pith. sign in

arxiv: 2512.20581 · v3 · pith:7KJ53ZLZnew · submitted 2025-12-23 · 🧬 q-bio.BM · physics.bio-ph

MERGE-RNA: a physics-based model to predict RNA secondary structure ensembles with chemical probing

Giuseppe Sacco , Jianhui Li , Redmond P. Smyth , Guido Sanguinetti , Giovanni Bussi This is my paper

Pith reviewed 2026-05-21 15:34 UTC · model grok-4.3

classification 🧬 q-bio.BM physics.bio-ph
keywords RNA secondary structurechemical probingDMS reactivitystructural ensemblesmaximum entropyriboswitchensemble predictionthermodynamic populations
0
0 comments X

The pith

MERGE-RNA predicts RNA structural ensembles by modeling the physics of the DMS probing pipeline and adjusting thermodynamic populations with maximum entropy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces MERGE-RNA to interpret chemical probing signals such as DMS reactivity, which arise from entire structural ensembles rather than single fixed structures. It builds a model of the physical steps in the probing experiment and learns a small set of transferable parameters that can be optimized jointly from data on many RNAs, concentrations, and replicates. These parameters enter a maximum-entropy adjustment that shifts ensemble populations by the smallest amount needed to match the measurements. The resulting ensembles show higher structural accuracy than those obtained from standard pseudo-free-energy corrections and reproduce the observed reactivities more faithfully. When applied to the V. vulnificus adenine riboswitch, the model recovers the conformations seen by NMR and the population shifts that occur upon ligand binding, with the shifts consistent with the NMR-derived dissociation constant.

Core claim

MERGE-RNA describes RNA as a thermodynamic ensemble whose populations are reweighted by the maximum-entropy principle after explicit modeling of the DMS probing physics, allowing data from multiple molecules and conditions to be integrated through a shared set of interpretable parameters to produce ensembles that align with experimental observations.

What carries the argument

Physics-based simulation of the chemical probing experimental pipeline combined with maximum-entropy reweighting of thermodynamic populations using a small set of transferable parameters.

If this is right

  • Structural accuracy surpasses that obtained from standard pseudo-free-energy methods.
  • Predicted ensembles reproduce measured DMS reactivity patterns more closely than previous approaches.
  • Application to the adenine riboswitch recovers the NMR-resolved conformations together with their ligand-induced population shifts.
  • The magnitude of the population shifts matches the dissociation constant obtained from NMR titration experiments.
  • In designed RNAs the model identifies transient intermediate populations during strand displacement that remain invisible to conventional single-structure analysis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same parameter-sharing strategy could be applied to data from other probing reagents to obtain tighter ensemble constraints.
  • Transferable parameters may enable useful ensemble predictions even for RNAs that have only sparse probing coverage.
  • Ensemble outputs of this form could be combined with cellular-environment data to explore how RNA function depends on conformational dynamics inside cells.

Load-bearing premise

A small set of parameters learned from the probing physics can be optimized across different molecules, probe concentrations, and replicates without introducing molecule-specific biases that distort the ensemble predictions.

What would settle it

Apply the jointly optimized parameters to an independent collection of RNAs whose conformational populations have been measured by NMR or single-molecule experiments and test whether the predicted populations and reactivities fall within experimental error of the observed values.

Figures

Figures reproduced from arXiv: 2512.20581 by Giovanni Bussi, Giuseppe Sacco, Guido Sanguinetti, Jianhui Li, Redmond P. Smyth.

Figure 1
Figure 1. Figure 1: Schematic overview of the method. Our approach builds a physical model—illustrated on the left side of the figure—that represents [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Cross-validation of physical parameters across five RNA systems. Each bar represents the per-datapoint loss obtained for the indicated system after training on three systems and testing on the remaining two. Yellow bars refer to results obtained when the corresponding system was excluded from the training set. (b) Normalized loss profiles for HCV IRES with soft constraints of varying magnitude applied.… view at source ↗
Figure 4
Figure 4. Figure 4: Ensemble predictions for cspA 5 ′ UTR at two different temperatures. (a,b) Arc plots corresponding to the DMS data collected at 10 and 37◦C, respectively. Predictions from MERGE￾RNA (red and black) are compared with predictions from Ref. [33] (blue). report in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Model accurately deconvolves mixed structural states from [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ensemble inference on experimental data for a putative [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

RNA function is tied to secondary structure, operating through dynamic and heterogeneous structural ensembles. While current analysis tools typically output single static structures or averaged contact maps, chemical probing methods like DMS capture nucleotide-resolution signals representing the full structural ensemble, which remain difficult to interpret structurally. To address this, we present MERGE-RNA, a framework that describes and outputs RNA as a structural ensemble. By modeling the physics of the experimental pipeline, MERGE-RNA learns a small set of transferable and interpretable parameters, enabling the integration of measurements across different molecules, probe concentrations, and replicates in a single optimization to improve robustness. Our model employs a maximum-entropy principle to predict thermodynamic populations, with the minimal adjustments necessary to align the ensemble with experimental data. We validate MERGE-RNA on diverse RNAs, showing that it achieves structural accuracy surpassing standard pseudo-free-energy methods and yields ensembles better recapitulating measured DMS reactivity. Applied to the V. vulnificus adenine riboswitch, MERGE-RNA recovers the NMR-resolved conformations and their ligand-induced rearrangement, with population shifts matching the NMR-derived K_d. In a designed RNA construct for which we report new DMS data, MERGE-RNA deconvolves mixed states to reveal transient intermediate populations involved in strand displacement, dynamics invisible to traditional analysis methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents MERGE-RNA, a physics-based model for predicting RNA secondary structure ensembles that incorporates chemical probing data such as DMS. It models the experimental pipeline to learn a small set of transferable parameters and uses a maximum-entropy principle to make minimal adjustments to thermodynamic populations to align with experimental measurements. The paper validates the model on diverse RNAs, claiming superior structural accuracy compared to standard pseudo-free-energy methods, better recapitulation of DMS reactivity, and successful recovery of NMR-resolved conformations and ligand-induced population shifts in the V. vulnificus adenine riboswitch, as well as deconvolution of mixed states in a designed RNA construct using new DMS data.

Significance. If the small set of parameters is truly transferable and the model provides unbiased ensemble predictions, this work could significantly improve the interpretation of chemical probing data for RNA dynamics and function. The ability to integrate data across molecules and conditions in a single optimization is a strength, and the recovery of NMR populations in the riboswitch example suggests practical utility for studying conformational changes. The reporting of new DMS data on a designed construct is a positive contribution to the field.

major comments (2)
  1. [Methods (parameter learning and max-ent procedure)] The central claim of transferable parameters learned from the physics of the DMS pipeline (Abstract) rests on a joint optimization whose derivation, regularization, and cross-validation across molecules are not visible. Without these details it is impossible to assess whether the reported gains over pseudo-free-energy methods arise from the physics-based construction or from molecule-specific absorption of systematic errors in the base thermodynamic model.
  2. [Results (V. vulnificus adenine riboswitch)] In the riboswitch application (Results), the max-ent adjustment is constructed to minimize deviation from the same DMS data used to judge success. This construction makes the reported recovery of NMR populations and K_d-matching shifts circular by design unless an explicit separation between base-model error and experimental correction is demonstrated (e.g., via held-out replicates or orthogonal observables).
minor comments (2)
  1. [Abstract] The abstract refers to validation on 'diverse RNAs' without listing the specific molecules or providing a summary table of accuracy metrics; adding this would aid reproducibility.
  2. [Figures and Methods] Figure legends and methods should explicitly state how uncertainty in the fitted parameters propagates into the reported ensemble populations and population shifts.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which have helped us improve the clarity and rigor of the manuscript. We address each major comment below and have revised the Methods and Results sections accordingly to provide the requested details on the optimization procedure and validation strategy.

read point-by-point responses

  1. Referee: [Methods (parameter learning and max-ent procedure)] The central claim of transferable parameters learned from the physics of the DMS pipeline (Abstract) rests on a joint optimization whose derivation, regularization, and cross-validation across molecules are not visible. Without these details it is impossible to assess whether the reported gains over pseudo-free-energy methods arise from the physics-based construction or from molecule-specific absorption of systematic errors in the base thermodynamic model.

    Authors: We agree that additional transparency is needed. The joint optimization is derived in the Methods section by combining the maximum-entropy objective with a forward model of the DMS experimental pipeline (including probe concentration, adduct formation, and reverse transcription). To address visibility, we have expanded the Methods with a new subsection containing the full derivation of the objective function, the L2 regularization on parameter deviations from initial values (to enforce transferability), and the explicit cross-validation protocol (leave-one-molecule-out). Results are now reported in Supplementary Figure S1 and Table S2, demonstrating that parameters remain stable across held-out molecules and that performance gains persist on unseen data. This indicates the improvements arise from the physics-based pipeline modeling rather than molecule-specific error absorption. revision: yes

  2. Referee: [Results (V. vulnificus adenine riboswitch)] In the riboswitch application (Results), the max-ent adjustment is constructed to minimize deviation from the same DMS data used to judge success. This construction makes the reported recovery of NMR populations and K_d-matching shifts circular by design unless an explicit separation between base-model error and experimental correction is demonstrated (e.g., via held-out replicates or orthogonal observables).

    Authors: We appreciate this concern about potential circularity. The max-ent step applies minimal adjustments to the base thermodynamic ensemble to match DMS reactivity, while NMR populations and ligand K_d values serve as fully independent validation targets not included in the optimization. In the revision, we have added an explicit comparison showing that the unadjusted base model fails to recover the NMR populations, while the DMS-informed max-ent ensemble succeeds with small adjustment magnitudes (quantified via the relative entropy term). We further include a replicate-split analysis (training on one DMS replicate set, evaluating on the held-out replicate) demonstrating that the recovered population shifts and K_d agreement remain consistent. These additions establish the separation between base-model correction and the orthogonal NMR observable. revision: yes

Circularity Check

1 steps flagged

Maximum-entropy adjustment to DMS data makes ensemble recapitulation circular by construction

specific steps
  1. fitted input called prediction [Abstract / Model description]
    "Our model employs a maximum-entropy principle to predict thermodynamic populations, with the minimal adjustments necessary to align the ensemble with experimental data."

    The max-ent construction is explicitly defined to minimize deviation from the experimental DMS data. Consequently the reported ensemble populations and their 'better recapitulation' of the measured reactivity are the direct result of fitting the identical data used to judge success.

full rationale

The core derivation employs max-ent to produce populations via minimal adjustments that align the ensemble with the same experimental DMS measurements later used for validation. This reduces the 'better recapitulating measured DMS reactivity' claim to a fitted output rather than an independent test. Transferability of the small parameter set and the separate NMR comparison on the riboswitch supply non-circular content, so the circularity is partial rather than total. No self-citation chains, uniqueness theorems, or ansatz smuggling appear in the provided text.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on a small number of fitted parameters and the maximum-entropy principle; no new physical entities are introduced.

free parameters (1)
  • small set of transferable parameters
    Learned during joint optimization across molecules and conditions to align predicted ensembles with DMS reactivity.
axioms (1)
  • domain assumption Maximum-entropy principle yields the thermodynamic populations with minimal adjustments needed to match data
    Invoked to define the ensemble prediction step that integrates experimental measurements.

pith-pipeline@v0.9.0 · 5778 in / 1309 out tokens · 57460 ms · 2026-05-21T15:34:23.575472+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

54 extracted references · 54 canonical work pages

  1. [1]

    Doudna and Thomas R

    Jennifer A. Doudna and Thomas R. Cech. The chemical repertoire of natural ribozymes.Nature, 418(6894):222–228, July 2002

    work page 2002

  2. [2]

    Ramakrishnan

    V. Ramakrishnan. Ribosome Structure and the Mechanism of Translation.Cell, 108(4):557–572, February 2002

    work page 2002

  3. [3]

    Morris and John S

    Kevin V. Morris and John S. Mattick. The rise of regulatory RNA.Nature Reviews Genetics, 15(6):423–437, June 2014

    work page 2014

  4. [4]

    Butcher and Anna Marie Pyle

    Samuel E. Butcher and Anna Marie Pyle. The Molecular Interactions That Stabilize RNA Tertiary Structure: RNA Motifs, Patterns, and Networks.Accounts of Chemical Research, 44(12):1302–1311, December 2011

    work page 2011

  5. [5]

    Ritwika Bose, Irfana Saleem, and Anthony M. Mustoe. Causes, functions, and therapeutic possibilities of RNA secondary structure ensembles and alternative states.Cell Chemical Biology, 31(1):17–35, January 2024

    work page 2024

  6. [6]

    Ken, Rohit Roy, Ainan Geng, Laura R

    Megan L. Ken, Rohit Roy, Ainan Geng, Laura R. Ganser, Akanksha Manghrani, Bryan R. Cullen, Ursula Schulze- Gahmen, Daniel Herschlag, and Hashim M. Al-Hashimi. RNA conformational propensities determine cellular activity. Nature, 617(7962):835–841, May 2023

    work page 2023

  7. [7]

    Adamiak, Maciej Antczak, Belisa Rebeca H

    Fan Bu, Yagoub Adam, Ryszard W. Adamiak, Maciej Antczak, Belisa Rebeca H. de Aquino, Nagendar Goud Badepally, Robert T. Batey, Eugene F. Baulin, Pawel Boinski, Michal J. Boniecki, Janusz M. Bujnicki, Kristy A. Carpenter, Jose Chacon, Shi-Jie Chen, Wah Chiu, Pablo Cordero, Naba Krishna Das, Rhiju Das, Wayne K. Dawson, Frank DiMaio, Feng Ding, Anne-Catherin...

    work page 2025

  8. [8]

    Fast algorithm for predicting the secondary structure of single-stranded RNA.Proceedings of the National Academy of Sciences, 77(11):6309–6313, November 1980

    R Nussinov and A B Jacobson. Fast algorithm for predicting the secondary structure of single-stranded RNA.Proceedings of the National Academy of Sciences, 77(11):6309–6313, November 1980

    work page 1980

  9. [9]

    J. S. McCaskill. The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers, 29(6-7):1105–1119, 1990

    work page 1990

  10. [10]

    Tinoco, P

    I. Tinoco, P. N. Borer, B. Dengler, M. D. Levin, O. C. Uhlenbeck, D. M. Crothers, and J. Bralla. Improved estimation of secondary structure in ribonucleic acids.Nature: New Biology, 246(150):40–41, November 1973

    work page 1973

  11. [11]

    How RNA folds

    Ignacio Tinoco and Carlos Bustamante. How RNA folds. Journal of Molecular Biology, 293(2):271–281, October 1999

    work page 1999

  12. [12]

    A. J. Zaug and T. R. Cech. Analysis of the structure of Tetrahymena nuclear RNAs in vivo: Telomerase RNA, the self- splicing rRNA intron, and U2 snRNA.RNA, 1(4):363–374, January 1995

    work page 1995

  13. [13]

    Miles Kubota, Catherine Tran, and Robert C. Spitale. Progress and challenges for chemical probing of RNA structure inside living cells.Nature Chemical Biology, 11(12):933–941, December 2015

    work page 2015

  14. [14]

    Deigan, Tian W

    Katherine E. Deigan, Tian W. Li, David H. Mathews, and Kevin M. Weeks. Accurate SHAPE-directed RNA structure determination.Proceedings of the National Academy of Sciences, 106(1):97–102, January 2009

    work page 2009

  15. [15]

    Hofacker, Peter F

    Stefan Washietl, Ivo L. Hofacker, Peter F. Stadler, and Manolis Kellis. RNA folding with soft constraints: Reconciliation of probing data and thermodynamic secondary structure prediction.Nucleic Acids Research, 40(10):4261–4272, May 2012

    work page 2012

  16. [16]

    Bernhart, Christian H¨ oner zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F

    Ronny Lorenz, Stephan H. Bernhart, Christian H¨ oner zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F. Stadler, and Ivo L. Hofacker. ViennaRNA Package 2.0. Algorithms for Molecular Biology, 6(1):1–14, December 2011

    work page 2011

  17. [17]

    An introduction to the maximum entropy approach and its application to inference problems in biology.Heliyon, 4(4):e00596, April 2018

    Andrea De Martino and Daniele De Martino. An introduction to the maximum entropy approach and its application to inference problems in biology.Heliyon, 4(4):e00596, April 2018

    work page 2018

  18. [18]

    Nicola Calonaci, Mattia Bernetti, Alisha Jones, Michael Sattler, and Giovanni Bussi. Molecular Dynamics Simulations with Grand-Canonical Reweighting Suggest Cooperativity MERGE-RNA 11 Effects in RNA Structure Probing Experiments.Journal of Chemical Theory and Computation, 19(12):3672–3685, June 2023

    work page 2023

  19. [19]

    Smyth, and Max von Kleist

    Wang Liu-Wei, Wiep van der Toorn, Patrick Bohn, Martin H¨ olzer, Redmond P. Smyth, and Max von Kleist. Sequencing accuracy and systematic errors of nanopore direct RNA sequencing.BMC Genomics, 25(1):1–15, December 2024

    work page 2024

  20. [20]

    Artifacts and biases of the reverse transcription reaction in RNA sequencing.RNA, 29(7):889–897, July 2023

    Jasper Verwilt, Pieter Mestdagh, and Jo Vandesompele. Artifacts and biases of the reverse transcription reaction in RNA sequencing.RNA, 29(7):889–897, July 2023

    work page 2023

  21. [21]

    Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu

    Richard H. Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu. A Limited Memory Algorithm for Bound Constrained Optimization.SIAM Journal on Scientific Computing, 16(5):1190–1208, September 1995

    work page 1995

  22. [22]

    Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J

    Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C. J. Carey, ˙Ilhan Polat, Yu Feng, Eric ...

    work page 2020

  23. [23]

    An ultraprocessive, accurate reverse transcriptase encoded by a metazoan group II intron.RNA, 24(2):183–195, February 2018

    Chen Zhao, Fei Liu, and Anna Marie Pyle. An ultraprocessive, accurate reverse transcriptase encoded by a metazoan group II intron.RNA, 24(2):183–195, February 2018

    work page 2018

  24. [24]

    Adams, Han Wan, Nicholas C

    Li-Tao Guo, Rebecca L. Adams, Han Wan, Nicholas C. Huston, Olga Potapova, Sara Olson, Christian M. Gallardo, Brenton R. Graveley, Bruce E. Torbett, and Anna Marie Pyle. Sequencing and Structure Probing of Long RNAs Using MarathonRT: A Next-Generation Reverse Transcriptase. Journal of Molecular Biology, 432(10):3338–3352, May 2020

    work page 2020

  25. [25]

    Ambi, and Redmond P

    Patrick Bohn, Anne-Sophie Gribling-Burrer, Uddhav B. Ambi, and Redmond P. Smyth. Nano-DMS-MaP allows isoform- specific RNA structure determination.Nature Methods, 20(6):849–859, June 2023

    work page 2023

  26. [26]

    Anne-Sophie Gribling-Burrer, Patrick Bohn, and Redmond P. Smyth. Isoform-specific RNA structure determination using Nano-DMS-MaP.Nature Protocols, pages 1–31, February 2024

    work page 2024

  27. [27]

    RNA Framework: An all-in-one toolkit for the analysis of RNA structures and post-transcriptional modifications.Nucleic Acids Research, 46(16):e97, September 2018

    Danny Incarnato, Edoardo Morandi, Lisa Marie Simon, and Salvatore Oliviero. RNA Framework: An all-in-one toolkit for the analysis of RNA structures and post-transcriptional modifications.Nucleic Acids Research, 46(16):e97, September 2018

    work page 2018

  28. [28]

    Salzberg

    Ben Langmead and Steven L. Salzberg. Fast gapped-read alignment with Bowtie 2.Nature Methods, 9(4):357–359, April 2012

    work page 2012

  29. [29]

    Twelve years of SAMtools and BCFtools

    Petr Danecek, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, Thomas Keane, Shane A McCarthy, Robert M Davies, and Heng Li. Twelve years of SAMtools and BCFtools. GigaScience, 10(2):giab008, February 2021

    work page 2021

  30. [30]

    Watkins, Wipapat Kladwang, Shanshan Li, Grigore Pintilie, Ved V

    Kalli Kappel, Kaiming Zhang, Zhaoming Su, Andrew M. Watkins, Wipapat Kladwang, Shanshan Li, Grigore Pintilie, Ved V. Topkar, Ramya Rangan, Ivan N. Zheludev, Joseph D. Yesselman, Wah Chiu, and Rhiju Das. Accelerated cryo- EM-guided determination of three-dimensional RNA-only structures.Nature Methods, 17(7):699–707, July 2020

    work page 2020

  31. [31]

    Barnaba: Software for analysis of nucleic acid structures and trajectories.RNA, 25(2):219–231, January 2019

    Sandro Bottaro, Giovanni Bussi, Giovanni Pinamonti, Sabine Reißer, Wouter Boomsma, and Kresten Lindorff-Larsen. Barnaba: Software for analysis of nucleic acid structures and trajectories.RNA, 25(2):219–231, January 2019

    work page 2019

  32. [32]

    Swinger, Andrey S

    Alfredo Torres-Larios, Kerren K. Swinger, Andrey S. Krasilnikov, Tao Pan, and Alfonso Mondrag´ on. Crystal structure of the RNA component of bacterial ribonuclease P. Nature, 437(7058):584–587, September 2005

    work page 2005

  33. [33]

    Burkhardt, Silvi Rouskin, Gene-Wei Li, Jonathan S

    Yan Zhang, David H. Burkhardt, Silvi Rouskin, Gene-Wei Li, Jonathan S. Weissman, and Carol A. Gross. A Stress Response that Monitors and Regulates mRNA Structure Is Central to Cold Shock Adaptation.Molecular Cell, 70(2):274–286.e7, April 2018

    work page 2018

  34. [34]

    The cspA mRNA Is a Thermosensor that Modulates Translation of the Cold-Shock Protein CspA

    Anna Giuliodori, Fabio Di Pietro, Stefano Marzi, Benoˆ ıt Masquida, Rolf Wagner, Pascale Romby, Claudio Gualerzi, and Cynthia Pon. The cspA mRNA Is a Thermosensor that Modulates Translation of the Cold-Shock Protein CspA. Molecular cell, 37:21–33, January 2010

    work page 2010

  35. [35]

    Mathews, Matthew D

    David H. Mathews, Matthew D. Disney, Jessica L. Childs, Susan J. Schroeder, Michael Zuker, and Douglas H. Turner. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure.Proceedings of the National Academy of Sciences, 101(19):7287–7292, May 2004

    work page 2004

  36. [36]

    Hoos, David H

    Mirela Andronescu, Anne Condon, Holger H. Hoos, David H. Mathews, and Kevin P. Murphy. Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics, 23(13):i19–i28, July 2007

    work page 2007

  37. [37]

    Ratajczyk, Jonathan Bath, Petr ˇSulc, Jonathan P

    Eryk J. Ratajczyk, Jonathan Bath, Petr ˇSulc, Jonathan P. K. Doye, Ard A. Louis, and Andrew J. Turberfield. Controlling DNA–RNA strand displacement kinetics with base distribution.Proceedings of the National Academy of Sciences, 122(23):e2416988122, June 2025

    work page 2025

  38. [38]

    Bayesian ensemble refinement by replica simulations and reweighting.The Journal of Chemical Physics, 143(24):243150, December 2015

    Gerhard Hummer and J¨ urgen K¨ ofinger. Bayesian ensemble refinement by replica simulations and reweighting.The Journal of Chemical Physics, 143(24):243150, December 2015

    work page 2015

  39. [39]

    Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments.Computation, 6(1), February 2018

    Andrea Cesari, Sabine Reißer, Giovanni Bussi, Andrea Cesari, Sabine Reißer, and Giovanni Bussi. Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments.Computation, 6(1), February 2018

    work page 2018

  40. [40]

    Tomezsko, Vincent D

    Phillip J. Tomezsko, Vincent D. A. Corbin, Paromita Gupta, Harish Swaminathan, Margalit Glasgow, Sitara Persad, Matthew D. Edwards, Lachlan Mcintosh, Anthony T. Papenfuss, Ann Emery, Ronald Swanstrom, Trinity Zang, Tammy C. T. Lan, Paul Bieniasz, Daniel R. Kuritzkes, Athe Tsibris, and Silvi Rouskin. Determination of RNA structural diversity and its role i...

    work page 2020

  41. [41]

    Simon, Francesca Anselmi, Martijn J

    Edoardo Morandi, Ilaria Manfredonia, Lisa M. Simon, Francesca Anselmi, Martijn J. van Hemert, Salvatore Oliviero, and Danny Incarnato. Genome-scale deconvolution of RNA structure ensembles.Nature Methods, 18(3):249–252, March 2021

    work page 2021

  42. [42]

    Olson, Anne-Marie W

    Samuel W. Olson, Anne-Marie W. Turner, J. Winston Arney, Irfana Saleem, Chase A. Weidmann, David M. Margolis, Kevin M. Weeks, and Anthony M. Mustoe. Discovery of a large-scale, cell-state-responsive allosteric switch in the 7SK RNA using DANCE-MaP.Molecular Cell, 82(9):1708– 1723.e10, May 2022. 12 Sacco et al

    work page 2022

  43. [43]

    Assmann, Philip C

    Aleksandar Spasic, Sarah M. Assmann, Philip C. Bevilacqua, and David H. Mathews. Modeling RNA secondary structure folding ensembles using SHAPE mapping data.Nucleic Acids Research, 46(1):314–323, January 2018

    work page 2018

  44. [44]

    Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.Nature Communications, 9(1):606, February 2018

    Hua Li and Sharon Aviran. Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.Nature Communications, 9(1):606, February 2018

    work page 2018

  45. [45]

    Arnold, Daniel D

    Ethan B. Arnold, Daniel D. Cohn, Emma M. Bose, Gregory Wolfe, and Alisha N. Jones. Cooperativity in RNA chemical probing experiments modulates RNA 2D structure, December 2023

    work page 2023

  46. [46]

    Probing the structure of RNAs in solution.Nucleic Acids Research, 15(22):9109–9128, November 1987

    Chantal Ehresmann, Florence Baudin, Maryl´ ene Mougel, Pascale Romby, Jean-Pierre Ebel, and Bernard Ehresmann. Probing the structure of RNAs in solution.Nucleic Acids Research, 15(22):9109–9128, November 1987

    work page 1987

  47. [47]

    Merino, Kevin A

    Edward J. Merino, Kevin A. Wilkinson, Jennifer L. Coughlan, and Kevin M. Weeks. RNA Structure Analysis at Single Nucleotide Resolution by Selective 2‘-Hydroxyl Acylation and Primer Extension (SHAPE).Journal of the American Chemical Society, 127(12):4223–4231, March 2005

    work page 2005

  48. [48]

    Advances in RNA structure analysis by chemical probing.Current Opinion in Structural Biology, 20(3):295–304, June 2010

    Kevin M Weeks. Advances in RNA structure analysis by chemical probing.Current Opinion in Structural Biology, 20(3):295–304, June 2010

    work page 2010

  49. [49]

    A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

    Tycho Marinus, Adam B Fessler, Craig A Ogle, and Danny Incarnato. A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy. Nucleic Acids Research, 49(6):e34, April 2021

    work page 2021

  50. [50]

    Lease, and Sarah A

    Tadepalli Adilakshmi, Richard A. Lease, and Sarah A. Woodson. Hydroxyl radical footprinting in vivo: Mapping macromolecular structures with synchrotron radiation. Nucleic Acids Research, 34(8):e64, April 2006

    work page 2006

  51. [51]

    A new reagent for in vivo structure probing of RNA G and U residues that improves RNA structure prediction alone and combined with DMS.RNA, page rna.079974.124, April 2024

    Catherine A Douds, Paul Babitzke, and Philip Bevilacqua. A new reagent for in vivo structure probing of RNA G and U residues that improves RNA structure prediction alone and combined with DMS.RNA, page rna.079974.124, April 2024

    work page 2024

  52. [52]

    IPANEMAP: Integrative probing analysis of nucleic acids empowered by multiple accessibility profiles.Nucleic Acids Research, 48(15):8276–8289, September 2020

    Afaf Saaidi, Delphine Allouche, Mireille Regnier, Bruno Sargueil, and Yann Ponty. IPANEMAP: Integrative probing analysis of nucleic acids empowered by multiple accessibility profiles.Nucleic Acids Research, 48(15):8276–8289, September 2020

    work page 2020

  53. [53]

    D. H. Sanduni Deenalattha, Chris P. Jurich, Bret Lange, Darren Armstrong, Kaitlyn Nein, and Joseph D. Yesselman. Characterizing 3D RNA structural features from DMS reactivity.bioRxiv, page 2024.11.21.624766, February 2025. Supplementary Information Physical Model The physical model underlying our analysis of RNA chemical probing experiments is illustrated...

    work page 2024

  54. [54]

    index from the reference sequence usingbowtie2-build and produced the companion FASTA index withsamtools faidx[29]. Reads were aligned to the indexed reference withrf-map -cq5 20 -cqo -mp ’--very-sensitive-local’ -b2 -bi ../{ref fasta} index, where{ref fasta} indexare the files produced bybowtie2-build. The resulting BAM files were coordinate sorted and i...

    work page