Bioinformatic identification of regulatory feedback motifs within RNAi pathways using multi-omics datasets

Small RNA pathways, also known as RNA interference (RNAi), are dynamic and essential regulatory systems that robustly silence a wide range of target genes in a precise, temporal, and cell-specific manner. Preventing aberrant targeting of genes by RNAi requires checks and balances to maintain homeostasis within the RNAi pathways. Yet, at present, our understanding of the mechanisms governing these complex regulatory pathways remains rudimentary; despite knowing they are crucial to maintaining cell homeostasis. Here we describe how to use our paired small RNA and mRNA sequencing approach with our bioinformatic workflow to systematically perform comparative analyses on multi-'omics datasets to identify which factors exhibit differential expression driven by changes in RNAi-targeting to generate a list of putative feedback motifs within RNAi pathways. Our workflow has the flexibility to enable high-throughput detection of putative feedback motifs for any pathway of interest in any organism.