Biology subjects

Tesei, L.

Publications and source records attributed to Tesei, L..

3 recordsLinked to original sources

bioRxiv2026

EXTRARNAS: A Framework for Extracting RNA Structures with Multiple Tools

Accurate annotation of RNA base-pairing interactions is essential for structural analysis, benchmarking, and data-driven RNA structure prediction. Several tools can extract RNA interactions from three-dimensional coordinates, but their outputs are heterogeneous and may disagree, particularly for non-canonical base pairs. We present EXTRARNAS, a Java-based framework for automated, reproducible, and user-friendly large-scale extraction of RNA structural annotations with multiple tools. EXTRARNAS processes batches of RNA structures specified by PDB identifier and chain, or provided as local PDB files, executes annotation tools through a Docker-based environment, and parses tool-specific outputs using ANTLR4-based grammars. For each structure-tool pair, the framework generates standard BPSEQ files for canonical cis Watson-Crick interactions and introduces BPSEQE, a standardized text format for representing the extended secondary structure, preserving canonical, non-canonical, and multiple interactions per nucleotide. The current prototype supports RNAView, MC-Annotate, and RNAPolis Annotator. We demonstrate EXTRARNAS on eight RNA structures containing triple-helix motifs, comparing extracted canonical pairs against curated BPSEQ references and evaluating the recovery of manually validated Hoogsteen interactions. The results show consistent differences among tools, especially for non-canonical interactions, highlighting the need for standardized representations such as BPSEQE to support reproducible comparison and future consensus-based annotation.

bioinformatics↗

bioRxiv2026

PhyloRNA: a database of RNA secondary structures with associated phylogenies

The ability to access, search, and analyse large collections of RNA molecules together with their secondary structure and evolutionary context is essential for comparative and phylogeny-driven studies. Although RNA secondary structure is known to be more conserved than primary sequence, no existing resource systematically associates individual RNA molecules with curated phylogenetic classifications. Here, we introduce PhyloRNA, a curated meta-database that provides large-scale access to RNA secondary structures collected from public resources or derived from experimentally resolved 3D structures. PhyloRNA allows users to search, select, and download extensive sets of RNA molecules in multiple textual formats, each entry being explicitly linked to phylogenetic annotations derived from five curated taxonomy systems. In addition to taxonomic information, each RNA molecule is accompanied by a rich set of descriptors, including pseudoknot order, genus, and three levels of structural abstraction--Core, Core Plus, and Shape--which facilitate comparative analyses across sets of molecules. PhyloRNA is publicly available at https://bdslab.unicam.it/phylorna/ and is regularly updated to incorporate newly available data and revised taxonomic annotations.

bioinformatics↗

bioRxiv2025

Decoding RNA Triple Helices: Identification from Sequence and Secondary Structure

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.

bioinformatics↗