scholarly journals Fine-grained parallel RNAalifold algorithm for RNA secondary structure prediction on FPGA

2009 ◽  
Vol 10 (S1) ◽  
Author(s):  
Fei Xia ◽  
Yong Dou ◽  
Xingming Zhou ◽  
Xuejun Yang ◽  
Jiaqing Xu ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Rna Secondary Structure Prediction ◽  
Fine Grained

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]

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

2018 ◽  
Vol 16 (06) ◽  
pp. 1840025 ◽  
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

A popular approach for predicting RNA secondary structure is the thermodynamic nearest-neighbor model that finds a thermodynamically most stable secondary structure with 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 a model has been reported. 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. Our fine-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 [Formula: see text] 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. The implementation of our algorithm is available at https://github.com/keio-bioinformatics/mxfold .

FINE-GRAINED PARALLEL APPLICATION SPECIFIC COMPUTING FOR RNA SECONDARY STRUCTURE PREDICTION ON FPGA

2014 ◽  
Vol 23 (03) ◽  
pp. 1450031
Author(s):  
QIANGHUA ZHU ◽  
FEI XIA ◽  
GUOQING JIN
Keyword(s):  
Dynamic Programming ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Secondary Structure Prediction ◽  
Data Reuse ◽  
Data Dependency ◽  
Rna Secondary Structure Prediction ◽  
Fine Grained

RNA secondary structure prediction is one of the important research areas in modern bioinformatics and computational biology. PKNOTS is the most famous benchmark program and has been widely used to predict RNA secondary structure including pseudoknots. It adopts the standard 4D dynamic programming method and is the basis of many variants and improved algorithms. Unfortunately, the O(N6) computing requirements and complicated data dependency greatly limits the usefulness of PKNOTS package with the explosion in gene database size. In this paper, we present a fine-grained parallel PKNOTS algorithm and prototype system for accelerating RNA folding application on field programmable gate-array (FPGA) platform. We improved data locality by converting cycle nested relationship and reorganizing computing order of the elements in source code. We aggressively exploit data reuse, data dependency elimination and memory access scheduling strategies to minimize the need for loading data from external memory. To the best of our knowledge, our design is the first FPGA implementation for accelerating 4D dynamic programming problem for RNA folding application including pseudoknots. The experimental results show a factor of more than 11 × average speedup over the PKNOTS-1.05 software running on a PC platform with AMD Phenom 9650 Quad CPU for input RNA sequences. However, the power consumption of our FPGA accelerator is only about 50% of the general-purpose micro-processors.

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