miR8461 and cla-miR2515 are unique miRNAs closely related to Schistosoma mansoni and Drosophila pseudoobscura, respectively.
URI: https://www.sciencedirect.com/science/article/pii/S0304423819300883#!
Appears in Collections:Research Articles



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