Dataset Information

Sequence based prediction of enhancer regions from DNA random walk.

ABSTRACT: Regulatory elements play a critical role in development process of eukaryotic organisms by controlling the spatio-temporal pattern of gene expression. Enhancer is one of these elements which contributes to the regulation of gene expression through chromatin loop or eRNA expression. Experimental identification of a novel enhancer is a costly exercise, due to which there is an interest in computational approaches to predict enhancer regions in a genome. Existing computational approaches to achieve this goal have primarily been based on training of high-throughput data such as transcription factor binding sites (TFBS), DNA methylation, and histone modification marks etc. On the other hand, purely sequence based approaches to predict enhancer regions are promising as they are not biased by the complexity or context specificity of such datasets. In sequence based approaches, machine learning models are either directly trained on sequences or sequence features, to classify sequences as enhancers or non-enhancers. In this paper, we derived statistical and nonlinear dynamic features along with k-mer features from experimentally validated sequences taken from Vista Enhancer Browser through random walk model and applied different machine learning based methods to predict whether an input test sequence is enhancer or not. Experimental results demonstrate the success of proposed model based on Ensemble method with area under curve (AUC) 0.86, 0.89, and 0.87 in B cells, T cells, and Natural killer cells for histone marks dataset.

SUBMITTER: Singh AP

PROVIDER: S-EPMC6206163 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Sequence based prediction of enhancer regions from DNA random walk.

Singh Anand Pratap AP Mishra Sarthak S Jabin Suraiya S

Scientific reports 20181029 1

Regulatory elements play a critical role in development process of eukaryotic organisms by controlling the spatio-temporal pattern of gene expression. Enhancer is one of these elements which contributes to the regulation of gene expression through chromatin loop or eRNA expression. Experimental identification of a novel enhancer is a costly exercise, due to which there is an interest in computational approaches to predict enhancer regions in a genome. Existing computational approaches to achieve ...[more]

PMID: 30374023

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Project description:Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.

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Dataset Information

Sequence based prediction of enhancer regions from DNA random walk.

Publications

Sequence based prediction of enhancer regions from DNA random walk.

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