Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method

MicroRNAs are vital gene expression regulators, extensively studied worldwide. The large-scale characterization of miRNAomes is possible using next-generation sequencing (NGS). This technology offers great opportunities, but these cannot be fully exploited without proper and comprehensive bioinformatics analysis. This may be achieved by the use of reliable dedicated software; however, different programs may generate divergent results, leading to additional discrepancies. Thus, the aim of this study was to compare three bioinformatic algorithms dedicated to NGS-based microRNA profiling and validate them using an alternative method, namely RT-qPCR. The comparison analysis revealed differences in the number and sets of identified miRNAs. The qPCR confirmed the expression of the investigated microRNAs. The correlation analysis of NGS and qPCR measurements showed strong and significant coefficients for a subset of the tested miRNAs, including those detected by all three algorithms. Single miRNA variants (isomiRs) showed different levels of correlation with the qPCR data. The obtained results revealed the good performance of all tested programs, despite the observed differences. Moreover, they implied that some specific miRNAs may be differentially estimated using NGS technology and the qPCR method, regardless of the used bioinformatics software. These discrepancies may stem from many factors, including the composition of the isomiR profile, their abundance, length, and investigated species. In conclusion, in this study, we shed light on the bioinformatics aspects of miRNAome profiling, elucidating its complexity and pinpointing potential features influencing validation. Thus, qPCR validation results should be open to interpretation when not fully concordant with NGS results until further, additional analyses are conducted.