What question did this study set out to answer?

To identify RNA triple helices using a novel secondary-structure framework and the TripleMatcher tool.

January 23, 2026

Decoding RNA Triple Helices: Identification from Sequence and Secondary Structure

Key Points

To identify RNA triple helices using a novel secondary-structure framework and the TripleMatcher tool.
Extended dot-bracket formalism with a third annotation for Hoogsteen contacts.
Developed TripleMatcher to search for triple-helix patterns and apply distance filtering.
Tested against known triple helices in telomerase RNAs for benchmark accuracy.
Scanned a large dataset of RNAs to identify feasible triple-helix regions.
Achieved structure-wise detection of 8 triple helices from annotated regions.
Increased precision from 0.42 to 0.81 and overall accuracy from 0.42 to 0.62 through geometric filtering.
Identified 90 geometrically feasible triple-helix regions from 150,948 candidates in a large RNA screen.

Abstract

The discovery of long non-coding RNAs has revealed additional layers of gene-expression control. Specific interactions of lncRNAs with DNA, RNAs, and RNA-binding proteins enable regulation in both cytoplasmic and nuclear compartments; for example, a conserved triple-helix motif is essential for MALAT1 stability and oncogenic activity. Here we present a secondary-structure–based framework to annotate and detect RNA triple helices. First, we extend the dot–bracket formalism with a third annotation line that encodes Hoogsteen contacts. Second, we introduce TripleMatcher, which searches for a triple-helix pattern, filters candidates by C1′ –C1′ distance thresholds, and merges overlaps into region-level zones. Using telomerase RNAs and RNA-stability elements with experimentally established triple helices (8 RNAs), TripleMatcher localized all annotated regions (structure-wise detection 8/8); geometric filtering removed most spurious candidates and improved precision (PPV from 0.42 to 0.81) and overall accuracy (F1 from 0.42 to 0.62) while maintaining sensitivity. Benchmarking eight predictors showed that pseudoknot-aware methods most reliably reproduce the local architecture required for detection, aligning secondary-structure quality with downstream triple-helix recovery. Applied prospectively, the framework identified candidate regions directly from predicted secondary structures and scaled to a screen of 4,147 RNAs, where distance filtering reduced 150,948 raw candidates to 90 geometrically feasible regions across seven molecules, including human telomerase complexes. Together, the notation and TripleMatcher provide a concise route from secondary structure to a small, interpretable set of triple-helix candidates suitable for targeted experimental validation.

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