Analysis of coding gene expression from small RNA sequencing

The popularity of microRNA expression analyses is reflected by the existence of thousands of sRNA-seq studies where matched total RNA-seq data are often unavailable. The lack of paired sequencing experiments limits the analysis of microRNA-gene regulatory networks. We explore whether protein-coding gene expression can be quantified directly from transcript fragments present in sRNA-seq experiments. We analyze studies containing matched total RNA and small RNA from four human tissues and recover transcript fragments from the sRNA-seq datasets. We find that the expression levels of protein-coding gene transcripts derived from sRNA-seq datasets are comparable to those from total RNA-seq experiments (R ² ranging from 0.33 to 0.76). Analyses across multiple tissues and species show similar correlations, indicating that the approach is applicable across organisms. We confirm that transcript half-life and the expression of housekeeping or highly abundant genes do not bias the results. Analysis of the expression of both microRNAs and coding genes from the same sRNA-seq experiments demonstrate that known microRNA-target interactions are, as expected, inversely correlated with the expression profiles of these microRNA-mRNA pairs. For a dual mRNA/miRNA profile, we recommend sequencing the ≥25 nucleotide fraction at ≥ 5 M reads. To confirm the utility of this approach, we apply our method to breast cancer sRNA-seq datasets lacking total RNA-seq data and achieve 75% recall and 64% accuracy comparing inferred coding gene expression with qPCR-validated targets. Our findings demonstrate that quantifying mRNA fragments from sRNA-seq experiments provides a reliable approach to investigate microRNA–mRNA interactions when total RNA-seq is unavailable.