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Advances in RNA secondary structure prediction and RNA modifications: Methods, data, and applications

T0 review · 0 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read RNA secondary structure and RNA modifications are one coupled system, and prediction methods are converging on modeling them together.

desk verdict A competent, current survey of RNA secondary structure prediction and modification tools; no new methods, but the synthesis and m6A parameter discussion make it worth refereeing. read the letter →

arxiv 2501.04056 v1 pith:VK5FANCU submitted 2025-01-07 q-bio.BM

Shu Yang , Nhat Truong Pham , Ziyang Li , Jae Young Baik , Joseph Lee , Tianhua Zhai , Weicheng Yu , Bojian Hou
show 5 more authors
Tianqi Shang Weiqing He Duy Duong-Tran Mayur Naik Li Shen
This is my paper · ORCID
classification q-bio.BM
keywords RNAsecondarystructurepredictionmodificationsm6Anearest-neighborthermodynamicsdeeplearningfoundationmodelsepitranscriptomicsbenchmarkdatasets
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that RNA secondary structure prediction and RNA modification identification have grown from separate toolboxes into a single intertwined problem: chemical modifications such as m6A change the free-energy landscape of folding, while structural context determines where modifications are installed. It organizes four decades of prediction methods into thermodynamic, comparative, learning-based, and hybrid families, then catalogs modification-prediction tools by modification type. Its distinctive contribution is to make explicit the two-way link between structure and modification, and to survey the data resources and energy parameters, such as m6A-expanded nearest-neighbor parameters, that now make joint modeling possible. If the review's reading of the field is right, the near-term payoff is structure prediction that accounts for modifications and modification prediction that uses structure features, improving RNA therapeutics design.

What carries the argument

The load-bearing object is the nearest-neighbor free energy model: a set of empirically measured parameters that decomposes an RNA secondary structure into loop and stacking units whose free energies add. The review shows how this same machinery, originally built on unmodified nucleotides, is being extended to modified RNAs, most completely for m6A, with full parameter sets from optical melting experiments, and how the Zuker and McCaskill dynamic programming algorithms use these parameters to predict modified structures. On the modification side, the counterpart machinery is the sequence-feature classifiers, including SVM, CNN, transformer, and ensemble methods, that consume RNA structural encodings such as RNAfold MFE values to predict modification sites. The review's organizational machinery is its four-way classification of RNA secondary structure prediction methods.

What would settle it

Run a head-to-head test of m6A-aware folding versus standard folding on in-cell RNA structure probing data such as SHAPE-MaP maps of m6A-containing transcripts; if the m6A parameters do not improve accuracy over unmodified parameters, the review's core recommendation for modified-RNA prediction collapses. Alternatively, retrain a top deep-learning model on a redundancy-filtered holdout from ArchiveII or bpRNA-1m and show that method rankings invert.

Watch

Extended reading notes

Core claim

The central discovery the paper seeks to establish is that RNA secondary structure and RNA modifications are not separate phenomena but a coupled regulatory system: RNA secondary structure motifs guide where writers like METTL3/METTL14 install modifications, and modifications such as m6A, A-to-I editing, and pseudouridine reshape the structure by changing base-pair stabilities and annealing kinetics. The review supports this by tracing method progression from Nussinov-style dynamic programming and Turner nearest-neighbor free energy models, through comparative SCFG methods, to deep learning and foundational RNA language models, and by highlighting the emergence of energy parameters for modified nucleotides, particularly the complete m6A nearest-neighbor parameter set measured by optical melting experiments and integrated into RNAstructure and ViennaRNA. The intended conclusion is that accurate prediction of functional RNA behavior requires models that fold modified sequences with modification-aware energy parameters and that exploit structural context to predict modification sites.

Load-bearing premise

The review's guidance assumes that thermodynamic parameters measured on short synthetic RNAs in the laboratory, especially the m6A nearest-neighbor values, transfer faithfully to how modified RNAs fold inside cells, and that the benchmark datasets it highlights are representative enough to rank methods reliably.

Editorial extensions

If this is right

  • Structure prediction tools that ignore modifications will systematically mispredict folding of modified transcripts, especially in m6A-rich regions such as 3' UTRs.
  • Including m6A-aware nearest-neighbor parameters in mainstream packages like RNAstructure and ViennaRNA makes modified-RNA folding computationally practical today.
  • Using RNA secondary structure features in modification predictors improves site prediction for m6A, 2'-O-methylation, and other marks.
  • Deep learning and RNA foundation models pre-trained on large sequence collections are the likely route to overcome data scarcity and bias in RNA structure prediction.
  • Joint modeling of structure and modification can inform the rational design of RNA vaccines and gene therapies, for example with N1-methylpseudouridine-modified messages.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper does not run: benchmark m6A-aware folding against transcriptome-wide in-cell probing data such as SHAPE-MaP to see whether melting-derived parameters improve accuracy over unmodified parameters in vivo.
  • The review's taxonomy implies that energy-based and learning-based approaches are converging; a plausible next step is fully differentiable folding that trains end-to-end on modified RNA structure data.
  • If the m6A-switch mechanism generalizes to other modifications, structure-aware modification prediction could become a general epitranscriptomics design rule rather than a per-modification special case.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 6 minor

Summary. This manuscript is a review of computational methods for RNA secondary structure (RSS) prediction and RNA modification prediction, together with the data resources used in these areas and the interplay between the two fields. It organizes RSS prediction into energy-based, comparative, learning-based, and hybrid approaches, surveys prediction tools for six RNA modification types, discusses the bidirectional effects of modifications and structure with emphasis on m6A and nearest-neighbor thermodynamic parameters, and lists relevant databases. The review closes with challenges such as data scarcity, pseudoknot handling, and therapeutic applications.

Significance. If accurate, this survey provides a useful structured map of a fast-moving field, and it is generally faithful to the cited literature. Its strengths include its breadth—covering method classes, tools, and data—and its focused discussion of m6A thermodynamics, including the Kierzek et al. and Szabat et al. parameter sets and the ViennaRNA modified-base support. It also explicitly flags data biases such as redundancy in ArchiveII and limitations of PDB-derived RNA sets. The review does not introduce new methods, so its value rests on the reliability of its synthesis; the issues found below are local and do not undermine the overall contribution.

minor comments (6)
  1. [Sec. 1 vs. Sec. 5.2/Table 4] Section 1 states that MODOMICS catalogs 'over 335 natural RNA modifications', but Section 5.2 and Table 4 report 'more than 170 different RNA modifications, 429 different RNA modified residues (335 natural ones)'; this internal inconsistency is a factual error and should be corrected.
  2. [Table 2 / Sec. 2.4] RNA-FM and RNAErnie are labeled as hybrid (H) in Table 2 and discussed under hybrid methods in Section 2.4, yet they are pre-trained language models rather than combinations of thermodynamic, comparative, and learning strategies as defined in Table 1; please clarify the hybrid definition or reclassify these entries.
  3. [Sec. 5.2] The subsection title 'RSS related RNA modification datasets' is misleading because ENCORI and eCLIP contain protein-RNA interaction data rather than RNA modification data; consider renaming the subsection or explicitly explaining their indirect relevance.
  4. [Table 2] The TORNADO entry lists 'Single sequence (FASTA, Stockholm)' as input, but Stockholm is a multiple-sequence alignment format; please clarify what input TORNADO actually accepts.
  5. [Throughout] There are numerous typographical and formatting errors, including 'Waston-Crick', 'Felsenstain', 'unaired region' (should be 'unpaired'), 'the the NIH grants', 'seqeunces', 'Lewi et al.' (should be 'Lewis et al.'), 'Tanzeret al.', 'Conventional' (should be 'Conventionally'), and inconsistent spacing/casing such as 'L INEAR FOLD', 'RNAFOLD', and 'MODOMICs'.
  6. [Refs / disclosure] Several of the cited modification prediction tools (e.g., H2Opred, Meta-2OM, ac4C-AFL, MST-m6A) are authored by a co-author of this review; adding a disclosure statement would improve transparency.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review is descriptive, and co-authored tools are cited as external published methods rather than as load-bearing inputs.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

As a review, the paper introduces no new parameters or entities. The axioms listed are the organizational and factual assumptions the survey relies on, all drawn from prior literature.

assumptions (3)
  • domain assumption RNA secondary structure prediction can be usefully categorized into energy-based, comparative, learning-based, and hybrid approaches.
    The entire structure of the review (Table 2 and sections 2.1-2.4) depends on this taxonomy, which is a modeling choice rather than a proven fact; some methods (e.g., CONTRAfold) straddle categories.
  • domain assumption The m6A nearest-neighbor thermodynamic parameters measured in vitro (Kierzek et al. 2022; Szabat et al. 2022) are valid for predicting secondary structure of m6A-modified RNA in vivo.
    Section 4.3 presents these parameters as the basis for modification-aware structure prediction, but the review does not critically assess the in vitro to in vivo transfer.
  • domain assumption Standard benchmarks (ArchiveII, bpRNA-1m, RNAStralign) and databases (Rfam, MODOMICS) accurately represent the ground truth for RNA secondary structure and modifications.
    Section 5 lists these as the reference data for the field; the review does not discuss their biases beyond noting redundancy in bpRNA-1m.

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Cite this review

Pith. "Pith review of Advances in RNA secondary structure prediction and RNA modifications: Methods, data, and applications." pith.science (2026). https://pith.science/paper/VK5FANCU

@misc{pith2026250104056,
  author       = {Pith},
  title        = {Pith review of: Advances in RNA secondary structure prediction and RNA modifications: Methods, data, and applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VK5FANCU}},
  note         = {Machine review of arXiv:2501.04056}
}
read the original abstract

Due to the hierarchical organization of RNA structures and their pivotal roles in fulfilling RNA functions, the formation of RNA secondary structure critically influences many biological processes and has thus been a crucial research topic. This review sets out to explore the computational prediction of RNA secondary structure and its connections to RNA modifications, which have emerged as an active domain in recent years. We first examine the progression of RNA secondary structure prediction methodology, focusing on a set of representative works categorized into thermodynamic, comparative, machine learning, and hybrid approaches. Next, we survey the advances in RNA modifications and computational methods for identifying RNA modifications, focusing on the prominent modification types. Subsequently, we highlight the interplay between RNA modifications and secondary structures, emphasizing how modifications such as m6A dynamically affect RNA folding and vice versa. In addition, we also review relevant data sources and provide a discussion of current challenges and opportunities in the field. Ultimately, we hope our review will be able to serve as a cornerstone to aid in the development of innovative methods for this emerging topic and foster therapeutic applications in the future.

Figures

Figures reproduced from arXiv: 2501.04056 by the authors.

Figure 1
Figure 1. An example of RNA secondary structures. As shown in this example, an RSS can be decomposed into a set of stem-loop structural motifs (indicated with different colors). The RNA sequence and the corresponding dot-bracket notation are shown at the bottom. This plot is generated with the help of the Forna visualization tool from the ViennaRNA package 49. The example assumes pseudoknot-free. foundational contribution tha… view at source ↗
Figure 2
Figure 2. RSS free energy computation based on nearest neighbor energy model. (A). An example of the free energy calculation for an RSS using Turner’s nearest neighbor parameters 51. This figure is generated with the help of the Forna visualization tool from the ViennaRNA package 49. The overall free energy of a given RNA secondary structure can be expressed as the sum of free energies across different structural units. For t… view at source ↗
Figure 3
Figure 3. A schematic illustration of comparative RNA secondary structure prediction strategy. Assuming the functionally important structures (not necessarily the sequences) are conserved through evolution, the idea is to first align equivalent sequences from related species (S1, .., S5), then find pairs of co-varying alignment columns (green), finally incorporate the co-varying information into the predicted structure. RNADE… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: An example of the interplay between RNA modification and secondary structures. The m6A modification is used here to illustrate the effects of modifications on RNA secondary structures. The left plot shows that stable m6A:U basepairing is only feasible in the anti-confo…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards secondary structure prediction of longer mRNA sequences using a quantum-centric optimization scheme

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Reference graph

Works this paper leans on

146 extracted references · 78 canonical work pages · cited by 1 Pith paper

  1. [1]

    Probing the dynamic RNA structurome and its functions

    Spitale RC, Incarnato D. Probing the dynamic RNA structurome and its functions. Nature Reviews Genetics. 2023;24(3):178- 96

    2023

  2. [2]

    Rock, scissors, paper: How RNA structure informs function

    Assmann SM, Chou HL, Bevilacqua PC. Rock, scissors, paper: How RNA structure informs function. The Plant Cell. 2023;35(6):1671-707

    2023

  3. [3]

    The roles of structural dynamics in the cellular functions of RNAs

    Ganser LR, Kelly ML, Herschlag D, Al-Hashimi HM. The roles of structural dynamics in the cellular functions of RNAs. Nature reviews Molecular cell biology. 2019;20(8):474-89

    2019

  4. [4]

    Riboswitches: small-molecule recognition by gene regulatory RNAs

    Edwards TE, Klein DJ, Ferr ´e-D’Amar´e AR. Riboswitches: small-molecule recognition by gene regulatory RNAs. Current opinion in structural biology. 2007;17(3):273-9

    2007

  5. [5]

    Non-coding RNA: a new frontier in regulatory biology

    Fu XD. Non-coding RNA: a new frontier in regulatory biology. National science review. 2014;1(2):190-204

    2014

  6. [6]

    RNA-targeted therapeutics

    Crooke ST, Witztum JL, Bennett CF, Baker BF. RNA-targeted therapeutics. Cell metabolism. 2018;27(4):714-39

    2018

  7. [7]

    RNA structure and stability

    Nowakowski J, Tinoco Jr I. RNA structure and stability. In: Seminars in virology. vol. 8. Elsevier; 1997. p. 153-65

    1997

  8. [8]

    How RNA folds

    Tinoco Jr I, Bustamante C. How RNA folds. Journal of molecular biology. 1999;293(2):271-81

    1999

Show all 146 references
  1. [9]

    RNA quaternary structure and global symmetry

    Jones CP, Ferr ´e-D’Amar´e AR. RNA quaternary structure and global symmetry. Trends in biochemical sciences. 2015;40(4):211-20

    2015

  2. [10]

    Folding and finding RNA secondary structure

    Mathews DH, Moss WN, Turner DH. Folding and finding RNA secondary structure. Cold Spring Harbor perspectives in biology. 2010;2(12):a003665

    2010

  3. [11]

    Highly accurate protein structure prediction with AlphaFold

    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. nature. 2021;596(7873):583-9

    2021

  4. [12]

    RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

    Singh J, Hanson J, Paliwal K, Zhou Y . RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nature communications. 2019;10(1):5407

    2019

  5. [13]

    UFold: fast and accurate RNA secondary structure prediction with deep learning

    Fu L, Cao Y , Wu J, Peng Q, Nie Q, Xie X. UFold: fast and accurate RNA secondary structure prediction with deep learning. Nucleic acids research. 2022;50(3):e14-4

    2022

  6. [14]

    Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions

    Chen J, Hu Z, Sun S, Tan Q, Wang Y , Yu Q, et al. Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions. arXiv preprint arXiv:220400300. 2022

    2022

  7. [15]

    Messenger RNA modifications: form, distribution, and function

    Gilbert WV , Bell TA, Schaening C. Messenger RNA modifications: form, distribution, and function. Science. 2016;352(6292):1408-12

    2016

  8. [16]

    RNA modifications in structure prediction–Status quo and future challenges

    Tanzer A, Hofacker IL, Lorenz R. RNA modifications in structure prediction–Status quo and future challenges. Methods. 2019;156:32-9

    2019

  9. [17]

    MODOMICS: a database of RNA modifications and related information

    Cappannini A, Ray A, Purta E, Mukherjee S, Boccaletto P, Moafinejad SN, et al. MODOMICS: a database of RNA modifications and related information. 2023 update. Nucleic Acids Research. 2024;52(D1):D239-44

    2023

  10. [18]

    Secondary structure prediction for RNA sequences including N6-methyladenosine

    Kierzek E, Zhang X, Watson RM, Kennedy SD, Szabat M, Kierzek R, et al. Secondary structure prediction for RNA sequences including N6-methyladenosine. Nature communications. 2022;13(1):1271

    2022

  11. [19]

    RNA modifications and structures cooperate to guide RNA–protein interactions

    Lewis CJ, Pan T, Kalsotra A. RNA modifications and structures cooperate to guide RNA–protein interactions. Nature reviews Molecular cell biology. 2017;18(3):202-10

    2017

  12. [20]

    The emerging role of RNA modifications in the regulation of mRNA stability

    Boo SH, Kim YK. The emerging role of RNA modifications in the regulation of mRNA stability. Experimental & molecular medicine. 2020;52(3):400-8

    2020

  13. [21]

    Dynamic regulation and functions of mRNA m6A modification

    Wang S, Lv W, Li T, Zhang S, Wang H, Li X, et al. Dynamic regulation and functions of mRNA m6A modification. Cancer cell international. 2022;22(1):48

    2022

  14. [22]

    RNA modifications: importance in immune cell biology and related diseases

    Cui L, Ma R, Cai J, Guo C, Chen Z, Yao L, et al. RNA modifications: importance in immune cell biology and related diseases. Signal transduction and targeted therapy. 2022;7(1):334

    2022

  15. [23]

    RNA modifications in cellular metabolism: implications for metabolism-targeted therapy and immunotherapy

    Liu WW, Zheng SQ, Li T, Fei YF, Wang C, Zhang S, et al. RNA modifications in cellular metabolism: implications for metabolism-targeted therapy and immunotherapy. Signal Transduction and Targeted Therapy. 2024;9(1):70

    2024

  16. [24]

    RNA modifications: an overview of select web-based tools

    Ramos J. RNA modifications: an overview of select web-based tools. RNA. 2022;28(11):1440-5

    2022

  17. [25]

    Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

    Song Z, Huang D, Song B, Chen K, Song Y , Liu G, et al. Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications. Nature communications. 2021;12(1):4011

    2021

  18. [26]

    Towards retraining-free RNA modification prediction with incremental learning

    Qiao J, Jin J, Yu H, Wei L. Towards retraining-free RNA modification prediction with incremental learning. Information Sciences. 2024;660:120105

    2024

  19. [27]

    TransRNAm: identifying twelve types of RNA modifications by an interpretable multi-label deep learning model based on Transformer

    Chen T, Wu T, Pan D, Xie J, Zhi J, Wang X, et al. TransRNAm: identifying twelve types of RNA modifications by an interpretable multi-label deep learning model based on Transformer. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2023

    2023

  20. [28]

    RMBase v3

    Xuan J, Chen L, Chen Z, Pang J, Huang J, Lin J, et al. RMBase v3. 0: decode the landscape, mechanisms and functions of RNA modifications. Nucleic Acids Research. 2024;52(D1):D273-84

    2024

  21. [29]

    m7GHub V2

    Wang X, Zhang Y , Chen K, Liang Z, Ma J, Xia R, et al. m7GHub V2. 0: an updated database for decoding the N7- methylguanosine (m7G) epitranscriptome. Nucleic Acids Research. 2024;52(D1):D203-12

    2024

  22. [30]

    H2Opred: a robust and efficient hybrid deep learning model for predicting 2’-O-methylation sites in human RNA

    Pham NT, Rakkiyapan R, Park J, Malik A, Manavalan B. H2Opred: a robust and efficient hybrid deep learning model for predicting 2’-O-methylation sites in human RNA. Briefings in Bioinformatics. 2024;25(1):bbad476

    2024

  23. [31]

    Meta-2OM: a multi-classifier meta-model for the accurate prediction of RNA 2’-O-methylation sites in human RNA

    Harun-Or-Roshid M, Pham NT, Manavalan B, Kurata H. Meta-2OM: a multi-classifier meta-model for the accurate prediction of RNA 2’-O-methylation sites in human RNA. PloS One. 2024;19(6):e0305406

    2024

  24. [32]

    Nmix: a hybrid deep learning model for precise prediction of 2’-O-methylation sites based on multi-feature fusion and ensemble learning

    Geng YQ, Lai FL, Luo H, Gao F. Nmix: a hybrid deep learning model for precise prediction of 2’-O-methylation sites based on multi-feature fusion and ensemble learning. Briefings in Bioinformatics. 2024;25(6):bbae601

    2024

  25. [33]

    ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning

    Pham NT, Terrance AT, Jeon YJ, Rakkiyappan R, Manavalan B. ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning. Molecular Therapy-Nucleic Acids. 2024;35(2)

    2024

  26. [34]

    V oting-ac4C: Pre-trained large RNA language model enhances RNA N4- acetylcytidine site prediction

    Jia Y , Zhang Z, Yan S, Zhang Q, Wei L, Cui F. V oting-ac4C: Pre-trained large RNA language model enhances RNA N4- acetylcytidine site prediction. International Journal of Biological Macromolecules. 2024;282:136940

    2024

  27. [35]

    iRNA-ac4C: a novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA

    Su W, Xie XQ, Liu XW, Gao D, Ma CY , Zulfiqar H, et al. iRNA-ac4C: a novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA. International Journal of Biological Macromolecules. 2023;227:1174-81

    2023

  28. [36]

    TransAC4C—a novel interpretable architecture for multi-species identification of N4- acetylcytidine sites in RNA with single-base resolution

    Liu R, Zhang Y , Wang Q, Zhang X. TransAC4C—a novel interpretable architecture for multi-species identification of N4- acetylcytidine sites in RNA with single-base resolution. Briefings in Bioinformatics. 2024;25(3):bbae200

    2024

  29. [37]

    Deepm5C: a deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

    Hasan MM, Tsukiyama S, Cho JY , Kurata H, Alam MA, Liu X, et al. Deepm5C: a deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy. Molecular Therapy. 2022;30(8):2856-67

    2022

  30. [38]

    MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models

    Kurata H, Harun-Or-Roshid M, Hasan MM, Tsukiyama S, Maeda K, Manavalan B. MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models. Methods. 2024;227:37-47

    2024

  31. [39]

    XGBoost framework with feature selection for the prediction of RNA N5-methylcytosine sites

    Abbas Z, ur Rehman M, Tayara H, Zou Q, Chong KT. XGBoost framework with feature selection for the prediction of RNA N5-methylcytosine sites. Molecular Therapy. 2023;31(8):2543-51

    2023

  32. [40]

    MST-m6A: A Novel Multi-Scale Transformer-based Framework for Accurate Prediction of m6A Modification Sites Across Diverse Cellular Contexts

    Su Q, Phan LT, Pham NT, Wei L, Manavalan B, et al. MST-m6A: A Novel Multi-Scale Transformer-based Framework for Accurate Prediction of m6A Modification Sites Across Diverse Cellular Contexts. Journal of Molecular Biology. 2024:168856

    2024

  33. [41]

    BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-methyladenosine Sites

    Xia Y , Zhang Y , Liu D, Zhu YH, Wang Z, Song J, et al. BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-methyladenosine Sites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2024

    2024

  34. [42]

    Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7- Methlguanosine Site Prediction

    Zhao Y , Jin J, Gao W, Qiao J, Wei L. Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7- Methlguanosine Site Prediction. Journal of Chemical Information and Modeling. 2024;64(15):6230-40

    2024

  35. [43]

    THRONE: a new approach for accurate prediction of human RNA N7-methylguanosine sites

    Shoombuatong W, Basith S, Pitti T, Lee G, Manavalan B. THRONE: a new approach for accurate prediction of human RNA N7-methylguanosine sites. Journal of Molecular Biology. 2022;434(11):167549

    2022

  36. [44]

    Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches

    Bugnon LA, Edera AA, Prochetto S, Gerard M, Raad J, Fenoy E, et al. Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches. Briefings in Bioinformatics. 2022;23(4):bbac205

    2022

  37. [45]

    Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery

    Sato K, Hamada M. Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery. Briefings in Bioinformatics. 2023;24(4):bbad186

    2023

  38. [46]

    When will RNA get its AlphaFold moment? Nucleic Acids Research

    Schneider B, Sweeney BA, Bateman A, Cerny J, Zok T, Szachniuk M. When will RNA get its AlphaFold moment? Nucleic Acids Research. 2023;51(18):9522-32

    2023

  39. [47]

    Machine learning modeling of RNA structures: methods, challenges and future perspectives

    Wu KE, Zou JY , Chang H. Machine learning modeling of RNA structures: methods, challenges and future perspectives. Briefings in Bioinformatics. 2023;24(4):bbad210

    2023

  40. [48]

    Advances and opportunities in RNA structure experimental determination and computational modeling

    Zhang J, Fei Y , Sun L, Zhang QC. Advances and opportunities in RNA structure experimental determination and computational modeling. Nature methods. 2022;19(10):1193-207

    2022

  41. [49]

    Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams

    Kerpedjiev P, Hammer S, Hofacker IL. Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams. Bioinformatics. 2015;31(20):3377-9

    2015

  42. [50]

    Fast algorithm for predicting the secondary structure of single-stranded RNA

    Nussinov R, Jacobson AB. Fast algorithm for predicting the secondary structure of single-stranded RNA. Proceedings of the National Academy of Sciences of the USA. 1980;77(11):6309-13

    1980

  43. [51]

    Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure

    Mathews DH, Disney MD, Childs JL, Schroeder SJ, Zuker M, Turner DH. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proceedings of the National Academy of Sciences. 2004;101(19):7287-92

    2004

  44. [52]

    Optical melting measurements of nucleic acid thermodynamics

    Schroeder SJ, Turner DH. Optical melting measurements of nucleic acid thermodynamics. In: Methods in enzymology. vol

  45. [53]

    NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure

    Turner DH, Mathews DH. NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure. Nucleic acids research. 2010;38(suppl 1):D280-2

    2010

  46. [54]

    Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information

    Zuker M, Stiegler P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic acids research. 1981;9(1):133-48

    1981

  47. [55]

    Computational biology of RNA interactions

    Dieterich1 C, Stadler PF. Computational biology of RNA interactions. Wiley Interdisciplinary Reviews: RNA. 2012;4:107-20

    2012

  48. [56]

    ViennaRNA Package 2.0

    Lorenz R, Bernhart SH, H ¨oner zu Siederdissen C, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA Package 2.0. Algorithms for molecular biology. 2011;6:1-14

    2011

  49. [57]

    A dynamic programming algorithm for RNA structure prediction including pseudoknots

    Rivas E, Eddy SR. A dynamic programming algorithm for RNA structure prediction including pseudoknots. Journal of molecular biology. 1999;285(5):2053-68

    1999

  50. [58]

    Expanded sequence dependence of thermodynamic parameters provides improved prediction of RNA secondary structure

    Mathews D, Sabina J, Zuker M, Turner D. Expanded sequence dependence of thermodynamic parameters provides improved prediction of RNA secondary structure. J Mol Biol 1999b;288

  51. [59]

    Biopolymers: Original Research on Biomolecules

    The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers: Original Research on Biomolecules. 1990;29(6-7):1105-19

    1990

  52. [60]

    RNAstructure: software for RNA secondary structure prediction and analysis

    Reuter JS, Mathews DH. RNAstructure: software for RNA secondary structure prediction and analysis. BMC bioinformatics. 2010;11:1-9

    2010

  53. [61]

    RNA Secondary Structure Analysis Using RNAstructure

    Ali SE, Mittal A, Mathews DH. RNA Secondary Structure Analysis Using RNAstructure. Current protocols. 2023 July;3(7):e846. Available from: https://europepmc.org/articles/PMC11267465

    2023

  54. [62]

    Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson- Crick base pairs

    Xia T, SantaLucia Jr J, Burkard ME, Kierzek R, Schroeder SJ, Jiao X, et al. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson- Crick base pairs. Biochemistry. 1998;37(42):14719-35

    1998

  55. [63]

    A set of nearest neighbor parameters for predicting the enthalpy change of RNA secondary structure formation

    Lu ZJ, Turner DH, Mathews DH. A set of nearest neighbor parameters for predicting the enthalpy change of RNA secondary structure formation. Nucleic acids research. 2006;34(17):4912-24

    2006

  56. [64]

    A range of complex probabilistic models for RNA secondary structure prediction that include the nearest neighbor model and more

    Rivas E, Lang R, Eddy SR. A range of complex probabilistic models for RNA secondary structure prediction that include the nearest neighbor model and more. RNA. 2012;18(2):193-212

    2012

  57. [65]

    LinearFold: linear-time approximate RNA folding by 5’-to-3’dynamic programming and beam search

    Huang L, Zhang H, Deng D, Zhao K, Liu K, Hendrix DA, et al. LinearFold: linear-time approximate RNA folding by 5’-to-3’dynamic programming and beam search. Bioinformatics. 2019;35(14):i295-304

    2019

  58. [66]

    Algorithms for predicting the secondary structure of pairs and combinatorial sets of nucleic acid strands

    Andronescu MS. Algorithms for predicting the secondary structure of pairs and combinatorial sets of nucleic acid strands. University of British Columbia; 2003

    2003

  59. [67]

    Efficient parameter estimation for RNA secondary structure prediction

    Andronescu M, Condon A, Hoos HH, Mathews DH, Murphy KP. Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics. 2007;23(13):i19-28

    2007

  60. [68]

    PPfold 3.0: fast RNA secondary structure prediction using phylogeny and auxiliary data

    S ¨uk¨osd Z, Knudsen B, Kjems J, Pedersen CN. PPfold 3.0: fast RNA secondary structure prediction using phylogeny and auxiliary data. Bioinformatics. 2012;28(20):2691-2

    2012

  61. [69]

    Multithreaded comparative RNA secondary structure prediction using stochastic context-free grammars

    S ¨uk¨osd Z, Knudsen B, Værum M, Kjems J, Andersen ES. Multithreaded comparative RNA secondary structure prediction using stochastic context-free grammars. BMC bioinformatics. 2011;12:1-8

    2011

  62. [70]

    A comparative method for finding and folding RNA secondary structures within protein-coding regions

    Pedersen JS, Meyer IM, Forsberg R, Simmonds P, Hein J. A comparative method for finding and folding RNA secondary structures within protein-coding regions. Nucleic acids research. 2004;32(16):4925-36

    2004

  63. [71]

    An evolutionary model for protein-coding regions with conserved RNA structure

    Pedersen JS, Forsberg R, Meyer IM, Hein J. An evolutionary model for protein-coding regions with conserved RNA structure. Molecular biology and evolution. 2004;21(10):1913-22

    2004

  64. [72]

    RNA secondary structure prediction with convolutional neural networks

    Saman Booy M, Ilin A, Orponen P. RNA secondary structure prediction with convolutional neural networks. BMC bioinformatics. 2022;23(1):58

    2022

  65. [73]

    CONTRAfold: RNA secondary structure prediction without physics-based models

    Do CB, Woods DA, Batzoglou S. CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics. 2006 07;22(14):e90-8. Available from: https://doi.org/10.1093/bioinformatics/btl246

    2006 doi

  66. [74]

    RNA Secondary Structure Prediction By Learning Unrolled Algorithms

    Chen X, Li Y , Umarov R, Gao X, Song L. RNA Secondary Structure Prediction By Learning Unrolled Algorithms. In: International Conference on Learning Representations; 2020. Available from: https://openreview.net/forum?id= S1eALyrYDH

    2020

  67. [75]

    REDfold: accurate RNA secondary structure prediction using residual encoder-decoder network

    Chen CC, Chan YM. REDfold: accurate RNA secondary structure prediction using residual encoder-decoder network. BMC bioinformatics. 2023;24(1):122

    2023

  68. [76]

    Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning

    Singh J, Paliwal K, Zhang T, Singh J, Litfin T, Zhou Y . Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning. Bioinformatics. 2021;37(17):2589- 600

    2021

  69. [77]

    A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

    Akiyama M, Sato K, Sakakibara Y . A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model. Bioinformatics. 2017;33(22):3373-9

    2017

  70. [78]

    RNA secondary structure prediction using deep learning with thermodynamic integration

    Sato K, Akiyama M, Sakakibara Y . RNA secondary structure prediction using deep learning with thermodynamic integration. Nature communications. 2021;12(1):941

    2021

  71. [79]

    RNAalifold: improved consensus structure prediction for RNA alignments

    Bernhart SH, Hofacker IL, Will S, Gruber AR, Stadler PF. RNAalifold: improved consensus structure prediction for RNA alignments. BMC bioinformatics. 2008;9:1-13

    2008

  72. [80]

    CENTROIDFOLD: a web server for RNA secondary structure prediction

    Sato K, Hamada M, Asai K, Mituyama T. CENTROIDFOLD: a web server for RNA secondary structure prediction. Nucleic acids research. 2009;37(suppl 2):W277-80

    2009

  73. [81]

    Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning

    Wang N, Bian J, Li Y , Li X, Mumtaz S, Kong L, et al. Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning. Nature Machine Intelligence. 2024:1-10

    2024

  74. [82]

    RNA secondary structure prediction using stochastic context-free grammars and evolutionary history

    Knudsen B, Hein J. RNA secondary structure prediction using stochastic context-free grammars and evolutionary history. Bioinformatics (Oxford, England). 1999;15(6):446-54

    1999

  75. [83]

    Pfold: RNA secondary structure prediction using stochastic context-free grammars

    Knudsen B, Hein J. Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic acids research. 2003;31(13):3423-8

    2003

  76. [84]

    Evolutionary trees from DNA sequences: a maximum likelihood approach

    Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of molecular evolution. 1981;17:368-76

    1981

  77. [85]

    Syntax in universal translation

    Sakai I. Syntax in universal translation. In: Proceedings of the International Conference on Machine Translation and Applied Language Analysis; 1961

    1961

  78. [86]

    Identity mappings in deep residual networks

    He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. Springer; 2016. p. 630-45

    2016

  79. [87]

    Long Short-term Memory

    Hochreiter S. Long Short-term Memory. Neural Computation MIT-Press. 1997

    1997

  80. [88]

    Bidirectional recurrent neural networks

    Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing. 1997;45(11):2673- 81

    1997

  81. [89]

    Multi-scale context aggregation by dilated convolutions

    Yu F, Koltun V . Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations; 2016

    2016

  82. [90]

    E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction; 2022

    Shen T, Hu Z, Peng Z, Chen J, Xiong P, Hong L, et al.. E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction; 2022. Available from: https://arxiv.org/abs/2207.01586

    2022 arXiv

  83. [91]

    U-net: Convolutional networks for biomedical image segmentation

    Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer;...

    2015

  84. [92]

    A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming.[J]

    Hao Z, et al. A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming.[J]. Frontiers in genetics. 2019;10:467

    2019

  85. [93]

    Deep residual learning for image recognition

    He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770-8

    2016

  86. [94]

    The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation

    J ´egou S, Drozdzal M, Vazquez D, Romero A, Bengio Y . The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops

  87. [95]

    Prediction of RNA secondary structure using generalized centroid estimators

    Hamada M, Kiryu H, Sato K, Mituyama T, Asai K. Prediction of RNA secondary structure using generalized centroid estimators. Bioinformatics. 2009;25(4):465-73

    2009

  88. [96]

    Computational approaches for RNA energy parameter estimation

    Andronescu M, Condon A, Hoos HH, Mathews DH, Murphy KP. Computational approaches for RNA energy parameter estimation. RNA. 2010;16(12):2304-18

    2010

  89. [97]

    Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues

    Zhang Y , Wang Z, Zhang Y , Li S, Guo Y , Song J, et al. Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues. Bioinformatics. 2023;39(12):btad709

    2023

  90. [98]

    Interconnections between m6A RNA modification, RNA structure, and protein–RNA complex assembly

    H ¨ofler S, Duss O. Interconnections between m6A RNA modification, RNA structure, and protein–RNA complex assembly. Life Science Alliance. 2024;7(1)

    2024

  91. [99]

    Machine learning applications in RNA modification sites prediction

    El Allali A, Elhamraoui Z, Daoud R. Machine learning applications in RNA modification sites prediction. Computational and Structural Biotechnology Journal. 2021;19:5510-24

    2021

  92. [100]

    RNAMethPre: a web server for the prediction and query of mRNA m6A sites

    Xiang S, Liu K, Yan Z, Zhang Y , Sun Z. RNAMethPre: a web server for the prediction and query of mRNA m6A sites. PloS one. 2016;11(10):e0162707

    2016

  93. [101]

    N 6-methyladenosine-dependent RNA structural switches regulate RNA– protein interactions

    Liu N, Dai Q, Zheng G, He C, Parisien M, Pan T. N 6-methyladenosine-dependent RNA structural switches regulate RNA– protein interactions. Nature. 2015;518(7540):560-4

    2015

  94. [102]

    Structure-mediated modulation of mRNA abundance by A-to-I editing

    Br ¨ummer A, Yang Y , Chan TW, Xiao X. Structure-mediated modulation of mRNA abundance by A-to-I editing. Nature communications. 2017;8(1):1255

    2017

  95. [103]

    NNDB: An Expanded Database of Nearest Neighbor Parameters for Predicting Stability of Nucleic Acid Secondary Structures

    Mittal A, Turner DH, Mathews DH. NNDB: An Expanded Database of Nearest Neighbor Parameters for Predicting Stability of Nucleic Acid Secondary Structures. Journal of Molecular Biology. 2024:168549

    2024

  96. [104]

    A test and refinement of folding free energy nearest neighbor parameters for RNA including N6-methyladenosine

    Szabat M, Prochota M, Kierzek R, Kierzek E, Mathews DH. A test and refinement of folding free energy nearest neighbor parameters for RNA including N6-methyladenosine. Journal of Molecular Biology. 2022;434(18):167632

    2022

  97. [105]

    Modified RNAs and predictions with the ViennaRNA Package

    Varenyk Y , Spicher T, Hofacker IL, Lorenz R. Modified RNAs and predictions with the ViennaRNA Package. Bioinformatics. 2023;39(11):btad696

    2023

  98. [106]

    Nearest-neighbor parameters for 7-deaza-adenosine· uridine base pairs in RNA duplexes

    Richardson KE, Znosko BM. Nearest-neighbor parameters for 7-deaza-adenosine· uridine base pairs in RNA duplexes. RNA. 2016;22(6):934-42

    2016

  99. [107]

    Nearest Neighbor Parameters for Inosine ·Uridine Pairs in RNA Duplexes

    Wright DJ, Rice JL, Yanker DM, Znosko BM. Nearest Neighbor Parameters for Inosine ·Uridine Pairs in RNA Duplexes. Biochemistry. 2007;46(15):4625-34

    2007

  100. [108]

    Stability of RNA duplexes containing inosine· cytosine pairs

    Wright DJ, Force CR, Znosko BM. Stability of RNA duplexes containing inosine· cytosine pairs. Nucleic Acids Research. 2018;46(22):12099-108

    2018

  101. [109]

    Thermodynamic contribution and nearest-neighbor parameters of pseudouridine- adenosine base pairs in oligoribonucleotides

    Hudson GA, Bloomingdale RJ, Znosko BM. Thermodynamic contribution and nearest-neighbor parameters of pseudouridine- adenosine base pairs in oligoribonucleotides. RNA. 2013;19(11):1474

    2013

  102. [110]

    The loss of a hydrogen bond: Thermodynamic contributions of a non-standard nucleotide

    Jolley EA, Znosko BM. The loss of a hydrogen bond: Thermodynamic contributions of a non-standard nucleotide. Nucleic acids research. 2017;45(3):1479-87

    2017

  103. [111]

    Conformational flexibility in RNA: the role of dihydrouridine

    Dalluge JJ, Hashizume T, Sopchik AE, McCloskey JA, Davis DR. Conformational flexibility in RNA: the role of dihydrouridine. Nucleic acids research. 1996;24(6):1073-9

    1996

  104. [112]

    Blind tests of RNA nearest-neighbor energy prediction

    Chou FC, Kladwang W, Kappel K, Das R. Blind tests of RNA nearest-neighbor energy prediction. Proceedings of the National Academy of Sciences. 2016;113(30):8430-5

    2016

  105. [113]

    RNA folding with hard and soft constraints

    Lorenz R, Hofacker IL, Stadler PF. RNA folding with hard and soft constraints. Algorithms for Molecular Biology. 2016;11:1- 13

    2016

  106. [114]

    Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

    Sloma MF, Mathews DH. Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures. RNA. 2016;22(12):1808-18

    2016

  107. [115]

    bpRNA: large-scale automated annotation and analysis of RNA secondary structure

    Danaee P, Rouches M, Wiley M, Deng D, Huang L, Hendrix D. bpRNA: large-scale automated annotation and analysis of RNA secondary structure. Nucleic acids research. 2018;46(11):5381-94

    2018

  108. [116]

    Rfam 15: RNA families database in 2025

    Ontiveros-Palacios N, Cooke E, Nawrocki EP, Triebel S, Marz M, Rivas E, et al. Rfam 15: RNA families database in 2025. Nucleic Acids Research. 2024:gkae1023

    2025

  109. [117]

    TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs

    Tan Z, Fu Y , Sharma G, Mathews DH. TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs. Nucleic acids research. 2017;45(20):11570-81

    2017

  110. [118]

    RNAcentral 2021: secondary structure integration, improved sequence search and new member databases

    Consortium TR. RNAcentral 2021: secondary structure integration, improved sequence search and new member databases. Nucleic acids research. 2021;49(D1):D212-20

    2021

  111. [119]

    The Protein Data Bank

    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Research. 2000;28(1):235-42

    2000

  112. [120]

    RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction

    Szikszai M, Magnus M, Sanghi S, Kadyan S, Bouatta N, Rivas E. RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction. Journal of Molecular Biology. 2024. Advance online publication

    2024

  113. [121]

    starBase v2

    Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2. 0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic acids research. 2014;42(D1):D92-7

    2014

  114. [122]

    Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP)

    Van Nostrand EL, Pratt GA, Shishkin AA, Gelboin-Burkhart C, Fang MY , Sundararaman B, et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nature methods. 2016;13(6):508-14

    2016

  115. [123]

    Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure

    Mathews DH, Sabina J, Zuker M, Turner DH. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Journal of molecular biology. 1999;288(5):911-40

    1999

  116. [124]

    The comparative RNA web (CRW) site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs

    Cannone JJ, Subramanian S, Schnare MN, Collett JR, D’Souza LM, Du Y , et al. The comparative RNA web (CRW) site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. BMC bioinformatics. 2002;3:1-31

    2002

  117. [125]

    The tmRDB and SRPDB resources

    Andersen ES, Rosenblad MA, Larsen N, Westergaard JC, Burks J, Wower IK, et al. The tmRDB and SRPDB resources. Nucleic acids research. 2006;34(suppl 1):D163-8

    2006

  118. [126]

    tmRDB (tmRNA database)

    Zwieb C, Gorodkin J, Knudsen B, Burks J, Wower J. tmRDB (tmRNA database). Nucleic acids research. 2003;31(1):446-7

    2003

  119. [127]

    SRPDB: signal recognition particle database

    Rosenblad MA, Gorodkin J, Knudsen B, Zwieb C, Samuelsson T. SRPDB: signal recognition particle database. Nucleic acids research. 2003;31(1):363-4

    2003

  120. [128]

    The ribonuclease P database

    Brown JW. The ribonuclease P database. Nucleic acids research. 1998;26(1):351-2

    1998

  121. [129]

    tRNAdb 2009: compilation of tRNA sequences and tRNA genes

    J ¨uhling F, M¨orl M, Hartmann RK, Sprinzl M, Stadler PF, P ¨utz J. tRNAdb 2009: compilation of tRNA sequences and tRNA genes. Nucleic acids research. 2009;37(suppl 1):D159-62

    2009

  122. [130]

    The RCSB protein data bank: integrative view of protein, gene and 3D structural information

    Rose PW, Prli ´c A, Altunkaya A, Bi C, Bradley AR, Christie CH, et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic acids research. 2016:gkw1000

    2016

  123. [131]

    Rfam 12.0: updates to the RNA families database

    Nawrocki EP, Burge SW, Bateman A, Daub J, Eberhardt RY , Eddy SR, et al. Rfam 12.0: updates to the RNA families database. Nucleic acids research. 2015;43(D1):D130-7

    2015

  124. [132]

    GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes

    Chan PP, Lowe TM. GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes. Nucleic acids research. 2016;44(D1):D184-9

    2016

  125. [133]

    MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search

    Chen K, Litfin T, Singh J, Zhan J, Zhou Y . MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search. Genomics, Proteomics & Bioinformatics. 2024;22(1):qzae018

    2024

  126. [134]

    UNI-RNA: universal pre-trained models revolutionize RNA research

    Wang X, Gu R, Chen Z, Li Y , Ji X, Ke G, et al. UNI-RNA: universal pre-trained models revolutionize RNA research. bioRxiv. 2023:2023-07

    2023

  127. [135]

    Accurate SHAPE-directed RNA structure determination

    Deigan KE, Li TW, Mathews DH, Weeks KM. Accurate SHAPE-directed RNA structure determination. Proceedings of the National Academy of Sciences. 2009;106(1):97-102

    2009

  128. [136]

    Suppression of RNA recognition by Toll-like receptors: the impact of nucleoside modification and the evolutionary origin of RNA

    Karik ´o K, Buckstein M, Ni H, Weissman D. Suppression of RNA recognition by Toll-like receptors: the impact of nucleoside modification and the evolutionary origin of RNA. Immunity. 2005;23(2):165-75

    2005

  129. [137]

    An RNA thermosensor controls expression of virulence genes in Listeria monocytogenes

    Johansson J, Mandin P, Renzoni A, Chiaruttini C, Springer M, Cossart P. An RNA thermosensor controls expression of virulence genes in Listeria monocytogenes. Cell. 2002;110(5):551-61

    2002

  130. [138]

    Protein interaction network analysis to investigate stress response, virulence, and antibiotic resistance mechanisms in Listeria monocytogenes

    Hanes R, Zhang F, Huang Z. Protein interaction network analysis to investigate stress response, virulence, and antibiotic resistance mechanisms in Listeria monocytogenes. Microorganisms. 2023;11(4):930

    2023

  131. [139]

    Discovery of MurA inhibitors as novel antimicrobials through an integrated computational and experimental approach

    Zhang F, Graham J, Zhai T, Liu Y , Huang Z. Discovery of MurA inhibitors as novel antimicrobials through an integrated computational and experimental approach. Antibiotics. 2022;11(4):528

    2022

  132. [140]

    Comparative Gene Expression Analysis of Salmonella Typhimurium DT104 in Ground Chicken Extract and Brain Heart Infusion Broth

    Liu Y , Zhang F, Hawkins JL, Elder JR, Baranzoni GM, Huang Z, et al. Comparative Gene Expression Analysis of Salmonella Typhimurium DT104 in Ground Chicken Extract and Brain Heart Infusion Broth. Microorganisms. 2024;12(7):1461

    2024

  133. [141]

    Small RNAs encoded within genetic islands of Salmonella typhimurium show host-induced expression and role in virulence

    Padalon-Brauch G, Hershberg R, Elgrably-Weiss M, Baruch K, Rosenshine I, Margalit H, et al. Small RNAs encoded within genetic islands of Salmonella typhimurium show host-induced expression and role in virulence. Nucleic acids research. 2008;36(6):1913-27

    2008

  134. [142]

    Investigation of Small Molecule Inhibitors for OAS 1 to Enhance Gene Therapy

    Zhai T, Song Z, Warga E, Elmer J, Huang Z. Investigation of Small Molecule Inhibitors for OAS 1 to Enhance Gene Therapy. In: 2023 AIChE Annual Meeting. AIChE; 2023

    2023

  135. [143]

    Dynamic RNA modifications in gene expression regulation

    Roundtree IA, Evans ME, Pan T, He C. Dynamic RNA modifications in gene expression regulation. Cell. 2017;169(7):1187- 200

    2017

  136. [144]

    Reading, writing and erasing mRNA methylation

    Zaccara S, Ries RJ, Jaffrey SR. Reading, writing and erasing mRNA methylation. Nature reviews Molecular cell biology. 2019;20(10):608-24

    2019

  137. [145]

    Structure Prediction of Large RNAs with AlphaFold3 Highlights its Capabilities and Limitations

    McDonnell RT, Henderson AN, Elcock AH. Structure Prediction of Large RNAs with AlphaFold3 Highlights its Capabilities and Limitations. Journal of Molecular Biology. 2024;436(22):168816

    2024

  138. [468]

    Elsevier; 2009. p. 371-87

    2009

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.