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相关论文: Prediction and statistics of pseudoknots in RNA st…

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We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of ${\cal O}(N^6)$ in time and ${\cal O}(N^4)$ in storage. The description of the…

生物物理 · 物理学 2009-09-25 Elena Rivas , Sean R. Eddy

In this paper we consider the problem of RNA folding with pseudoknots. We use a graphical representation in which the secondary structures are described by planar diagrams. Pseudoknots are identified as non-planar diagrams. We analyze the…

生物大分子 · 定量生物学 2007-05-23 G. Vernizzi , H. Orland , A. Zee

Computational prediction of RNA structures is an important problem in computational structural biology. Studies of RNA structure formation often assume that the process starts from a fully synthesized sequence. Experimental evidence,…

生物大分子 · 定量生物学 2021-04-28 Vo Hong Thanh , Dani Korpela , Pekka Orponen

Dual graphs have been applied to model RNA secondary structures with pseudoknots, or intertwined base pairs. In previous works, a linear-time algorithm was introduced to partition dual graphs into maximally connected components called…

生物大分子 · 定量生物学 2021-09-09 Louis Petingi

An RNA sequence is a word over an alphabet on four elements $\{A,C,G,U\}$ called bases. RNA sequences fold into secondary structures where some bases match one another while others remain unpaired. Pseudoknot-free secondary structures can…

数据结构与算法 · 计算机科学 2018-03-28 Édouard Bonnet , Paweł Rzążewski , Florian Sikora

The kinetic folding of RNA sequences into secondary structures is modeled as a complex adaptive system, the components of which are possible RNA structural rearrangements (SRs) and their associated bases and base pairs. RNA bases and base…

生物大分子 · 定量生物学 2007-05-23 Wilfred Ndifon

In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly…

机器学习 · 计算机科学 2020-06-11 Xinshi Chen , Yu Li , Ramzan Umarov , Xin Gao , Le Song

Existing state-of-the-art methods that take a single RNA sequence and predict the corresponding RNA secondary-structure are thermodynamic methods. These predict the most stable RNA structure, but do not consider the process of structure…

生物大分子 · 定量生物学 2012-07-26 Jeff R. Proctor , Irmtraud M. Meyer

In this paper, we develop new algorithms for the basic RNA folding problem. Given an RNA sequence that contains $n$ nucleotides, the goal of the problem is to compute a pseudoknot-free secondary structure that maximizes the number of base…

数据结构与算法 · 计算机科学 2015-03-23 Yinglei Song

RNA molecules are known to form complex secondary structures including pseudoknots. A systematic framework for the enumeration, classification and prediction of secondary structures is critical to determine the biological significance of…

生物大分子 · 定量生物学 2025-12-24 Rayan Ibrahim , Allison H. Moore

We further develop the large $ N $ formalism presented by some of us in earlier works in order to recursively calculate the partition function of a singly pseudoknotted RNA. We demonstrate that this calculation takes time proportional to…

软凝聚态物质 · 物理学 2009-09-29 M. Pillsbury , J. A. Taylor , H. Orland , A. Zee

It is the first step for understanding how RNA structure folds from base sequences that to know how its secondary structure is formed. Traditional energy-based algorithms are short of precision, particularly for non-nested sequences, while…

量子物理 · 物理学 2023-05-18 Ji Jiang , Qipeng Yan , Ye Li , Min Lu , Ziwei Cui , Menghan Dou , Qingchun Wang , Yu-Chun Wu , Guo-Ping Guo

We present McGenus, an algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. McGenus can treat sequences of up to 1000 bases and…

生物大分子 · 定量生物学 2013-02-18 M. Bon , C. Micheletti , H. Orland

RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy (MFE) methods to partition function-based methods that account for folding ensembles…

生物大分子 · 定量生物学 2024-02-08 He Zhang , Liang Zhang , David H. Mathews , Liang Huang

RNA molecules are essential cellular machines performing a wide variety of functions for which a specific three-dimensional structure is required. Over the last several years, experimental determination of RNA structures through X-ray…

生物大分子 · 定量生物学 2015-06-11 Tristan Cragnolini , Philippe Derreumaux , Samuela Pasquali

Predicting the secondary structure of RNA is a core challenge in computational biology, essential for understanding molecular function and designing novel therapeutics. The field has evolved from foundational but accuracy-limited…

生物大分子 · 定量生物学 2026-05-20 Giuseppe Sacco , Giovanni Bussi , Guido Sanguinetti

We present TT2NE, a new algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. TT2NE guarantees to find the minimum free energy…

生物大分子 · 定量生物学 2010-10-22 Michael Bon , Henri Orland

RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of…

生物物理 · 物理学 2019-05-21 Ya-Zhou Shi , Lei Jin , Chen-Jie Feng , Ya-Lan Tan , Zhi-Jie Tan

RNA secondary structure folding kinetics is known to be important for the biological function of certain processes, such as the hok/sok system in E. coli. Although linear algebra provides an exact computational solution of secondary…

生物大分子 · 定量生物学 2017-07-14 Peter Clote , Amir H. Bayegan

We consider the folding of a self-avoiding homopolymer on a lattice, with saturating hydrogen bond interactions. Our goal is to numerically evaluate the statistical distribution of the topological genus of pseudoknotted configurations. The…

生物大分子 · 定量生物学 2009-11-11 G. Vernizzi , P. Ribeca , H. Orland , A. Zee
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