scholarly journals miR-Explore: Predicting MicroRNA Precursors by Class Grouping and Secondary Structure Positional Alignment

2013 ◽  
Vol 7 ◽  
pp. BBI.S10758 ◽  
Author(s):  
Bram Sebastian ◽  
Samuel E. Aggrey
Keyword(s):  
Secondary Structure ◽  
Comparative Method ◽  
High Specificity ◽  
Computational Prediction ◽  
Training Data ◽  
Structure Alignment ◽  
Global Alignment ◽  
Gene Expressions ◽  
Small Noncoding Rnas ◽  
Prediction Approach

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expressions by targeting the mRNAs especially in the 3′UTR regions. The identification of miRNAs has been done by biological experiment and computational prediction. The computational prediction approach has been done using two major methods: comparative and noncomparative. The comparative method is dependent on the conservation of the miRNA sequences and secondary structure. The noncomparative method, on the other hand, does not rely on conservation. We hypothesized that each miRNA class has its own unique set of features; therefore, grouping miRNA by classes before using them as training data will improve sensitivity and specificity. The average sensitivity was 88.62% for miR-Explore, which relies on within miRNA class alignment, and 70.82% for miR-abela, which relies on global alignment. Compared with global alignment, grouping miRNA by classes yields a better sensitivity with very high specificity for pre-miRNA prediction even when a simple positional based secondary and primary structure alignment are used.

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

2017 ◽  
Author(s):  
Manato Akiyama ◽  
Kengo Sato ◽  
Yasubumi Sakakibara
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Prediction Accuracy ◽  
Secondary Structure Prediction ◽  
Training Data ◽  
Support Vector ◽  
Rna Secondary Structure Prediction ◽  
Fine Grained

AbstractMotivation: A popular approach for predicting RNA secondary structure is the thermodynamic nearest neighbor model that finds a thermodynamically most stable secondary structure with the minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a machine learning based fine-grained model achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such model has been reported.Results: In this paper, we propose a novel algorithm for RNA secondary structure prediction that integrates the thermodynamic approach and the machine learning based weighted approach. Ourfine-grained model combines the experimentally determined thermodynamic parameters with a large number of scoring parameters for detailed contexts of features that are trained by the structured support vector machine (SSVM) with the ℓ1 regularization to avoid overfitting. Our benchmark shows that our algorithm achieves the best prediction accuracy compared with existing methods, and heavy overfitting cannot be observed.Availability: The implementation of our algorithm is available at https://github.com/keio-bioinformatics/mxfold.Contact:[email protected]

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

scholarly journals Analyzing Secondary Structure Patterns in DNA Aptamers Identified via CompELS

Molecules ◽  
2019 ◽  
Vol 24 (8) ◽  
pp. 1572 ◽  
Author(s):  
Richard Sullivan ◽  
Mary Catherine Adams ◽  
Rajesh R. Naik ◽  
Valeria T. Milam
Keyword(s):  
Secondary Structure ◽  
Dna Sequences ◽  
High Throughput Sequencing ◽  
Large Population ◽  
Binding Activity ◽  
Global Alignment ◽  
Dna Aptamers ◽  
Secondary Structure Analysis ◽  
Screening Platform ◽  
Analytical Tools

In contrast to sophisticated high-throughput sequencing tools for genomic DNA, analytical tools for comparing secondary structure features between multiple single-stranded DNA sequences are less developed. For single-stranded nucleic acid ligands called aptamers, secondary structure is widely thought to play a pivotal role in driving recognition-based binding activity between an aptamer sequence and its specific target. Here, we employ a competition-based aptamer screening platform called CompELS to identify DNA aptamers for a colloidal target. We then analyze predicted secondary structures of the aptamers and a large population of random sequences to identify sequence features and patterns. Our secondary structure analysis identifies patterns ranging from position-dependent score matrixes of individual structural elements to position-independent consensus domains resulting from global alignment.

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