SMARTIV

SMARTIV predicts combined RNA sequence and structure motifs from high-throughput in-vivo RNA-binding data to characterize sequence and secondary-structure binding preferences of RNA-binding proteins (RBPs).

Key Features:

• Combined sequence-structure motif discovery: Predicts motifs by analyzing enriched k-mers that integrate ranked RNA sequences and their predicted secondary structures.
• Secondary structure prediction: Leverages various RNA folding methods to obtain predicted RNA secondary structures for motif analysis.
• Combined alphabet PWMs: Generates Position Weight Matrices (PWMs) within a combined sequence and structure alphabet.
• Statistical assessment: Assigns P-values to PWMs to provide statistical significance for identified motifs.
• Integrated visualization: Represents sequence and structure motif content as a single logo combining both types of information.
• Validated on diverse datasets: Demonstrated consistent performance across in-vivo high-throughput binding experiments involving RBPs from multiple families and sequencing technologies.

Scientific Applications:

• RBP binding specificity characterization: Identify sequence and secondary-structure binding preferences of RNA-binding proteins (RBPs).
• Motif discovery from in-vivo data: Discover combined sequence-structure motifs from ranked in-vivo high-throughput binding datasets.
• Interpretation of RNA–protein interactions in gene regulation: Elucidate interactions between RNA sequence, RNA secondary structure, and RBPs relevant to gene expression regulation studies.

Methodology:

Analyzes enriched k-mers from ranked RNA sequences and their predicted secondary structures, uses various RNA folding methods to predict secondary structure, generates PWMs in a combined sequence–structure alphabet, and assigns P-values to PWMs.