## 2 Materials and Methods
### In Silico Analysis  
The exon three sequence of _VSX1_ was analyzed for the presence of potential cis-acting splicing-regulatory elements (SREs) using Human Splicing Finder (HSF), Exonic splice enhancer (ESE) element finder and through Exon-Identity Element (EIEs) prediction software. All three software have been widely used and cited to predict SREs. HSF predicts the effect of a mutation in an exonic region on splicing by combining twelve different algorithms, which include position weight matrices, maximum entropy and motif comparison algorithms [21]. The ESE finder identifies and predicts the ESE elements by scanning the subject's exonic sequence for the presence of binding motifs of four SR proteins; the binding motifs are searched on the basis of position-specific scoring matrices based on frequency weighting values derived from the alignment of winner sequences [22]. [PERSON] et al. [23] proposed a model for the identification of cis-acting SREs which scans the subject's sequence for exon-identity elements (EIEs) and intron-identity elements (IIEs). The program identifies EIEs and IIEs based on asymmetry in the nucleotide frequency and distribution compared to the rest of genomic DNA. The asymmetry exhibited by the SREs would have developed due to evolutionary selection pressure over the SREs, but the remainder of the nucleic acid shows a symmetric frequency of distribution of bases. Another program used was RESCUE-ESE, which uses a position weight matrix-based strategy validated experimentally and known as ESE sequences [24].  
The 3'UTR sequence of _VSX1_ was checked using PITA (Probability of Interaction by Target Accessibility) software. PITA is a widely used algorithm for screening miRNA binding regions in the 3'UTR [25]. PITA has devised a model which successfully predicts miRNA binding sequence motifs based on the difference between free energies of unbound duplexed target mRNA with a hidden miRNA site and those of an open mRNA with a binding site accessible to the subject miRNA. The difference between two free energies is termed \(\Delta\Delta\)G; the lower the \(\Delta\Delta\)G value, the better will be the binding of miRNA with the target.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Exons** & **Primer Sequence** & **Annealing** (°C)** & **Product Size** & **MgCl2** (**mM)** & **Primer (\#M)** \\ \hline Exon 1\_1 & F: 5′-TTTCAGAGGACAGCCAGAC-3′ & 60 & 449 & 2 & 0.4 \\  & R: 5′-AGGTCCGATGGCGAAAG-3′ & 61 & 489 & 2.5 & 0.32 \\ Exon 1\_2 & F: 5′-TGCTTGCTAAGGCCAGGGACAGG-3′ & 58 & 327 & 2.5 & 0.4 \\ Exon 2 & F: 5′-AATCCGGCTCCATACTGTAAACC-3′ & 58 & 332 & 2.5 & 0.4 \\  & R: 5′-AACCAGAGAACCACTGGG-3′ & 58 & 332 & 2.5 & 0.4 \\ Exon 3 & F: 5′-AGCAGAGG  
The 3\({}^{\prime}\)UTR of _VSX1_ was also analyzed through the miRanda algorithm. The miRanda algorithm functions, such as the Smith-Waterman alignment algorithm, to search for potential miRNA binding sequences, but instead of aligning the matching nucleotides (A-A and U-U), it searches for the target sequence based on the complementarity of base pairs (A=U or G\(\equiv\)C). miRanda is a widely cited algorithm, it searches for complementarity matches between miRNAs and 3\({}^{\prime}\)UTR targets through alignment generated by dynamic programming [26; 27].