iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach

Liu, Bin; Li, Kai; Huang, De-Shuang; Chou, Kuo‐Chen

doi:10.1093/bioinformatics/bty458

Cited by 184 publications

(129 citation statements)

References 79 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.…”

Section: Resultsmentioning

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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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%

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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“…A user-friendly web-server which is publically accessible, as indicated in [68] and also in a series of latest publications (see, e.g., [65,[69][70][71][72][73][74]), represents the steps ahead for developing prediction methods and computational tools which are more practical and useful. We therefore, in our future works, shall make efforts to provide a web-server for the prediction method presented in this paper.…”

Section: Comparison Of Evolstruct-phogly With the Existing Methodsmentioning

confidence: 99%

EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction

et al. 2019

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Background: Post-translational modification (PTM), which is a biological process, tends to modify proteome that leads to changes in normal cell biology and pathogenesis. In the recent times, there has been many reported PTMs. Out of the many modifications, phosphoglycerylation has become particularly the subject of interest. The experimental procedure for identification of phosphoglycerylated residues continues to be an expensive, inefficient and time-consuming effort, even with a large number of proteins that are sequenced in the post-genomic period. Computational methods are therefore being anticipated in order to effectively predict phosphoglycerylated lysines. Even though there are predictors available, the ability to detect phosphoglycerylated lysine residues still remains inadequate. Results: We have introduced a new predictor in this paper named EvolStruct-Phogly that uses structural and evolutionary information relating to amino acids to predict phosphoglycerylated lysine residues. Benchmarked data is employed containing experimentally identified phosphoglycerylated and non-phosphoglycerylated lysines. We have then extracted the three structural information which are accessible surface area of amino acids, backbone torsion angles, amino acid's local structure conformations and profile bigrams of position-specific scoring matrices.

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“…Support vector machines (SVMs) and random forest classifiers remain common approaches (e.g. Arbel et al, 2019;Chen et al, 2018;He et al, 2017;Le et al, 2019;Liu et al, 2018), although 'deep learning' approaches using artificial neural networks (ANNs) have been increasing in popularity as these methods become more mature and more feasible with current advances in computing power (e.g. Chen et al, 2018;Liu et al, 2016;Min et al, 2017;Yang et al, 2017).…”

Section: Computational Enhancer Prediction Through Integration Of Mulmentioning

confidence: 99%

“…Therefore, approaches that rely solely on genome sequence are likely to be the most appealing to researchers that use non-traditional insect models. A number of these are available, most of which fall into the 'specific' enhancer discovery class Kazemian and Halfon, 2019;Le et al, 2019;Liu et al, 2018). In general, these approaches deconstruct the training sequences into a set of small (e.g.…”

Section: Enhancer Prediction Independent Of Experimentally Derived Fementioning

confidence: 99%

How to study enhancers in non-traditional insect models

Tomoyasu

Halfon

2020

Journal of Experimental Biology

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Transcriptional enhancers are central to the function and evolution of genes and gene regulation. At the organismal level, enhancers play a crucial role in coordinating tissue-and context-dependent gene expression. At the population level, changes in enhancers are thought to be a major driving force that facilitates evolution of diverse traits. An amazing array of diverse traits seen in insect morphology, physiology and behavior has been the subject of research for centuries. Although enhancer studies in insects outside of Drosophila have been limited, recent advances in functional genomic approaches have begun to make such studies possible in an increasing selection of insect species. Here, instead of comprehensively reviewing currently available technologies for enhancer studies in established model organisms such as Drosophila, we focus on a subset of computational and experimental approaches that are likely applicable to non-Drosophila insects, and discuss the pros and cons of each approach. We discuss the importance of validating enhancer function and evaluate several possible validation methods, such as reporter assays and genome editing. Key points and potential pitfalls when establishing a reporter assay system in non-traditional insect models are also discussed. We close with a discussion of how to advance enhancer studies in insects, both by improving computational approaches and by expanding the genetic toolbox in various insects. Through these discussions, this Review provides a conceptual framework for studying the function and evolution of enhancers in non-traditional insect models.

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iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 184 publications

References 79 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

EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction

How to study enhancers in non-traditional insect models

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