i6mA-Vote: Cross-Species Identification of DNA N6-Methyladenine Sites in Plant Genomes Based on Ensemble Learning With Voting

Abstract

DNA N6-Methyladenine (6mA) is a common epigenetic modification, which plays some significant roles in the growth and development of plants. It is crucial to identify 6mA sites for elucidating the functions of 6mA. In this article, a novel model named i6mA-vote is developed to predict 6mA sites of plants. Firstly, DNA sequences were coded into six feature vectors with diverse strategies based on density, physicochemical properties, and position of nucleotides, respectively. To find the best coding strategy, the feature vectors were compared on several machine learning classifiers. The results suggested that the position of nucleotides has a significant positive effect on 6mA sites identification. Thus, the dinucleotide one-hot strategy which can describe position characteristics of nucleotides well was employed to extract DNA features in our method. Secondly, DNA sequences of Rosaceae were divided into a training dataset and a test dataset randomly. Finally, i6mA-vote was constructed by combining five different base-classifiers under a majority voting strategy and trained on the Rosaceae training dataset. The i6mA-vote was evaluated on the task of predicting 6mA sites from the genome of the Rosaceae, Rice, and Arabidopsis separately. In Rosaceae, the performances of i6mA-vote were 0.955 on accuracy (ACC), 0.909 on Matthew correlation coefficients (MCC), 0.955 on sensitivity (SN), and 0.954 on specificity (SP). Those indicators, in the order of ACC, MCC, SN, SP, were 0.882, 0.774, 0.961, and 0.803 on Rice while they were 0.798, 0.617, 0.666, and 0.929 on Arabidopsis. According to the indicators, our method was effectiveness and better than other concerned methods. The results also illustrated that i6mA-vote does not only well in 6mA sites prediction of intraspecies but also interspecies plants. Moreover, it can be seen that the specificity is distinctly lower than the sensitivity in Rice while it is just the opposite in Arabidopsis. It may be resulted from sequence similarity among Rosaceae, Rice and Arabidopsis.

I-DNAN6mA: Accurate Identification of DNA N6-Methyladenine Sites Using the Base-Pairing Map and Deep Learning

“…To further evaluate the power of I-DNAN6mA, its performance is also assessed on the independent testing data set of the Rosaceae genome, compared to eight control methods, i.e., Meta-i6mA, i6mA-Fuse, i6mA-stack, i6mA-Pred, iDNA6mA-Rice, MM-6mAPred, 6mA-Finder, and i6mA-vote . Among them, i6mA-Fuse contains both prediction models, which are renamed i6mA-Fuse_FV and i6mA-Fuse_RC, respectively, for the sake of description.…”

“… a Results computed with the fixed FPR at 0.1. b Results computed with the fixed FPR at 0.2. c Results excerpted from ref ; “-” means that the value is not given. …”

“…High-quality benchmark data sets are important to establish reliable prediction models. To fairly compare the performance of I-DNAN6mA with that of other existing methods, in this study, the benchmark data sets containing the DNA 6mA site data for four species, i.e., Arabidopsis thaliana, Drosophila melanogaster, Rice , and Rosaceae , are derived from the recently published studies. ,,− The above three species, i.e., A. thaliana, D.…”

iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers

“…Several studies have utilized the properties of integrative machine learning frameworks in generating prediction models. Recent studies on the prediction of epigenetic modifications including DNA N6- methyladenine sites across several plant species indicate the potential of machine learning algorithms across plants and animal species [ 33 , 34 ].…”

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

“…Selecting the feature encodings that are useful and autonomous is a key stage in establishing machine learning-based models (Lv et al, 2021 ; Zhang D. et al, 2021 ; Ao et al, 2022a ; Li et al, 2022a ; Ning et al, 2022 ; Teng et al, 2022 ; Wei et al, 2022 ). Representing the DNA sequences with a mathematical manifestation is very important in functional element identification.…”