A Bayesian approach to pairwise RNA Secondary Structure Alignment

2006 ◽  
Author(s):  
Zhuozhi Wang ◽  
Elisabeth R. M. Tillier
Keyword(s):  
Secondary Structure ◽  
Bayesian Approach ◽  
Rna Secondary Structure ◽  
Structure Alignment

RNA secondary structure alignment based on an extended binary coding method

2011 ◽  
Vol 111 (5) ◽  
pp. 978-982 ◽  
Author(s):  
Zhi Cao ◽  
Bo Liao ◽  
Renfa Li ◽  
Jiawei Luo ◽  
Wen Zhu
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Structure Alignment ◽  
Binary Coding ◽  
Coding Method

A Binary Coding Method for RNA Secondary Structure Alignment

2010 ◽  
Vol 7 (3) ◽  
pp. 619-622
Author(s):  
Xuyu Xiang ◽  
Bo Liao ◽  
Dafang Zhang ◽  
Jiawei Luo
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Structure Alignment ◽  
Binary Coding ◽  
Coding Method

DSARna: RNA Secondary Structure Alignment Based on Digital Sequence Representation

2020 ◽  
Vol 23 ◽  
Author(s):  
Longjian Gao ◽  
Chengzhen Xu ◽  
Wangan Song ◽  
Feng Xiao ◽  
Xiaomin Wu ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
High Throughput Sequencing ◽  
Structural Information ◽  
Dynamic Programming Algorithm ◽  
Structure Alignment ◽  
Programming Algorithm ◽  
Structure Information ◽  
Alignment Algorithms

Background: With increasing applications and development of high-throughput sequencing, knowledge of the primary structure of RNA has expanded exponentially. Moreover, the function of RNA is determined by the secondary or higher RNA structure, and similar structures are related to similar functions, such as the secondary clover structure of tRNA. Therefore, RNA structure alignment is an important subject in computational biology and bioinformatics to accurately predict function. However, the traditional RNA structure alignment algorithms have some drawbacks such as high complexity and easy loss of secondary structure information. Objective: To study RNA secondary structure alignment according to the shortcomings of existing secondary structure alignment algorithms and the characteristics of RNA secondary structure. Method: We propose a new digital sequence RNA structure representation algorithm named “DSARna” . Then based on a dynamic programming algorithm, the scoring matrix and binary path matrix are simultaneously constructed. The backtracking path is identified in the path matrix, and the optimal result is predicted according to the path length. Conclusions: Upon comparison with the existing SimTree algorithm through experimental analysis, the proposed method showed higher accuracy and could ensure that the structural information is not easily lost in terms of improved specificity, sensitivity, and the Matthews correlation coefficient.

A NON-PARAMETRIC BAYESIAN APPROACH FOR PREDICTING RNA SECONDARY STRUCTURES

2010 ◽  
Vol 08 (04) ◽  
pp. 727-742 ◽  
Author(s):  
KENGO SATO ◽  
MICHIAKI HAMADA ◽  
TOUTAI MITUYAMA ◽  
KIYOSHI ASAI ◽  
YASUBUMI SAKAKIBARA
Keyword(s):  
Secondary Structure ◽  
Bayesian Approach ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Secondary Structures ◽  
Generative Models ◽  
Rna Secondary Structures ◽  
Stochastic Context Free Grammars ◽  
Non Parametric

Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Here non-parametric means that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.

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