EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron–ion interaction potential feature selection

He, Wenying; Jia, Cangzhi

doi:10.1039/c7mb00054e

Cited by 41 publications

(28 citation statements)

References 52 publications

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“…The recently proposed position-specific theory has been successfully applied to numerous fields of bioinformatics, such as identifying protein S-nitrosylation sites [ 47 ], hydroxyproline and hydroxylysine sites [ 48 ], DNA replication origin sites [ 49 ], Enhancer [ 41 ], etc. Besides, a series of studies have shown that the trinucleotides composition was effective in recognition of promoter [ 28 , 50 – 52 ].…”

Section: Methodsmentioning

confidence: 99%

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70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

Jia

Duan

et al. 2018

BMC Syst Biol

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BackgroundPromoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ70 promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It’s also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification of σ70 promoter regions in DNA sequence is not easy.ResultsIn order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNPSS combined with 64 features of PseEIIP obtained the best performance for sigma70 promoter identification, with a promising accuracy and the Matthews correlation coefficient (MCC) at 95.56% and 0.90, respectively.ConclusionThe jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0570-1) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

“…There are 4 3 = 64trinucleotides: AAA, AAC, AAG, ..., TTT. So, for an 81 bp sample, its details of the trinucleotides position specificity can be expressed by the following 64 × 79 matrix [ 41 ]: where the variable …”

Section: Methodsmentioning

confidence: 99%

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

Jia

Duan

et al. 2018

BMC Syst Biol

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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%

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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“…Position-specific triple-nucleotide propensity describes the differences in nucleotide composition at each position between the sequences with and without 6mA modification. As a statistics-based feature extraction method, PSTNP has been used to address multiple molecular biological problems, including DNA N 4 -methylcytosine (4mC) site prediction [22], enhancer prediction [42], and σ70 promoter predictor [43]. Two subtypes of PSTNP were used in this study, i.e., single-stranded and double-stranded (PSTNP SS and PSTNP DS , respectively).…”

Section: Pstnp Featuresmentioning

confidence: 99%

Identification of DNA N6-methyladenine sites by integration of sequence features

Wang

Xiao

et al. 2020

Epigenetics & Chromatin

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Background: An increasing number of nucleic acid modifications have been profiled with the development of sequencing technologies. DNA N 6-methyladenine (6mA), which is a prevalent epigenetic modification, plays important roles in a series of biological processes. So far, identification of DNA 6mA relies primarily on time-consuming and expensive experimental approaches. However, in silico methods can be implemented to conduct preliminary screening to save experimental resources and time, especially given the rapid accumulation of sequencing data. Results: In this study, we constructed a 6mA predictor, p6mA, from a series of sequence-based features, including physicochemical properties, position-specific triple-nucleotide propensity (PSTNP), and electron-ion interaction pseudopotential (EIIP). We performed maximum relevance maximum distance (MRMD) analysis to select key features and used the Extreme Gradient Boosting (XGBoost) algorithm to build our predictor. Results demonstrated that p6mA outperformed other existing predictors using different datasets. Conclusions: p6mA can predict the methylation status of DNA adenines, using only sequence files. It may be used as a tool to help the study of 6mA distribution pattern. Users can download it from https ://githu b.com/Kongl ab404 / p6mA.

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EnhancerPred2.0: predicting enhancers and their strength based on position-specific trinucleotide propensity and electron–ion interaction potential feature selection

Cited by 41 publications

References 52 publications

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

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

Identification of DNA N6-methyladenine sites by integration of sequence features

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