bioRxivBioinformaticsPreprint
Mountain Centroid: RNA Ensemble Representation with Mountain Profiles
Abstract
Background: RNA molecules form thermodynamic ensembles, but interpretation often requires a single representative structure. Existing base-pair centroid estimators assess agreement at the level of individual base pairs and do not directly target nesting depth along the sequence. Methods: We introduce Mountain Centroid, which minimizes expected squared mountain-profile distance, and derive dynamic programming algorithms with and without RNA pairing constraints. We also combine the Mountain Centroid objective with the base-pair centroid gain. Results: Across 21,254 RNAStrAlign sequences, Mountain Centroid had lower median normalized mean squared mountain distance (NMSMD) than minimum-free-energy (MFE) and base-pair centroid ({gamma} = 1) structures, whereas its median base-pair F1 was lower. Imposing RNA pairing constraints improved base-pair F1 for 59.35% of sequences and reduced it for 3.58%. At an illustrative weight, the combined objective had median base-pair F1 similar to MFE while retaining lower median NMSMD than MFE and all tested {gamma}-centroid settings. Conclusions: Mountain Centroid represents an RNA structural ensemble with a single secondary structure that reflects how nesting depth varies across nucleotide positions. Combining mountain-profile and individual-base-pair criteria allows their relative contributions to be varied.
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- PARSEbp: Pairwise Agreement-based RNA Scoring with Emphasis on Base Pairings
Sumit Tarafder, Debswapna Bhattacharya·Bioinformatics AdvancesPeer-reviewed·Jan 1, 2026
- CoSTAR: Coarse Stem-Topology Alignment of Pseudoknotted RNA Structures by Relation-Constrained Search
Finn Archinuk, Hosna Jabbari·bioRxiv·May 26, 2026
- SHAPE directed RNA folding
Dominik Luntzer, Ronny Lorenz, … Michael T. Wolfinger·BioinformaticsPeer-reviewed·Sep 9, 2015
- Ab initio prediction of RNA structure ensembles with RNAnneal
Lukas Herron, Yunrui Qiu, … Pratyush Tiwary·bioRxiv·Feb 1, 2026
- LinearKnot: linear-time ensemble analysis of RNA density-2 pseudoknots under hierarchical folding
Takumi Otagaki, Kengo Sato·bioRxiv·Nov 9, 2026
- RNA tertiary structure modeling with BRiQ potential in CASP15
Ke Chen, Yaoqi Zhou, … Peng Xiong·Proteins Structure Function and BioinformaticsPeer-reviewed·Aug 28, 2023