EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features

Jia, Cangzhi; He, Wenying

doi:10.1038/srep38741

Cited by 92 publications

(75 citation statements)

References 47 publications

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“…iEnhancer-EL [70] and iEnhancer-2L [26] produced better outcomes using ensemble classifiers and achieved accuracy of 78.03% and 76.89% respectively in which they were successful in predicting strong enhancers with accuracy of 65.03% and 61.93% respectively. Whereas EnhancerPred [27] achieved 80.82% accuracy and used SVMs which produced slightly better results in predicting strong enhancers with 62.06% accuracy. Similarly, iEnhancer-2L-Hybrid [71]and iEnhancer-5Step [29] improved the accuracy results with their prediction model and acquired 77.86% and 82.3% accuracies respectively with identifying the strong enhancers with 65.83% and 68.1% accuracies respectively.…”

Section: Resultsmentioning

confidence: 99%

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EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

Ah¹,

Alkhalaf

Iqbal

et al. 2020

Preprint

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Enhancers are DNA fragments that do not encode RNA molecules and proteins, but they act critically in the production of RNAs and proteins by controlling gene expression. Prediction of enhancers and their strength plays significant role in regulating gene expression. Prediction of enhancer regions, in sequences of DNA, is considered a difficult task due to the fact that they are not close to the target gene, have less common motifs and are mostly tissue/cell specific. In recent past, several bioinformatics tools were developed to discriminate enhancers from other regulatory elements and to identify their strengths as well.However the need for improvement in the quality of its prediction method requires enhancements in its application value practically. In this study, we proposed a new method that builds on nucleotide composition and statistical moment based features to distinguish between enhancers and non-enhancers and additionally determine their strength. Our proposed method achieved accuracy better than current state-of-the-art methods using 5-fold and 10-fold cross-validation. The outcomes from our proposed method suggest that the use of statistical moments based features could bear more efficient and effective results. For the accessibility of the scientific community, we have developed a user-friendly web server for EnhancerP-2L which will increase the impact of bioinformatics on medicinal chemistry and drive medical science into an unprecedented resolution. Web server is freely accessible at http://www.biopred.org/enpred.

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Section: Resultsmentioning

confidence: 99%

“…Furthermore, many other methods, such as EnhancerPred [27] and EnhancerPred_2.0 [28], were introduced to improve the performance by incorporating other features based on DNA sequences. iEnhancer-5Step [29] was recently developed using the hidden information of DNA sequences infused with Support Vector Machine (SVM) based predictions.…”

Section: Introductionmentioning

confidence: 99%

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

Ah¹,

Alkhalaf

Iqbal

et al. 2020

Preprint

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“…In order to distinguish the contribution of different features to the prediction model. To analyze these feature vectors, F-score method (Chen W. et al, 2016 ; Jia and He, 2016 ; Tang et al, 2016 , 2018 ; He and Jia, 2017 ) was adopted to rank the feature, in this study. The F-score value of the i -th feature is defined as:

where

and

are the average values of the i -th feature in whole, ncDNA and cDNA datasets, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…It has been proven to be powerful in many fields of pattern recognition and data classification (Byun and Lee, 2002 ; Nasrabadi, 2007 ; Zhang N. et al, 2018 ;). More and more applications also proved that SVM also has strong data processing capabilities in the fields of bioinformatics (Xiong et al, 2011 ; Jia et al, 2013 , 2017 ; Cao et al, 2014 ; Liu et al, 2014 , 2017b ; Wei et al, 2015 ; Chen X. X. et al, 2016 ; Jia and He, 2016 ; Yang et al, 2016 ; Zou et al, 2016 ; Xiao et al, 2017 ; Qiao et al, 2018 ; Su et al, 2018 ). A set of ncDNA samples and cDNA samples were represented by the feature vectors.…”

Section: Methodsmentioning

confidence: 99%

Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA in Saccharomyces cerevisiae

Zeng

et al. 2018

Front. Microbiol.

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With the rapid development of high-speed sequencing technologies and the implementation of many whole genome sequencing project, research in the genomics is advancing from genome sequencing to genome synthesis. Synthetic biology technologies such as DNA-based molecular assemblies, genome editing technology, directional evolution technology and DNA storage technology, and other cutting-edge technologies emerge in succession. Especially the rapid growth and development of DNA assembly technology may greatly push forward the success of artificial life. Meanwhile, DNA assembly technology needs a large number of target sequences of known information as data support. Non-coding DNA (ncDNA) sequences occupy most of the organism genomes, thus accurate recognizing of them is necessary. Although experimental methods have been proposed to detect ncDNA sequences, they are expensive for performing genome wide detections. Thus, it is necessary to develop machine-learning methods for predicting non-coding DNA sequences. In this study, we collected the ncDNA benchmark dataset of Saccharomyces cerevisiae and reported a support vector machine-based predictor, called Sc-ncDNAPred, for predicting ncDNA sequences. The optimal feature extraction strategy was selected from a group included mononucleotide, dimer, trimer, tetramer, pentamer, and hexamer, using support vector machine learning method. Sc-ncDNAPred achieved an overall accuracy of 0.98. For the convenience of users, an online web-server has been built at: http://server.malab.cn/Sc_ncDNAPred/index.jsp.

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“…Definitive epigenetic signatures of enhancer elements have been challenging to identify. A number of computational tools have been developed to predict enhancer elements from chromatin state and transcription factor in vivo DNA binding information (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Tools that attempt to measure predictive accuracy using only indirect evidence of enhancer activity, e.g.…”

Section: Introductionmentioning

confidence: 99%

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

Arbel

Fisher

Hammonds

et al. 2018

Preprint

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Identifying functional enhancers elements in metazoan systems is a major challenge.For example, large-scale validation of enhancers predicted by ENCODE reveal false positive rates of at least 70%. Here we use the pregrastrula patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held out data results from heterogeneity of functional signatures in enhancer elements. We show that two classes of enhancer are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, over 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well predicted elements is composed predominantly of enhancers driving multi-stage, segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome, 916 of which are novel. An analysis of 32 novel SDEs using wholemount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.. CC-BY-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/250241 doi: bioRxiv preprint first posted online Jan. 18, 2018; 3 Significance StatementWe demonstrate a high accuracy method for predicting enhancers genome wide with > 85% precision as validated by transgenic reporter assays in Drosophila embryos. This is the first time such accuracy has been achieved in a metazoan system, allowing us to predict with highconfidence 1640 enhancers, 916 of which are novel. The predicted enhancers are demarcated by heterogeneous collections of epigenetic marks; many strong enhancers are free from classical indicators of activity, including H3K27ac, but are bound by key transcription factors.H3K27ac, often used as a one-dimensional predictor of enhancer activity, is an uninformative parameter in our data.

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EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features

Cited by 92 publications

References 47 publications

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA in Saccharomyces cerevisiae

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

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