Abstract
RNA-binding proteins (RBPs) regulate their RNA targets by binding to short sequence motifs, but the underlying mechanisms enabling sequence-specific recognition within the vast transcriptome remain unclear for the majority of human RBPs. Sequence contexts are believed to be a significant contributing factor to RBP binding specificity but are often overlooked. Further, existing motif discovery algorithms do not consider the structure and composition of the motif's flanking regions in their construction, which represents a consequential shortcoming. Herein, we present a novel linguistics-inspired RBP motif and context discovery algorithm that is consensus-based, deterministic, and flexible. Our algorithm draws multiple parallels between natural language and genomic language and relies on three important k-mer properties that impart lexical, syntactic, and semantic structures and rules to the process of motif and context discovery. Critically, our algorithm integrates information from sequence contexts when constructing RBP motifs. We demonstrate that our algorithm achieves strong discovery accuracy against a ground-truth set, and even outperforms existing methods in primary motif ranking.