MicroRNAs (miRNAs) are 20-24 nucleotides long non-coding RNAs known to play important regulatory roles during biotic and abiotic stresses by controlling gene expression. Blackgram (Vigna mungo), an economically important grain legume is highly susceptible to pathogenic begomovirus Mungbean Yellow Mosaic India Virus (MYMIV) and resulting in high yield loss. In this study two different leaf-small-RNA libraries were prepared from the pooled RNA at three different time points of resistant V. mungo inbred line VM84 inoculated either with viruliferous or non-viruliferous whiteflies carrying MYMIV and performed high-throughput Illumina sequencing. Sequencing followed by bioinformatics analysis of the small RNA reads indicated that the expression patterns of most of the known and novel miRNAs were altered in resistant line over mock-inoculated sample during the plant virus incompatible interaction. Highly altered miRNAs belong to the families of miR156, miR159, miR160, miR166, miR398, miR1511, miR1514, miR2118 and novel vmu-miRn7, vmu-miRn8, vmu-miRn13 and vmu-miRn14. These results were validated using qPCR, and most of the miRNAs showed similar pattern of expression like that of Illumina reads. The expression patterns of some selected known and novel miRNAs were also compared between the infected MYMIV-resistant and-susceptible genotypes and most of these were modulated after MYMIV-inoculation. Target transcripts like NB-LRR, NAC, MYB, Zinc finger, CCAAT-box transcription factor, fructose 2-6 bisphosphate, HDZIP protein that confers immune response were predicted as targets amongst identified miRNAs using psRNATarget server. Some selected target transcripts including NB-LRR, ARF, SOD, SPB, Basic blue copper protein were validated and their differential expression were demonstrated between MYMIV-resistant and susceptible V. mungo by qPCR data analyses. In the present study we have identified miRNAs that implicate in the regulation of MYMIV-induced stress response in V. mungo; and generated genomic resources for a non-model legume with the aid of bioinformatics tools supplemented by experimental validation. (C) 2017 Elsevier B.V. All rights reserved.