RNA-Lexis: a probabilistic algorithm using a non-parametric segmentation logic to detect meaningful sequences in RNA

Abstract

Abstract Deciphering sequence–function relationships in long non-coding RNAs (lncRNAs) remains challenging due to rapid evolutionary turnover and limited primary sequence conservation. Alignment-based approaches often fail to detect functional domains, and fixed-length k-mer models inadequately capture variable-length regulatory elements. Here, we introduce RNA-Lexis, a non-parametric statistical framework for unbiased discovery of candidate RNA sequence elements. RNA-Lexis applies segmentation based on local conditional probabilities to identify non-random sequence extensions, enabling detection of recurrent, variable-length motifs without prior biological assumptions. Conceptually analogous to language segmentation, the framework partitions continuous RNA sequences into statistically defined units (“xmotifs” and “cores”), providing an interpretable representation of sequence architecture. RNA-Lexis reconstructs the modular organization of well-characterized lncRNAs, including XIST and NORAD. In additional case studies, RNA-Lexis prioritized recurrent GC-rich elements in SNHG14 that were tested experimentally and shown to bind histones in RNA pulldown assays. RNA-Lexis also identified recurrent LINC01001 core motifs that overlap chromatin interaction patterns detected by GRID-seq. These analyses support the use of RNA-Lexis to nominate candidate sequence elements for functional follow-up, while biological function remains dependent on orthogonal experimental validation. RNA-Lexis provides a statistically grounded and interpretable framework for motif-level analysis of lncRNAs. Rather than directly inferring function, the method identifies recurrent sequence architecture and prioritizes candidate elements for mechanistic testing.