Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small size of miRNAs, the small number of unique miRNAs, the fact that similar miRNAs can be transcribed from multiple loci, and the presence of miRNA isoforms known as isomiRs. Methods failing to address these issues can return misleading information. We propose a novel quantification method designed to address these concerns.
Researchers from the Colorado School of Public Health present miR-MaGiC, a novel miRNA quantification method, implemented as a cross-platform tool in Java. miR-MaGiC performs stringent mapping to a core region of each miRNA and defines a meaningful set of target miRNA sequences by collapsing the miRNA space to “functional groups”. They hypothesize that these two features, mapping stringency and collapsing, provide more optimal quantification to a more meaningful unit (i.e., miRNA family). The researchers test miR-MaGiC and several published methods on 210 small RNA-seq libraries, evaluating each method’s ability to accurately reflect global miRNA expression profiles. They define accuracy as total counts close to the total number of input reads originating from miRNAs. They find that miR-MaGiC, which incorporates both stringency and collapsing, provides the most accurate counts.
Total counts returned by each method
(Top: all samples; bottom: zoom in on the area of highest density). Each dot represents one library being quantified by one method. Results for miR-MaGiC with collapsing by MIMAT number are not pictured as they are extremely similar to collapsing by miRBase name. A dot’s position along the horizontal axis indicates the number of raw reads for the library. Its position along the vertical axis indicates the total count returned for the library by the method indicated by dot color. The solid lines indicate a theoretical ratio of total count to input raw reads. For example, a dot lying on the 0.8 line would mean the total counts for that library and quantification method was 0.8 times the number of raw reads. For dots lying above the 1.0 line, the total counts for that library and method added up to more than the number of raw reads. See Additional file 1: Table S1 for detailed explanation of method abbreviations
Availability – miR-MaGiC software is distributed under the MIT license at https://github.com/KechrisLab/miR-MaGiC. The software is written in Java and is platform-independent, requiring only Java 8 or higher and Snakemake.