β-thalassemia is a significant health issue worldwide, with approximately 7% of the world's population having defective hemoglobin genes. MicroRNAs (miRNAs) are short noncoding RNAs regulating gene expression at the post-transcriptional level by targeting multiple gene transcripts. The levels of fetal hemoglobin (HbF) can be increased by regulating the expression of the γ-globin gene using the suppressive effects of miRNAs on several transcription factors such as MYB, BCL11A, GATA1, and KLF. An early step in discovering miRNA:mRNA target interactions is the computational prediction of miRNA targets that can be later validated with wet-lab investigations. This review highlights some commonly employed computational tools such as miRBase, Target scan, DIANA-microT-CDS, miRwalk, miRDB, and micro-TarBase that can be used to predict miRNA targets. Upon comparing the miRNA target prediction tools, 4 main aspects of the miRNA:mRNA target interaction are shown to include a few common features on which most target prediction is based: conservation sites, seed match, free energy, and site accessibility. Understanding these prediction tools' usage will help users select the appropriate tool and interpret the results accurately. This review will, therefore, be helpful to peers to quickly choose a list of the best miRNAs associated with HbF induction. Researchers will obtain significant results using these bioinformatics tools to establish a new important concept in managing β-thalassemia and delivering therapeutic strategies for improving their quality of life.
Keywords: Bioinformatics; beta-thalassemia; gamma-globin; hemoglobinopathy; microRNAs.
© The Author(s) 2022.