PRIESSTESS

PRIESSTESS identifies and models RNA sequence and secondary-structure motifs to interpret RNA-binding protein (RBP) binding preferences.

Key Features:

• Universal motif-finding and scanning strategy: Identifies enriched RNA primary sequence and secondary-structure motifs across diverse RBP datasets using a universal motif-finding and scanning approach.
• Motif reduction via logistic regression with LASSO regularization: Distills identified motifs into core motifs by applying logistic regression combined with LASSO regularization.
• Interpretable visualizations of core motifs: Produces visual representations of core sequence-structure motifs to support interpretation of RBP secondary-structure specificity.
• Large-scale evaluation of secondary-structure preferences: Enables quantitative evaluation across many datasets, demonstrated on 144 RBPs across 202 RNA binding datasets reporting that ~75% of RBPs exhibit secondary-structure preference and ~10% show specificity beyond unpaired bases.

Scientific Applications:

• HTR-SELEX data analysis: Captures primary sequence and secondary-structure preferences from HTR-SELEX (High Throughput RNA Selection and Sequencing) datasets, demonstrated on 23 RBPs with diverse binding modes.
• Characterization of RBP binding modes and specificity: Distinguishes sequence-driven versus structure-driven recognition and assesses whether RBPs recognize the accessibility of primary sequences.
• Interpretation of RBP–RNA interaction mechanisms: Provides core motif models to interpret molecular mechanisms underlying RBP–RNA interactions.

Methodology:

Applies a universal motif-finding and scanning strategy, reduces motifs to core motifs using logistic regression with LASSO regularization, and generates visualizations of core sequence-structure motifs; methods were applied to HTR-SELEX data and large RBP datasets (144 RBPs, 202 datasets).