MultiStructRNA: a Python package for multi-algorithm RNA secondary structure prediction, ensemble analysis, and visualization

[2608.14846] MultiStructRNA: a Python package for multi-algorithm RNA secondary structure prediction, ensemble analysis, and visualization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Quantitative Methods arXiv:2608.14846 (q-bio) [Submitted on 14 Aug 2026] Title: MultiStructRNA: a Python package for multi-algorithm RNA secondary structure prediction, ensemble analysis, and visualization Authors: Yashrajsinh Jadeja , Haining Lin , Mihir Metkar View a PDF of the paper titled MultiStructRNA: a Python package for multi-algorithm RNA secondary structure prediction, ensemble analysis, and visualization, by Yashrajsinh Jadeja and 2 other authors View PDF 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. Subjects: Quantitative Methods (q-bio.QM) ; Biomolecules (q-bio.BM) Cite as: arXiv:2608.14846 [q-bio.QM] (or arXiv:2608.14846v1 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2608.14846 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yashrajsinh Jadeja [ view email ] [v1] Fri, 14 Aug 2026 19:38:08 UTC (615 KB) Full-text links: Access Paper: View a PDF of the paper titled MultiStructRNA: a Python package for multi-algorithm RNA secondary structure prediction, ensemble analysis, and visualization, by Yashrajsinh Jadeja and 2 other authors View PDF view license Current browse context: q-bio.QM < prev | next > new | recent | 2026-08 Change to browse by: q-bio q-bio.BM References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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