## 4 Discussion  
We studied the efficiency of different computational algorithms used here, to evaluate the predicted target binding sites of rubber tree miRNAs for screening of false positive findings. In the RTV1 viral genome, predictions based on computational algorithms provide quick ways to identify potential host-derived miRNAs. At the individual, intersection and union levels, we developed the most effective approach for analyzing rubber tree miRNA prediction findings. Using several prediction algorithms, default algorithm characteristic parameters were efficient for filtering out false-positive targets [27,28]. Default algorithms indicate optimum identification for a miRNA to its appropriate viral genome target site [28]. In the current study, several potential mature rubber tree miRNAs (hbr-miR159a, hbr-miR396 (a, b), hbr-miR398, hbr-miR408b, hbr-miR482a, hbr-miR6167, hbr-miR6169, hbr-miR6171, hbr-miR6484 and hbr-miR6484) were selected to generate a RTV1-resistant variety. The study indicated that hbr-miR396a and hbr-miR398 were selectively employed by RTV1. Computational algorithms (miRanda, RNA22, RNAhybrid and Tapirhybrid) predicted the consensus hybridization site of hbr-miR396a at locus (6676), while consensus of four algorithms predicted the target binding site of hbr-miR398 at a common locus, 1840. Intriguingly, the MFEs of the consensus target pairs were calculated as very high (Figures 4 and 6; Table 1). The RNA duplex is considered to be more stable due to the stronger binding of miRNA to mRNA [29, 30, 31, 32]. The miRanda algorithm was used to test a variety of factors, ranging from target site preservation to miRNA target prediction across the entire genome. Conservation level is the key feature of the miRanda algorithm [33]. Meanwhile, the psRNATarget algorithm was utilized to predict miRNA target binding sites, using unique plant-based features [22]. The RNA22 algorithm was distinct from the other algorithms due to its unique feature which is pattern-based recognition of miRNA-mRNA interactions [19]. RNAhybrid was implemented for the calculation of minimum free energy (MFE) and used to evaluate target inhibition of rubber tree miRNAs as per the [PERSON] conclusion [20,34]. The intersection of the psRNATarget and Tapirhybrid algorithms' output delivers an extremely precise prediction of rubber tree miRNAs in this study [21,33]. In this study, we designed three algorithmic approaches at individual, union and intersection levels to evaluate false-positive prediction results. The union approach depends upon the combination of one or more prediction tools to predict the false positive results. This approach increases the sensitivity level of prediction as compared to the specificity of the results; meanwhile, the intersection approach is entirely reliant on the combination of algorithmic tools and enhances the specificity of the prediction results [35]. Our target prediction results revealed that both computational approaches achieved the best outcomes with maximum performance for predicting and estimating the best targets (Figures 3 and 6). Previous experimental studies have also demonstrated the silencing of plant viruses using host genome-encoded miRNAs [11, 36, 37, 38, 39, 40]. In the current study, predicted consensusual miRNAs were concluded after setting the standard setting parameters of the algorithms: MFE; seed pairing; target site accessibility; minimum folding energy (MFE) and pattern recognition. Thus, integrating all major aspects of miRNA target prediction was considered during prediction. Therefore, these predicted miRNAs are considered as the most effective selections for the silencing of the RTV1 genome. Rubber tree miRNA target binding-sites in the RTV1 genome and miRNA-mRNA duplex was consensually identified by all five algorithms. In this study, the RTV1 genome (ORF1 and ORF2) is vulnerable to targeting by eleven consensus rubber tree miRNAs (Figure 6).