csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule

PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms

DNA methylation is of crucial importance for biological genetic expression, such as biological cell differentiation and cellular tumours. The identification of DNA-6mA sites using traditional biological experimental methods requires more cumbersome steps and a large amount of time. The advent of neural network technology has facilitated the identification of 6 mA sites on cross-species DNA with enhanced efficacy. Nevertheless, the majority of contemporary neural network models for identifying 6 mA sites prioritize the design of the identification model, with comparatively limited research conducted on the statistically significant DNA sequence itself. Consequently, this paper will focus on the statistical strategy of DNA double-stranded features, utilising the multi-head self-attention mechanism in neural networks applied to DNA position probabilistic relationships. Furthermore, a new recognition model, PSATF-6 mA, will be constructed by continually adjusting the attentional tendency of feature fusion through an integrated learning framework. The experimental results, obtained through cross-validation with cross-species data, demonstrate that the PSATF-6 mA model outperforms the baseline model. The in-Matthews correlation coefficient (MCC) for the cross-species dataset of rice and m. musus genomes can reach a score of 0.982. The present model is expected to assist biologists in more accurately identifying 6 mA locus and in formulating new testable biological hypotheses.

SoftVoting6mA: An improved ensemble-based method for predicting DNA N6-methyladenine sites in cross-species genomes

The DNA N6-methyladenine (6mA) is an epigenetic modification, which plays a pivotal role in biological processes encompassing gene expression, DNA replication, repair, and recombination. Therefore, the precise identification of 6mA sites is fundamental for better understanding its function, but challenging. We proposed an improved ensemble-based method for predicting DNA N6-methyladenine sites in cross-species genomes called SoftVoting6mA. The SoftVoting6mA selected four (electron-ion-interaction pseudo potential, One-hot encoding, Kmer, and pseudo dinucleotide composition) codes from 15 types of encoding to represent DNA sequences by comparing their performances. Similarly, the SoftVoting6mA combined four learning algorithms using the soft voting strategy. The 5-fold cross-validation and the independent tests showed that SoftVoting6mA reached the state-of-the-art performance. To enhance accessibility, a user-friendly web server is provided at http://www.biolscience.cn/SoftVoting6mA/.

Improving DNA 6mA Site Prediction via Integrating Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Self-Attention Mechanism

Identifying DNA N6-methyladenine (6mA) sites is significantly important to understanding the function of DNA. Many deep learning-based methods have been developed to improve the performance of 6mA site prediction. In this study, to further improve the performance of 6mA site prediction, we propose a new meta method, called Co6mA, to integrate bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNNs), and self-attention mechanisms (SAM) via assembling two different deep learning-based models. The first model developed in this study is called CBi6mA, which is composed of CNN, BiLSTM, and fully connected modules. The second model is borrowed from LA6mA, which is an existing 6mA prediction method based on BiLSTM and SAM modules. Experimental results on two independent testing sets of different model organisms, i.e., Arabidopsis thaliana and Drosophila melanogaster, demonstrate that Co6mA can achieve an average accuracy of 91.8%, covering 89% of all 6mA samples while achieving an average Matthews correlation coefficient value (0.839), which is higher than the second-best method DeepM6A.

Biological Sequence Classification: A Review on Data and General Methods

With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (http://lab.malab.cn/~acy/BioseqData/home.html), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.

Distance-based support vector machine to predict DNA N6-methyladenine modification

Background:

DNA N6-methyladenine plays an important role in the restriction-modification system to isolate invasion from adventive DNA. The shortcomings of the high time-consumption and high costs of experimental methods have been exposed, and some computational methods have emerged. The support vector machine theory has received extensive attention in the bioinformatics field due to its solid theoretical foundation and many good characteristics.

Objective:

General machine learning methods include an important step of extracting features. The research has omitted this step and replaced with easy-to-obtain sequence distances matrix to obtain better results

Method:

First sequence alignment technology was used to achieve the similarity matrix. Then a novel transformation turned the similarity matrix into a distance matrix. Next, the similarity-distance matrix is made positive semi-definite so that it can be used in the kernel matrix. Finally, the LIBSVM software was applied to solve the support vector machine.

Results:

The five-fold cross-validation of this model on rice and mouse data has achieved excellent accuracy rates of 92.04% and 96.51%, respectively. This shows that the DB-SVM method has obvious advantages compared with traditional machine learning methods. Meanwhile this model achieved 0.943,0.982 and 0.818 accuracy,0.944, 0.982, and 0.838 Matthews correlation coefficient and 0.942, 0.982 and 0.840 F1 scores for the rice, M. musculus and cross-species genome datasets, respectively.

Conclusion:

These outcomes show that this model outperforms the iIM-CNN and csDMA in the prediction of DNA 6mA modification, which are the lastest research on DNA 6mA.

MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block

DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, researchers have developed many computational methods to predict 6mA sites and achieved good performance. However, the existing methods do not consider the occurrence mechanism of 6mA to extract features from the molecular structure. In this paper, a novel deep learning method is proposed by devising DNA molecular graph feature and residual block structure for 6mA sites prediction in rice, named MGF6mARice. Firstly, the DNA sequence is changed into a simplified molecular input line entry system (SMILES) format, which reflects chemical molecular structure. Secondly, for the molecular structure data, we construct the DNA molecular graph feature based on the principle of graph convolutional network. Then, the residual block is designed to extract higher level, distinguishable features from molecular graph features. Finally, the prediction module is used to obtain the result of whether it is a 6mA site. By means of 10-fold cross-validation, MGF6mARice outperforms the state-of-the-art approaches. Multiple experiments have shown that the molecular graph feature and residual block can promote the performance of MGF6mARice in 6mA prediction. To the best of our knowledge, it is the first time to derive a feature of DNA sequence by considering the chemical molecular structure. We hope that MGF6mARice will be helpful for researchers to analyze 6mA sites in rice.

Research progress in predicting DNA methylation modifications and the relation with human diseases

DNA methylation is an important mode of regulation in epigenetic mechanisms, and it is one of the research foci in the field of epigenetics. DNA methylation modification affects a series of biological processes, such as eukaryotic cell growth, differentiation and transformation mechanisms, by regulating gene expression. In this review, we systematically summarized the DNA methylation databases, prediction tools for DNA methylation modification, machine learning algorithms for predicting DNA methylation modification, and the relationship between DNA methylation modification and diseases such as hypertension, Alzheimer's disease, diabetic nephropathy, and cancer. An in-depth understanding of DNA methylation mechanisms can promote accurate prediction of DNA methylation modifications and the treatment and diagnosis of related diseases.

Harnessing Current Knowledge of DNA N6-Methyladenosine From Model Plants for Non-model Crops

Epigenetic modifications alter the gene activity and function by causing change in the chromosomal architecture through DNA methylation/demethylation, or histone modifications without causing any change in DNA sequence. In plants, DNA cytosine methylation (5mC) is vital for various pathways such as, gene regulation, transposon suppression, DNA repair, replication, transcription, and recombination. Thanks to recent advances in high throughput sequencing (HTS) technologies for epigenomic “Big Data” generation, accumulated studies have revealed the occurrence of another novel DNA methylation mark, N6-methyladenosine (6mA), which is highly present on gene bodies mainly activates gene expression in model plants such as eudicot Arabidopsis (Arabidopsis thaliana) and monocot rice (Oryza sativa). However, in non-model crops, the occurrence and importance of 6mA remains largely less known, with only limited reports in few species, such as Rosaceae (wild strawberry), and soybean (Glycine max). Given the aforementioned vital roles of 6mA in plants, hereinafter, we summarize the latest advances of DNA 6mA modification, and investigate the historical, known and vital functions of 6mA in plants. We also consider advanced artificial-intelligence biotechnologies that improve extraction and prediction of 6mA concepts. In this Review, we discuss the potential challenges that may hinder exploitation of 6mA, and give future goals of 6mA from model plants to non-model crops.

i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites

DNA N6-methyladenine (6 mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6 mA sites is beneficial to the development of biological genetics. Biochemical experimental methods are considered to be time-consuming and laborious. Most of the established machine learning methods have a single dataset. Although some of them have achieved cross-species prediction, their results are not satisfactory. Therefore, we designed a novel statistical model called i6mA-VC to improve the accuracy for 6 mA sites. On the one hand, kmer and binary encoding are applied to extract features, and then gradient boosting decision tree (GBDT) embedded method is applied as the feature selection strategy. On the other hand, DNA sequences are represented by vectors through the feature extraction method of ring-function-hydrogen-chemical properties (RFHCP) and the feature selection strategy of ExtraTree. After fusing the two optimal features, a voting classifier based on gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM) and multilayer perceptron classifier (MLPC) is constructed for final classification and prediction. The accuracy of Rice dataset and M.musculus dataset with five-fold cross-validation are 0.888 and 0.967, respectively. The cross-species dataset is selected as independent testing dataset, and the accuracy reaches 0.848. Through rigorous experiments, it is demonstrated that the proposed predictor is convincing and applicable. The development of i6mA-VC predictor will become an effective way for the recognition of N6-methyladenine sites, and it will also be beneficial for biological geneticists to further study gene expression and DNA modification. In addition, an accessible web-server for i6mA-VC is available from http://www.zhanglab.site/ .

6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning

With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identification in individual species. In the present study, we aimed to develop an identification method suitable for multiple species. Based on previous research, we propose a method for 6mA site recognition. Our experiments prove that the proposed 6mA-Pred method is effective for identifying 6mA sites in genes from taxa such as rice, Mus musculus, and human. A series of experimental results show that 6mA-Pred is an excellent method. We provide the source code used in the study, which can be obtained from http://39.100.246.211:5004/6mA_Pred/.

Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

DNA N6 methyladenine (6mA) is a newly recognized methylation modification in eukaryotes. It exists widely and conservatively in organisms, and its modification level changes dynamically in the whole life cycle. This study proposes an algorithm based on a deep learning framework including LSTM and CNN to predict 6mA sites. The results showed that our method could accurately predict the 6mA sites in different species, which means DNA sub-sequences containing 6mA sites among species have certain conservation. Importantly, we found that 6mA methylation in most different species is more likely to occur on the GAGG motif. In addition, we also found that 6mA is rich in the promoter’s TATA box, which may be a mechanism of regulating downstream gene expression.

Prediction of bio-sequence modifications and the associations with diseases

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.

Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition

Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identifies speech emotion using different corpora and languages. Recent cross-corpus SER research has been conducted to improve generalization. To improve the cross-corpus SER performance, we pretrained the log-mel spectrograms of the source dataset using our designed visual attention convolutional neural network (VACNN), which has a 2D CNN base model with channel- and spatial-wise visual attention modules. To train the target dataset, we extracted the feature vector using a bag of visual words (BOVW) to assist the fine-tuned model. Because visual words represent local features in the image, the BOVW helps VACNN to learn global and local features in the log-mel spectrogram by constructing a frequency histogram of visual words. The proposed method shows an overall accuracy of 83.33%, 86.92%, and 75.00% in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Berlin Database of Emotional Speech (EmoDB), and Surrey Audio-Visual Expressed Emotion (SAVEE), respectively. Experimental results on RAVDESS, EmoDB, SAVEE demonstrate improvements of 7.73%, 15.12%, and 2.34% compared to existing state-of-the-art cross-corpus SER approaches.

DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou's 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and "Combined-species" genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

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

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.

Some illuminating remarks on molecular genetics and genomics as well as drug development

Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.