scholarly journals In silico methods for co-transcriptional RNA secondary structure prediction and for investigating alternative RNA structure expression

Methods ◽  
2017 ◽  
Vol 120 ◽  
pp. 3-16 ◽  
Author(s):  
Irmtraud M. Meyer
Keyword(s):  
Secondary Structure ◽  
In Silico ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Rna Secondary Structure Prediction ◽  
In Silico Methods

scholarly journals A Novel Framework Based on ACO and PSO for RNA Secondary Structure Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Gang Wang ◽  
Wen-yi Zhang ◽  
Qiao Ning ◽  
Hui-ling Chen
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Genetic Diseases ◽  
New Drugs ◽  
Accuracy Rate ◽  
Rna Secondary Structure Prediction ◽  
Comparison Results

Prediction of RNA structure is a useful process for creating new drugs and understanding genetic diseases. In this paper, we proposed a particle swarm optimization (PSO) and ant colony optimization (ACO) based framework (PAF) for RNA secondary structure prediction. PAF consists of crucial stem searching (CSS) and global sequence building (GSB). In CSS, a modified ACO (MACO) is used to search the crucial stems, and then a set of stems are generated. In GSB, we used a modified PSO (MPSO) to construct all the stems in one sequence. We evaluated the performance of PAF on ten sequences, which have length from 122 to 1494. We also compared the performance of PAF with the results obtained from six existing well-known methods, SARNA-Predict, RnaPredict, ACRNA, PSOfold, IPSO, and mfold. The comparison results show that PAF could not only predict structures with higher accuracy rate but also find crucial stems.

New Algorithm for Predicting RNA Secondary Structure with Pseudoknots

2012 ◽  
Vol 532-533 ◽  
pp. 1796-1799 ◽  
Author(s):  
Zhen Dong Liu ◽  
Da Ming Zhu
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Experimental Results ◽  
Rna Secondary Structure Prediction ◽  
Rna Molecules ◽  
Specificity And Sensitivity

Pseudoknots are complicated and stable RNA structure. Based on the idea of iteratively forming stable stems, and the character that the stems in RNA molecules are relatively stable, an algorithm is presented to predict RNA secondary structure including pseudoknots, it is an improvement from the previously used algorithm ,the algorithm takes O(n3) time and O(n2) sapce , in predicting accuracy, it outperforms other known algorithm of RNA secondary structure prediction, its performance is tested with the RNA sub-sequences in PseudoBase. The experimental results indicate that the algorithm has good specificity and sensitivity.

scholarly journals RNA secondary structure prediction using deep learning with thermodynamic integration

2020 ◽  
Author(s):  
Kengo Sato ◽  
Manato Akiyama ◽  
Yasubumi Sakakibara
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Secondary Structures ◽  
Thermodynamic Integration ◽  
Rna Secondary Structure Prediction ◽  
Rna Secondary Structures ◽  
Non Coding Rnas

RNA secondary structure prediction is one of the key technologies for revealing the essential roles of functional non-coding RNAs. Although machine learning-based rich-parametrized models have achieved extremely high performance in terms of prediction accuracy, the risk of overfitting for such models has been reported. In this work, we propose a new algorithm for predicting RNA secondary structures that uses deep learning with thermodynamic integration, thereby enabling robust predictions. Similar to our previous work, the folding scores, which are computed by a deep neural network, are integrated with traditional thermodynamic parameters to enable robust predictions. We also propose thermodynamic regularization for training our model without overfitting it to the training data. Our algorithm (MXfold2) achieved the most robust and accurate predictions in computational experiments designed for newly discovered non-coding RNAs, with significant 2–10 % improvements over our previous algorithm (MXfold) and standard algorithms for predicting RNA secondary structures in terms of F-value.

Sign in / Sign up

Export Citation Format

Share Document