Computational Tools for Genome-Wide miRNA Prediction and Study

Abstract

MicroRNAs (miRNAs) are single-stranded non-coding RNA susually of 22 nucleotidesin length that play an important post-transcriptional regulation role in many organisms. MicroRNAs bind a seed sequence to the 3´-untranslated region (UTR) region of the target messenger RNA (mRNA), inducing degradation or inhibition of translation and resulting in a reduction in the protein level. This regulatory mechanism is central to many biological processes and perturbation could lead to diseases such as cancer. Given the biological importance, of miRNAs, there is a great need to identify and study their targets and functions. However, miRNAs are very difficult to clone in the lab and this has hindered the identification of novel miRNAs. Next-generation sequencing coupled with new computational tools has recently evolved to help researchers efficiently identify large numbers of novel miRNAs. In this review, we describe recent miRNA prediction tools and discuss their priorities, advantages and disadvantages.

In silico identification of conserved miRNAs and their selective target gene prediction in indicine (Bos indicus) cattle

“…Overall, 736 conserved miRNAs were retrieved from the taurine genome and 12 miRNAs showed copy number variations. Out of 154 non-perfect similar precursors, 18 showed mutations in their mature sequences whereas 8 of them showed a mutation in their seed region, which were further subjected to target gene prediction [ 9 , 61 ]. The distribution of miRNAs in indicine is shown in Table 1 .…”

“…(2017) reported in silico miRNA identification approach as an accurate, precise, less expensive, fast and reliable method [ 37 , 53 ]. The principle employed for the computational analysis include correct hairpins secondary structure, high degree evolutionary conservation between the closely related species and value of minimum folding energy [ 2 , 5 , 7 , 9 , 10 ]. We believe that the methodology opted here is sensitive enough to predict the miRNA homologs as performed by [ 5 ].…”

“…Many cellular and physiological processes are regulated by them. Predominantly, miRNA in animals can bind to 3’ untranslated regions (3’ UTR) [ 9 – 11 ], coding regions and to the 5’ end untranslated region (5’ UTR) [ 12 , 13 ] with non-perfect complementarity. Whereas, in plants, the more dominant behavior is cleavage of the target mRNA by perfect complementarity within the target mRNA [ 14 , 15 ].…”

“…The seed region around 2 to 9 nucleotides from the 5’-end of miRNA is the key binding region for miRNA target recognition [ 20 , 21 ]. The stability of precursor miRNA, as well as target specificity of mature miRNA, rely on mutations in the mature sequences, especially in the seed region [ 9 ].…”