Small non-coding RNAs, in particular microRNAs, are critical for normal physiology and are candidate biomarkers, regulators, and therapeutic targets for a wide variety of diseases. There is an ever-growing interest in the comprehensive and accurate annotation of microRNAs across diverse cell types, conditions, species, and disease states. Highthroughput sequencing technology has emerged as the method of choice for profiling microRNAs. Specialized bioinformatic strategies are required to mine as much meaningful information as possible from the sequencing data to provide a comprehensive view of the microRNA landscape.
Researchers from UNC Chapel Hill have developed miRquant 2.0, an expanded bioinformatics tool for accurate annotation and quantification of microRNAs and their isoforms (termed isomiRs) from small RNA-sequencing data. The researchers anticipate that miRquant 2.0 will be useful for researchers interested not only in quantifying known microRNAs but also mining the rich well of additional information embedded in small RNA-sequencing data.
Major steps of the miRquant 2.0 workflow are shown. Cutadapt is used for trimming partial or full adapter sequences from the 3’-end of small RNA sequencing reads. Those reads that do not have a specified length of adapter sequence at the 3’- end, or that entirely consist of adapter sequences, are discarded. Bowtie and SHRiMP are used for the two-tier mapping process. Unaligned reads after the SHRiMP step are discarded. Annotation is provided for miRNAs, tRNA-derived RNAs (tDRs), and Y-RNA derived RNAs (yDRs). All other mapped loci are grouped into the unannotated category. Normalized expression levels are provided as reads per million mapped (RPMM), and as reads per million mapped to miRNAs (RPMMM) for miRNAs.
Availability – miRquant is avialble at: https://github.com/Sethupathy-Lab/miRquant