# RNAMotifProfile: a graph-based approach to build RNA structural motif profiles

## TL;DR

RNAMotifProfile is a new method that builds detailed profiles of RNA structural motifs to better understand their structure and function.

## Contribution

RNAMotifProfile introduces a novel profile-to-profile alignment algorithm that captures motif variations and base interactions comprehensively.

## Key findings

- RNAMotifProfile generates accurate profiles by aligning motif instances using a guide-tree-based approach.
- The method captures both similarities and variations in base interactions within RNA structural motifs.
- RNAMotifProfile can be used as a motif search tool for RNA engineering and therapeutic design.

## Abstract

RNA structural motifs are the recurrent segments in RNA three-dimensional structures that play a crucial role in the functional diversity of RNAs. Understanding the similarities and variations within these recurrent motif groups is essential for gaining insights into RNA structure and function. While recurrent structural motifs are generally assumed to be composed of the same isosteric base interactions, this consistent pattern is not observed across all examples of these motifs. Existing methods for analyzing and comparing RNA structural motifs may overlook variations in base interactions and associated nucleotides. RNAMotifProfile is a novel profile-to-profile alignment algorithm that generates a comprehensive profile from a group of structural motifs, incorporating all base interactions and associated nucleotides at each position. By structurally aligning input motif instances using a guide-tree-based approach, RNAMotifProfile captures the similarities and variations within recurrent motif groups. Additionally, RNAMotifProfile can function as a motif search tool, enabling the identification of instances of a specific motif family by searching with the corresponding profile. The ability to generate accurate and comprehensive profiles for RNA structural motif families, and to search for these motifs, facilitates a deeper understanding of RNA structure–function relationships and potential applications in RNA engineering and therapeutic design.