MultiStructRNA enables seamless switching between prediction methods without requiring workflow changes and supports both in-notebook and exportable visualizations, and supports high-throughput analyses and simplifies comparison across methods while standardizing downstream feature extraction.
Abstract
We introduce MultiStructRNA, a unified Python toolkit for RNA secondary structure prediction, ensemble analysis, and visualization. Although RNA secondary structure is central to RNA biology and therapeutic design, practical adoption is often hindered by fragmented tooling, incompatible input and output formats, and limited visualization support. MultiStructRNA addresses these challenges through a single high-level API that orchestrates multiple prediction algorithms, harmonizes results into a consistent schema, and provides reproducible, ensemble-aware metrics through an object model suited to both interactive notebooks and production pipelines. MultiStructRNA enables seamless switching between prediction methods without requiring workflow changes and supports both in-notebook and exportable visualizations. Designed for scalability, it supports high-throughput analyses and simplifies comparison across methods while standardizing downstream feature extraction. The current release also includes optional agent-readable workflow recipes that document dependency setup, backend-adapter conventions, SHAPE-data reconciliation, structure interpretation, and comparative sequence analyses. By integrating diverse RNA secondary structure packages within a common framework, MultiStructRNA streamlines structure analysis and facilitates its use in RNA design, optimization, and machine learning workflows.
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.
Federico Di Petta, Piermichele Rosati, Piero Hierro Canchari et al.· bioRxiv· 0 citations
RNA sequencing (RNA-seq) is widely used to investigate transcriptional programs in plant biology, yet the need to combine multiple specialized tools and bioinformatics expertise to convert raw sequencing reads into biologically interpretable results remains a major technical barrier for many plant biologists. Here, we present VizR (VIsualiZation of Rna seq), a web- based platform that integrates end-to-end RNA-seq analysis and visualization within a single integrated environment. VizR automates upstream processing, including quality control, adapter trimming, genome alignment, and transcript quantification, and connects the resulting expression data to downstream exploratory analyses. Its interface is designed to make expression patterns immediately searchable and interpretable: users can query genes through an equalizer-style expression-pattern interface, inspect expression profiles using inline heatmaps embedded in gene tables, and perform context-integrated gene ontology analysis throughout the workflow. VizR also supports comparative analysis through interactive Venn diagram module, allowing users to transfer gene sets directly from result tables. As a Docker- based application, VizR can be deployed locally and accessed through a standard web browser. By unifying automated RNA-seq processing, interactive visualization, and functional interpretation, VizR lowers the technical barrier to transcriptome analysis and provides a practical platform for plant biology research.
Post-translational modifications (PTMs) and genetic variants regulate protein function, signalling, and disease, but their interpretation requires integration of sequence annotations with structural, interaction, and biophysical context. Although resources such as Scop3P, UniProt, the Protein Data Bank, and AlphaFold provide extensive annotations and structural information, integrating these data into reproducible structure-aware analyses still requires custom scripting and manual coordination between multiple independent tools. To address this challenge, we developed Scop3P-Toolkit, an open-source executable analytical environment for interactive analysis of PTMs, mutations, and proteomics-derived peptides in their structural context. The toolkit integrates protein annotation retrieval with structural mapping, residue interaction network analysis, comparative structural analysis, and residue-level biophysical profiling within a unified framework. Experimentally supported phosphosites, phosphopeptides, and phosphoproteomics evidence are provided for human proteins through Scop3P, with optional integration of curated UniProt PTM annotations. UniProt-derived PTMs, sequence features, and genetic variants are available for proteins from any species, extending the framework beyond the human phosphoproteome. Scop3P-Toolkit supports structure-centric analyses including interpretation of PTMs and disease-associated variants, analysis of residue interaction networks and their rewiring across alternative conformations, structural localisation of peptides, and exploration of protein–protein, protein–ligand, and host–pathogen interfaces. Interactive visualisation links sequence annotations, three-dimensional structures, residue interaction networks, and biophysical profiles, enabling coordinated exploration across multiple molecular representations. The toolkit is distributed as Jupyter notebooks, browser-based Voilà applications, and a Galaxy interactive tool, providing transparent, accessible, and reproducible workflows for both computational and experimental researchers. By integrating biological annotation resources into executable, structure-aware workflows, Scop3P-Toolkit enables reproducible interpretation of PTMs, mutations, and proteomics data.
Adrián Díaz, Natalia Tichshenko, Boris Depoortere et al.· bioRxiv· 0 citations
RNA-Seq, analyses of RNA abundance by next-generation sequencing, has become a near-universal tool in modern biology. Availability of streamlined protocols and kits, straightforward ability to multiplex hundreds of samples, low cost of short-read sequencing, and well-established analytical pipelines make RNA-Seq a method of choice when even a few genes need to be analyzed in parallel. While many tools have been developed for quality control, mapping, and visualization of RNA-Seq data, managing all these individually still requires substantial familiarity with shell scripting and R, and remains a bottleneck for laboratories with limited computational background. We assembled FetchR, an intuitive pipeline with built-in, clear explanations of features and outputs, for local analyses of RNA-Seq data from either own .fastq files or data imported from Sequence Read Archive via the ENA Portal API. The pipeline operates in Windows Subsystem for Linux (WSL) and is installed via a single script that handles all individual tools, as well as their dependencies and updates, including the reference genome annotation(s), and system requirements. The outputs include standard quality control checks, data visualization, read summation, differential gene expression analyses, visualization, and exploratory analyses using Gene Ontology and Gene Set Enrichment Analyses, as well as detailed logs of every step for subsequent reproducible reporting.
Dustin R. Fetch, Alexey A. Soshnev· bioRxiv· 0 citations
The VizFold plugin is described, a modular framework that can be extended toward end-to-end composable pipelines and demonstrated feasibility through standardized hook-based tracing for ESMFold and Boltz-2, archive validation, and reproducible deployment on an HPC cluster using managed caches, modules, quotas, and Slurm workflows.
Jayanth Vennamreddy, Arish Virani, Kevin Yin et al.· Practice and Experience in A...· 0 citations
Clustering and classification of RNA secondary structures are central to understanding RNA function. However, widely used alignment methods, such as LocARNA and bpRNA-align, are not explicitly designed to exploit the hierarchical relationships among RNA structural elements, limiting their applicability to complex, multi-branched structures. In this study, we introduce Tree_RNA-Align, a novel method for RNA secondary structure clustering and classification based on a tree-structure alignment algorithm. The method transforms dot–bracket structures into tree representations, in which multibranch loops and stems serve as nodes, thereby preserving the hierarchical relationships among structural elements. It integrates a bottom–up hierarchical comparison for clustering with a top–down comparison for classification and prediction. Notably, classification experiments on five RNA families (16S rRNA, group_I_intron, RNase P, SRP, and tmRNA) achieved a micro-averaged F1-score of 0.933 (range across families: 0.760–0.981) and effectively identified representative structures within each family. Overall, these results suggest that incorporating classification can further improve RNA secondary structure prediction, demonstrating the utility of Tree_RNA-Align for structural analysis and functional annotation.
Zhi-Jie He, Chengzhen Xu, Xiaomin Wu· International Journal of Mol...· 0 citations
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