iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule

Tahir, Muhammad; Tayara, Hilal; Chong, Kil To

doi:10.1016/j.chemolab.2019.04.007

Cited by 82 publications

(60 citation statements)

References 84 publications

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“…(6) iDNA6mA-PseKNC: A sequence based predictor that enables identification of DNA 6mA sites with 100% specificity and 96% accuracy without going for complex mathematical formulae [88]. 7iDNA 6mA: Identification of 6mA sites in the rice genome using deep learning method based on conventional neural network, which takes single input of DNA sequences [89]. (8) i6mA-Pred: Identification of 6mA sites in the rice genome with 83% accuracy in which the DNA sequences were formulated and encoded effectively by the use of chemical property and frequency of nucleotide based on support vector machine method [90].…”

Section: Bioinformatic Analysis Tools For 6mamentioning

confidence: 99%

DNA N6-Methyladenine Modification in Plant Genomes—A Glimpse into Emerging Epigenetic Code

Karanthamalai

Chodon

Chauhan

et al. 2020

Plants

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N6-methyladenine (6mA) is a DNA base modification at the 6th nitrogen position; recently, it has been resurfaced as a potential reversible epigenetic mark in eukaryotes. Despite its existence, 6mA was considered to be absent due to its undetectable level. However, with the new advancements in methods, considerable 6mA distribution is identified across the plant genome. Unlike 5-methylcytosine (5mC) in the gene promoter, 6mA does not have a definitive role in repression but is exposed to have divergent regulation in gene expression. Though 6mA information is less known, the available evidences suggest its function in plant development, tissue differentiation, and regulations in gene expression. The current review article emphasizes the research advances in DNA 6mA modifications, identification, available databases, analysis tools and its significance in plant development, cellular functions and future perspectives of research.

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Section: Bioinformatic Analysis Tools For 6mamentioning

confidence: 99%

DNA N6-Methyladenine Modification in Plant Genomes—A Glimpse into Emerging Epigenetic Code

Karanthamalai

Chodon

Chauhan

et al. 2020

Plants

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“…They used Maximum Relevance Maximum Distance (MRMD) method ( [14]) along with Incremental Feature Selection (IFS) for limiting feature space. This same dataset was used to train and build iDNA6mA tool which utilized one hot matrix as sample feature in [15]. They used sequential one dimensional convolutional neural network architecture for classification.…”

Section: Introductionmentioning

confidence: 99%

A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome

Rahman

Amin

Shatabda

et al. 2020

Preprint

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ABSTRACTDNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification and is responsible for many biological functions. Experimental methods for genome wide 6mA site detection is an expensive and manual labour intensive process. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves area under the receiver operating characteristic curve of 0.98 with an overall accuracy of 0.94 using 5 fold cross validation on benchmark dataset. Finally, we evaluate our model on two other plant genome 6mA site identification datasets besides rice. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. Supplementary data (benchmark dataset, independent test dataset, comparison purpose dataset, trained model, physicochemical property values, attention mechanism details for motif finding) are available at Nucleic Acids Research online. Web tool for this research can be found here.

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“…However, MM-6mAPred uses a manual feature extraction method which only achieved an accuracy of 89.72% on the rice genome. iDNA6mA [25] based on convolutional neural networks uses automatic feature extraction to predict 6mA sites in rice genome. However, it achieved only an accuracy of 86.64% on the rice benchmark dataset.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Predicting DNA N6-Methyladenine (6mA) Sites in Eukaryotes

Roland

Wannige

2020

IEEE Access

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DNA N6-methyladenine (6mA) is an epigenetic modification, which is involved in many biological regulation processes like DNA replication, DNA repair, transcription, and gene expression regulation. The widespread presence of this 6mA modification in eukaryotes has been unclear until recently. Studying the genome-wide distribution of 6mA can provide a deeper understanding of the epigenetic modification process and the biological processes it involves. Existing experimental techniques are time-consuming and computational machine learning methods have room for performance improvement. DNA N6-methyladenine prediction in eukaryotic cross-species shows low performance. Hence, there is a need for a more accurate, time-efficient method to predict the distribution of 6mA sites in eukaryotes. Since deep learning architectures have shown higher accuracy, we develop a customized VGG16 architecture-based model named 6mAVGG using convolution neural networks for the prediction of DNA 6mA sites in eukaryotes. We introduce a novel 3-dimensional encoding mechanism extending the one-hot encoding method to support the input of the VGG16 model. Specifically, the 10-fold crossvalidation on the benchmark datasets for the proposed model achieves higher accuracies of 98.01%, 97.44%, 99.56% respectively for cross-species, Rice, and M. musculus genomes. The proposed model outperforms existing tools for the prediction of 6mA sites and has enhanced accuracies by 2.88%, 4.2%, 0.9% respectively for cross-species, Rice, and M. musculus genomes compared to the state of the art method SNNRice6mA. The model trained with benchmark data predicts 6mA sites of other species ArabidopsisThaliana, RosaChinensis, Drosophila, and Yeast with prediction accuracy over 70%. Thus, this model can be used for the genome-wide prediction of 6mA sites in eukaryotes.

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iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule

Cited by 82 publications

References 84 publications

DNA N6-Methyladenine Modification in Plant Genomes—A Glimpse into Emerging Epigenetic Code

DNA N6-Methyladenine Modification in Plant Genomes—A Glimpse into Emerging Epigenetic Code

A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome

A Deep Learning Model for Predicting DNA N6-Methyladenine (6mA) Sites in Eukaryotes

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