Abstract
microRNAs (miRNAs) are small regulatory RNAs that repress target mRNA transcripts through base pairing. Although the mechanisms of miRNA production and function are clearly established, new insights into miRNA regulation or miRNA-mediated gene silencing are still emerging. In order to facilitate the discovery of miRNA regulators or effectors, we have developed sRNA-Effector, a machine learning algorithm trained on enhanced crosslinking and immunoprecipitation sequencing and RNA sequencing data following knockdown of specific genes. sRNA-Effector can accurately identify known miRNA biogenesis and effector proteins and identifies 9 putative regulators of miRNA function, including serine/threonine kinase STK33, splicing factor SFPQ, and proto-oncogene BMI1. We validated the role of STK33, SFPQ, and BMI1 in miRNA regulation, showing that sRNA-Effector is useful for identifying new players in small RNA biology. sRNA-Effector will be a web tool available for all researchers to identify potential miRNA regulators in any cell line of interest.