iDNA-MS: An Integrated Computational Tool for Detecting DNA Modification Sites in Multiple Genomes

Multi-scale DNA language model improves 6 mA binding sites prediction

DNA methylation at the N6 position of adenine (N6-methyladenine, 6 mA), which refers to the attachment of a methyl group to the N6 site of the adenine (A) of DNA, is an important epigenetic modification in prokaryotic and eukaryotic genomes. Accurately predicting the 6 mA binding sites can provide crucial insights into gene regulation, DNA repair, disease development and so on. Wet experiments are commonly used for analyzing 6 mA binding sites. However, they suffer from high cost and expensive time. Therefore, various deep learning methods have been widely used to predict 6 mA binding sites recently. In this study, we develop a framework based on multi-scale DNA language model named "iDNA6mA-MDL". "iDNA6mA-MDL" integrates multiple kmers and the nucleotide property and frequency method for feature embedding, which can capture a full range of DNA sequence context information. At the prediction stage, it also leverages DNABERT to compensate for the incomplete capture of global DNA information. Experiments show that our framework obtains average AUC of 0.981 on a classic 6 mA rice gene dataset, going beyond all existing advanced models under fivefold cross-validations. Moreover, "iDNA6mA-MDL" outperforms most of the popular state-of-the-art methods on another 11 6 mA datasets, demonstrating its effectiveness in 6 mA binding sites prediction.

The markers of the predictive DNA test for canine hip dysplasia may have a stronger relationship with elbow dysplasia

Canine hip and elbow dysplasias, which are prevalent orthopedic conditions rooted in developmental and hereditary factors are yet to be comprehensively assessed. This study aimed to address this gap by exploring the prognostic significance of five markers linked to canine hip dysplasia using available genome-wide association studies (GWAS) data. The influence of these markers on both hip and elbow dysplasia was examined in dogs exposed to standardized environmental conditions. We made a groundbreaking discovery using custom primers, qPCR assays, and evaluation of fluorescent resonance energy transfer (FRET) probes. Three specific SNPs previously associated with the risk of canine hip dysplasia demonstrated a potentially stronger correlation with elbow dysplasia. Notably, the SNP at nucleotide position 22691322, located near the canine CHST3 gene, displayed significance as a marker in multivariable logistic regression analysis. Surprisingly, none of the initially targeted SNPs showed a direct association with hip dysplasia. The genomic positions of these SNPs reside within a region conserved across mammals. In silico analyses suggested that the relevant variant might be positioned in a region linked to bone and muscle structures. Our findings revealed a remarkable relationship between SNP2 genotypes and methylation patterns, shedding light on the underlying mechanism that partially explains the genotype-phenotype correlation in canine CHST3. These groundbreaking findings offer essential insights for future, more extensive investigations into canine orthopedic health. This research significantly contributes to our understanding of the molecular foundations of hip and elbow dysplasia in dogs by charting a course for advancements in veterinary medicine and the overall well-being of canine companions.

DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism

As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a specialized deep learning framework called DeepPGD. DNA methylation, a crucial biological modification, plays a vital role in gene expression analyses, cellular differentiation, and the study of disease progression. However, accurately and efficiently identifying DNA methylation sites remains a pivotal concern in the field of bioinformatics. The issue addressed in this paper is the presence of methylation in DNA, which is a binary classification problem. To address this, our research aimed to develop a deep learning algorithm capable of more precisely identifying these sites. The DeepPGD framework combined a dual residual structure involving Temporal convolutional networks (TCNs) and bidirectional long short-term memory (BiLSTM) networks to effectively extract intricate DNA structural and sequence features. Additionally, to meet the practical requirements of DNA methylation identification, extensive experiments were conducted across a variety of biological species. The experimental results highlighted DeepPGD's exceptional performance across multiple evaluation metrics, including accuracy, Matthews' correlation coefficient (MCC), and the area under the curve (AUC). In comparison to other algorithms in the same domain, DeepPGD demonstrated superior classification and predictive capabilities across various biological species datasets. This significant advancement in algorithmic prowess not only offers substantial technical support, but also holds potential for research and practical implementation within the DNA methylation identification domain. Moreover, the DeepPGD framework shows potential for application in genomics research, biomedicine, and disease diagnostics, among other fields.

iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation

Introduction: DNA methylation is a critical epigenetic modification involving the addition of a methyl group to the DNA molecule, playing a key role in regulating gene expression without changing the DNA sequence. The main difficulty in identifying DNA methylation sites lies in the subtle and complex nature of methylation patterns, which may vary across different tissues, developmental stages, and environmental conditions. Traditional methods for methylation site identification, such as bisulfite sequencing, are typically labor-intensive, costly, and require large amounts of DNA, hindering high-throughput analysis. Moreover, these methods may not always provide the resolution needed to detect methylation at specific sites, especially in genomic regions that are rich in repetitive sequences or have low levels of methylation. Furthermore, current deep learning approaches generally lack sufficient accuracy. Methods: This study introduces the iDNA-OpenPrompt model, leveraging the novel OpenPrompt learning framework. The model combines a prompt template, prompt verbalizer, and Pre-trained Language Model (PLM) to construct the prompt-learning framework for DNA methylation sequences. Moreover, a DNA vocabulary library, BERT tokenizer, and specific label words are also introduced into the model to enable accurate identification of DNA methylation sites. Results and Discussion: An extensive analysis is conducted to evaluate the predictive, reliability, and consistency capabilities of the iDNA-OpenPrompt model. The experimental outcomes, covering 17 benchmark datasets that include various species and three DNA methylation modifications (4mC, 5hmC, 6mA), consistently indicate that our model surpasses outstanding performance and robustness approaches.

PSAC-6mA: 6mA site identifier using self-attention capsule network based on sequence-positioning

DNA N6-methyladenine (6mA) modifications play a pivotal role in the regulation of growth, development, and diseases in organisms. As a significant epigenetic marker, 6mA modifications extensively participate in the intricate regulatory networks of the genome. Hence, gaining a profound understanding of how 6mA is intricately involved in these biological processes is imperative for deciphering the gene regulatory networks within organisms. In this study, we propose PSAC-6mA (Position-self-attention Capsule-6mA), a sequence-location-based self-attention capsule network. The positional layer in the model enables positional relationship extraction and independent parameter setting for each base position, avoiding parameter sharing inherent in convolutional approaches. Simultaneously, the self-attention capsule network enhances dimensionality, capturing correlation information between capsules and achieving exceptional results in feature extraction across multiple spatial dimensions within the model. Experimental results demonstrate the superior performance of PSAC-6mA in recognizing 6mA motifs across various species.

4mCPred-GSIMP: Predicting DNA N4-methylcytosine sites in the mouse genome with multi-Scale adaptive features extraction and fusion

The epigenetic modification of DNA N4-methylcytosine (4mC) is vital for controlling DNA replication and expression. It is crucial to pinpoint 4mC's location to comprehend its role in physiological and pathological processes. However, accurate 4mC detection is difficult to achieve due to technical constraints. In this paper, we propose a deep learning-based approach 4mCPred-GSIMP for predicting 4mC sites in the mouse genome. The approach encodes DNA sequences using four feature encoding methods and combines multi-scale convolution and improved selective kernel convolution to adaptively extract and fuse features from different scales, thereby improving feature representation and optimization effect. In addition, we also use convolutional residual connections, global response normalization and pointwise convolution techniques to optimize the model. On the independent test dataset, 4mCPred-GSIMP shows high sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the curve, which are 0.7812, 0.9312, 0.8562, 0.7207 and 0.9233, respectively. Various experiments demonstrate that 4mCPred-GSIMP outperforms existing prediction tools.

Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction

BACKGROUND

DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for understanding and exploring gene regulatory mechanisms. Currently, machine learning algorithms are primarily used for model construction. However, several challenges remain to be addressed, including limited prediction accuracy, constrained generalization capability, and insufficient learning capacity.

RESULTS

In response to the aforementioned challenges, this paper leverages the similarities between DNA sequences and time series to introduce a time series-based hybrid ensemble learning model, called Multi2-Con-CAPSO-LSTM. The model utilizes multivariate and multidimensional encoding approach, combining three types of time series encodings with three kinds of genetic feature encodings, resulting in a total of nine types of feature encoding matrices. Convolutional Neural Networks are utilized to extract features from DNA sequences, including temporal, positional, physicochemical, and genetic information, thereby creating a comprehensive feature matrix. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm for predicting DNA methylation.

CONCLUSIONS

Through cross-validation experiments conducted on 17 species involving three types of DNA methylation (6 mA, 5hmC, and 4mC), the results demonstrate the robust predictive capabilities of the Multi2-Con-CAPSO-LSTM model in DNA methylation prediction across various types and species. Compared with other benchmark models, the Multi2-Con-CAPSO-LSTM model demonstrates significant advantages in sensitivity, specificity, accuracy, and correlation. The model proposed in this paper provides valuable insights and inspiration across various disciplines, including sequence alignment, genetic evolution, time series analysis, and structure-activity relationships.

BERT-5mC: an interpretable model for predicting 5-methylcytosine sites of DNA based on BERT

DNA 5-methylcytosine (5mC) is widely present in multicellular eukaryotes, which plays important roles in various developmental and physiological processes and a wide range of human diseases. Thus, it is essential to accurately detect the 5mC sites. Although current sequencing technologies can map genome-wide 5mC sites, these experimental methods are both costly and time-consuming. To achieve a fast and accurate prediction of 5mC sites, we propose a new computational approach, BERT-5mC. First, we pre-trained a domain-specific BERT (bidirectional encoder representations from transformers) model by using human promoter sequences as language corpus. BERT is a deep two-way language representation model based on Transformer. Second, we fine-tuned the domain-specific BERT model based on the 5mC training dataset to build the model. The cross-validation results show that our model achieves an AUROC of 0.966 which is higher than other state-of-the-art methods such as iPromoter-5mC, 5mC_Pred, and BiLSTM-5mC. Furthermore, our model was evaluated on the independent test set, which shows that our model achieves an AUROC of 0.966 that is also higher than other state-of-the-art methods. Moreover, we analyzed the attention weights generated by BERT to identify a number of nucleotide distributions that are closely associated with 5mC modifications. To facilitate the use of our model, we built a webserver which can be freely accessed at: http://5mc-pred.zhulab.org.cn.

StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning

BACKGROUND

DNA methylation, instrumental in numerous life processes, underscores the paramount importance of its accurate prediction. Recent studies suggest that deep learning, due to its capacity to extract profound insights, provides a more precise DNA methylation prediction. However, issues related to the stability and generalization performance of these models persist.

RESULTS

In this study, we introduce an efficient and stable DNA methylation prediction model. This model incorporates a feature fusion approach, adaptive feature correction technology, and a contrastive learning strategy. The proposed model presents several advantages. First, DNA sequences are encoded at four levels to comprehensively capture intricate information across multi-scale and low-span features. Second, we design a sequence-specific feature correction module that adaptively adjusts the weights of sequence features. This improvement enhances the model's stability and scalability, or its generality. Third, our contrastive learning strategy mitigates the instability issues resulting from sparse data. To validate our model, we conducted multiple sets of experiments on commonly used datasets, demonstrating the model's robustness and stability. Simultaneously, we amalgamate various datasets into a single, unified dataset. The experimental outcomes from this combined dataset substantiate the model's robust adaptability.

CONCLUSIONS

Our research findings affirm that the StableDNAm model is a general, stable, and effective instrument for DNA methylation prediction. It holds substantial promise for providing invaluable assistance in future methylation-related research and analyses.

MethSemble-6mA: an ensemble-based 6mA prediction server and its application on promoter region of LBD gene family in Poaceae

The Lateral Organ Boundaries Domain (LBD) containing genes are a set of plant-specific transcription factors and are crucial for controlling both organ development and defense mechanisms as well as anthocyanin synthesis and nitrogen metabolism. It is imperative to understand how methylation regulates gene expression, through predicting methylation sites of their promoters particularly in major crop species. In this study, we developed a user-friendly prediction server for accurate prediction of 6mA sites by incorporating a robust feature set, viz., Binary Encoding of Mono-nucleotide DNA. Our model,MethSemble-6mA, outperformed other state-of-the-art tools in terms of accuracy (93.12%). Furthermore, we investigated the pattern of probable 6mA sites at the upstream promoter regions of the LBD-containing genes in Triticum aestivum and its allied species using the developed tool. On average, each selected species had four 6mA sites, and it was found that with speciation and due course of evolution in wheat, the frequency of methylation have reduced, and a few sites remain conserved. This obviously cues gene birth and gene expression alteration through methylation over time in a species and reflects functional conservation throughout evolution. Since DNA methylation is a vital event in almost all plant developmental processes (e.g., genomic imprinting and gametogenesis) along with other life processes, our findings on epigenetic regulation of LBD-containing genes have dynamic implications in basic and applied research. Additionally, MethSemble-6mA (http://cabgrid.res.in:5799/) will serve as a useful resource for a plant breeders who are interested to pursue epigenetic-based crop improvement research.

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.

CapsNetYY1: identifying YY1-mediated chromatin loops based on a capsule network architecture

BACKGROUND

Previous studies have identified that chromosome structure plays a very important role in gene control. The transcription factor Yin Yang 1 (YY1), a multifunctional DNA binding protein, could form a dimer to mediate chromatin loops and active enhancer-promoter interactions. The deletion of YY1 or point mutations at the YY1 binding sites significantly inhibit the enhancer-promoter interactions and affect gene expression. To date, only a few computational methods are available for identifying YY1-mediated chromatin loops.

RESULTS

We proposed a novel model named CapsNetYY1, which was based on capsule network architecture to identify whether a pair of YY1 motifs can form a chromatin loop. Firstly, we encode the DNA sequence using one-hot encoding method. Secondly, multi-scale convolution layer is used to extract local features of the sequence, and bidirectional gated recurrent unit is used to learn the features across time steps. Finally, capsule networks (convolution capsule layer and digital capsule layer) used to extract higher level features and recognize YY1-mediated chromatin loops. Compared with DeepYY1, the only prediction for YY1-mediated chromatin loops, our model CapsNetYY1 achieved the better performance on the independent datasets (AUC [Formula: see text]).

CONCLUSION

The results indicate that CapsNetYY1 is an excellent method for identifying YY1-mediated chromatin loops. We believe that the CapsNetYY1 method will be used for predictive classification of other DNA sequences.

Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network

DNA methylation takes on critical significance to the regulation of gene expression by affecting the stability of DNA and changing the structure of chromosomes. DNA methylation modification sites should be identified, which lays a solid basis for gaining more insights into their biological functions. Existing machine learning-based methods of predicting DNA methylation have not fully exploited the hidden multidimensional information in DNA gene sequences, such that the prediction accuracy of models is significantly limited. Besides, most models have been built in terms of a single methylation type. To address the above-mentioned issues, a deep learning-based method was proposed in this study for DNA methylation site prediction, termed the MEDCNN model. The MEDCNN model is capable of extracting feature information from gene sequences in three dimensions (i.e., positional information, biological information, and chemical information). Moreover, the proposed method employs a convolutional neural network model with double convolutional layers and double fully connected layers while iteratively updating the gradient descent algorithm using the cross-entropy loss function to increase the prediction accuracy of the model. Besides, the MEDCNN model can predict different types of DNA methylation sites. As indicated by the experimental results,the deep learning method based on coding from multiple dimensions outperformed single coding methods, and the MEDCNN model was highly applicable and outperformed existing models in predicting DNA methylation between different species. As revealed by the above-described findings, the MEDCNN model can be effective in predicting DNA methylation sites.

PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence

Promoters are DNA non-coding regions around the transcription start site and are responsible for regulating the gene transcription process. Due to their key role in gene function and transcriptional activity, the prediction of promoter sequences and their core elements accurately is a crucial research area in bioinformatics. At present, models based on machine learning and deep learning have been developed for promoter prediction. However, these models cannot mine the deeper biological information of promoter sequences and consider the complex relationship among promoter sequences. In this work, we propose a novel prediction model called PromGER to predict eukaryotic promoter sequences. For a promoter sequence, firstly, PromGER utilizes four types of feature-encoding methods to extract local information within promoter sequences. Secondly, according to the potential relationships among promoter sequences, the whole promoter sequences are constructed as a graph. Furthermore, three different scales of graph-embedding methods are applied for obtaining the global feature information more comprehensively in the graph. Finally, combining local features with global features of sequences, PromGER analyzes and predicts promoter sequences through a tree-based ensemble-learning framework. Compared with seven existing methods, PromGER improved the average specificity of 13%, accuracy of 10%, Matthew's correlation coefficient of 16%, precision of 4%, F1 score of 6%, and AUC of 9%. Specifically, this study interpreted the PromGER by the t-distributed stochastic neighbor embedding (t-SNE) method and SHAPley Additive exPlanations (SHAP) value analysis, which demonstrates the interpretability of the model.

EpiTEAmDNA: Sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species

Methylation is a major DNA epigenetic modification for regulating the biological processes without altering the DNA sequence, and multiple types of DNA methylations have been discovered, including 6mA, 5hmC, and 4mC. Multiple computational approaches were developed to automatically identify the DNA methylation residues using machine learning or deep learning algorithms. The machine learning (ML) based methods are difficult to be transferred to the other predicting tasks of the DNA methylation sites using additional knowledge. Deep learning (DL) may facilitate the transfer learning of knowledge from similar tasks, but they are often ineffective on small datasets. This study proposes an integrated feature representation framework EpiTEAmDNA based on the strategies of transfer learning and ensemble learning, which is evaluated on multiple DNA methylation types across 15 species. EpiTEAmDNA integrates convolutional neural network (CNN) and conventional machine learning methods, and shows improved performances than the existing DL-based methods on small datasets when no additional knowledge is available. The experimental data suggests that the EpiTEAmDNA models may be further improved via transfer learning based on additional knowledge. The evaluation experiments on the independent test datasets also suggest that the proposed EpiTEAmDNA framework outperforms the existing models in most prediction tasks of the 3 DNA methylation types across 15 species. The source code, pre-trained global model, and the EpiTEAmDNA feature representation framework are freely available at http://www.healthinformaticslab.org/supp/.

4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies

N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana, C. elegans, D. melanogaster, E. coli, G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT.

DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method

Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/.

CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction

N6-methyladenine (6mA) plays a critical role in various epigenetic processing including DNA replication, DNA repair, silencing, transcription, and diseases such as cancer. To understand such epigenetic mechanisms, 6 mA has been detected by high-throughput technologies on a genome-wide scale at single-base resolution, together with conventional methods such as immunoprecipitation, mass spectrometry and capillary electrophoresis, but these experimental approaches are time-consuming and laborious. To complement these problems, we have developed a CNN-based 6 mA site predictor, named CNN6mA, which proposed two new architectures: a position-specific 1-D convolutional layer and a cross-interactive network. In the position-specific 1-D convolutional layer, position-specific filters with different window sizes were applied to an inquiry sequence instead of sharing the same filters over all positions in order to extract the position-specific features at different levels. The cross-interactive network explored the relationships between all the nucleotide patterns within the inquiry sequence. Consequently, CNN6mA outperformed the existing state-of-the-art models in many species and created the contribution score vector that intelligibly interpret the prediction mechanism. The source codes and web application in CNN6mA are freely accessible at https://github.com/kuratahiroyuki/CNN6mA.git and http://kurata35.bio.kyutech.ac.jp/CNN6mA/, respectively.

MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction

Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism, and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation, and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep learning framework for predicting DNA methylation sites, which is based on 5 popular transformer-based language models. The framework identifies methylation sites for 3 different types of DNA methylation: N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the "pretrain and fine-tune" paradigm. Pretraining is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA methylation status of each type. The 5 models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source, and we provide a web server that implements the approach.

Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning

N6-methyladenosine (m6A) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA m6A transcriptome-wide at base resolution via experimental approaches, which are generally time-consuming and expensive. Developing computational methods is a good strategy for accurate in silico detection of m6A modification sites from the large amount of RNA sequence data. Unfortunately, the existing computational models are usually only for m6A site prediction in a single species, without considering the tissue level of species, while most of them are constructed based on low-confidence level data generated by an m6A antibody immunoprecipitation (IP)-based sequencing method, thereby restricting reliability and generalizability of proposed models. Here, we review recent advances in computational prediction of m6A sites and construct a new computational approach named im6APred using ensemble deep learning to accurately identify m6A sites based on high-confidence level data in multiple tissues of mammals. Our model im6APred builds upon a comprehensive evaluation of multiple classification methods, including four traditional classification algorithms and three deep learning methods and their ensembles. The optimal base-classifier combinations are then chosen by five-fold cross-validation test to achieve an effective stacked model. Our model im6APred can produce the area under the receiver operating characteristic curve (AUROC) in the range of 0.82-0.91 on independent tests, indicating that our model has the ability to learn general methylation rules on RNA bases and generalize to m6A transcriptome-wide identification. Moreover, AUROCs in the range of 0.77-0.96 were achieved using cross-species/tissues validation on the benchmark dataset, demonstrating differences in predictive performance at the tissue level and the need for constructing tissue-specific models for m6A site prediction.

iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.

TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer

Recent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated source data become more popular than unsupervised clustering algorithms. However, accurate cellular annotation of single cell transcription data remains a significant challenge. Here, we propose a hybrid network structure called TransCluster, which uses linear discriminant analysis and a modified Transformer to enhance feature learning. It is a cell-type identification tool for single-cell transcriptomic maps. It shows high accuracy and robustness in many cell data sets of different human tissues. It is superior to other known methods in external test data set. To our knowledge, TransCluster is the first attempt to use Transformer for annotating cell types of scRNA-seq, which greatly improves the accuracy of cell-type identification.

ISTRF: Identification of sucrose transporter using random forest

Sucrose transporter (SUT) is a type of transmembrane protein that exists widely in plants and plays a significant role in the transportation of sucrose and the specific signal sensing process of sucrose. Therefore, identifying sucrose transporter is significant to the study of seed development and plant flowering and growth. In this study, a random forest-based model named ISTRF was proposed to identify sucrose transporter. First, a database containing 382 SUT proteins and 911 non-SUT proteins was constructed based on the UniProt and PFAM databases. Second, k-separated-bigrams-PSSM was exploited to represent protein sequence. Third, to overcome the influence of imbalance of samples on identification performance, the Borderline-SMOTE algorithm was used to overcome the shortcoming of imbalance training data. Finally, the random forest algorithm was used to train the identification model. It was proved by 10-fold cross-validation results that k-separated-bigrams-PSSM was the most distinguishable feature for identifying sucrose transporters. The Borderline-SMOTE algorithm can improve the performance of the identification model. Furthermore, random forest was superior to other classifiers on almost all indicators. Compared with other identification models, ISTRF has the best general performance and makes great improvements in identifying sucrose transporter proteins.

Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction

BACKGROUND

DNA N4-methylcytosine is part of the restrictive modification system, which works by regulating some biological processes, for example, the initiation of DNA replication, mismatch repair and inactivation of transposon. However, using experimental methods to detect 4mC sites is time-consuming and expensive. Besides, considering the huge differences in the number of 4mC samples among different species, it is challenging to achieve a robust multi-species 4mC site prediction performance. Hence, it is of great significance to develop effective computational tools to identify 4mC sites.

RESULTS

This work proposes a flexible deep learning-based framework to predict 4mC sites, called Hyb4mC. Hyb4mC adopts the DNA2vec method for sequence embedding, which captures more efficient and comprehensive information compared with the sequence-based feature method. Then, two different subnets are used for further analysis: Hyb_Caps and Hyb_Conv. Hyb_Caps is composed of a capsule neural network and can generalize from fewer samples. Hyb_Conv combines the attention mechanism with a text convolutional neural network for further feature learning.

CONCLUSIONS

Extensive benchmark tests have shown that Hyb4mC can significantly enhance the performance of predicting 4mC sites compared with the recently proposed methods.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning

DNA N⁴-methylcytosine (4mC) is a pivotal epigenetic modification that plays an essential role in DNA replication, repair, expression and differentiation. To gain insight into the biological functions of 4mC, it is critical to identify their modification sites in the genomics. Recently, deep learning has become increasingly popular in recent years and frequently employed for the 4mC site identification. However, a systematic analysis of how to build predictive models using deep learning techniques is still lacking. In this work, we first summarized all existing deep learning-based predictors and systematically analyzed their models, features and datasets, etc. Then, using a typical standard dataset with three species (A. thaliana, C. elegans, and D. melanogaster), we assessed the contribution of different model architectures, encoding methods and the attention mechanism in establishing a deep learning-based model for the 4mC site prediction. After a series of optimizations, convolutional-recurrent neural network architecture using the one-hot encoding and attention mechanism achieved the best overall prediction performance. Extensive comparison experiments were conducted based on the same dataset. This work will be helpful for researchers who would like to build the 4mC prediction models using deep learning in the future.

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.

BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches

N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are costly and time-consuming. To detect the 6mA and complement these shortcomings of experimental methods, we proposed a novel, deep leaning approach called BERT6mA. To compare the BERT6mA with other deep learning approaches, we used the benchmark datasets including 11 species. The BERT6mA presented the highest AUCs in eight species in independent tests. Furthermore, BERT6mA showed higher and comparable performance with the state-of-the-art models while the BERT6mA showed poor performances in a few species with a small sample size. To overcome this issue, pretraining and fine-tuning between two species were applied to the BERT6mA. The pretrained and fine-tuned models on specific species presented higher performances than other models even for the species with a small sample size. In addition to the prediction, we analyzed the attention weights generated by BERT6mA to reveal how the BERT6mA model extracts critical features responsible for the 6mA prediction. To facilitate biological sciences, the BERT6mA online web server and its source codes are freely accessible at https://github.com/kuratahiroyuki/BERT6mA.git, respectively.

Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique

4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in Geobacter pickeringii. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.

DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species

DNA N4-methylcytosine (4mC) being a significant genetic modification holds a dominant role in controlling different biological functions, i.e., DNA replication, DNA repair, gene regulations and gene expression levels. The identification of 4mC sites is important to get insight information regarding different organics mechanisms. However, getting modification prediction from experimental methods is a challenging task due to high expenses and time-consuming techniques. Therefore, computational tools can be a great option for modification identification. Various computational tools are proposed in literature but their generalization and prediction performance require improvement. For this motive, we have proposed a neural network based tool named DCNN-4mC for identifying 4mC sites. The proposed model involves a set of neural network layers with a skip connection which allows to share the shallow features with dense layers. Skip connection have allowed to gather crucial information regarding 4mC sites. In literature, different models are employed on different species hence in many cases different datasets are available for a single species. In this research, we have combined all available datasets to create a single benchmark dataset for every species. To the best of our knowledge, no model in literature is employed on more than six different species. To ensure the generalizability of DCNN-4mC we have used 12 different species for performance evaluation. The DCNN-4mC tool has attained 2% to 14% higher accuracy than state-of-the-art tools on all available datasets of different species. Furthermore, independent test datasets are also engaged and DCNN-4mC have overall yielded high performance in them as well.

The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite

Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.

iDNA-ABT : advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization

MOTIVATION

DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, the identification of DNA methylation sites is critical for better understanding and revealing their functional mechanisms. To date, several machine learning and deep learning methods have been developed for the prediction of different methylation types. However, they still highly rely on manual features, which can largely limit the high-latent information extraction. Moreover, most of them are designed for one specific methylation type, and therefore cannot predict multiple methylation sites in multiple species simultaneously. In this study, we propose iDNA-ABT, an advanced deep learning model that utilizes adaptive embedding based on bidirectional transformers for language understanding together with a novel transductive information maximization (TIM) loss.

RESULTS

Benchmark results show that our proposed iDNA-ABT can automatically and adaptively learn the distinguishing features of biological sequences from multiple species, and thus perform significantly better than the state-of-the-art methods in predicting three different DNA methylation. In addition, TIM loss is proven to be effective in dichotomous tasks via the comparison experiment. Furthermore, we verify that our features have strong adaptability and robustness to different species through comparison of adaptive embedding and six handcrafted feature encodings. Importantly, our model shows great generalization ability in different species, demonstrating that our model can adaptively capture the cross-species differences and improve the predictive performance. For the convenient use of our method, we further established an online webserver as the implementation of the proposed iDNA-ABT.

AVAILABILITY

our proposed iDNA-ABT, which is now freely accessible via http://server.wei-group.net/iDNA_ABT and our source codes are available in the GitHub repository (https://github.com/YUYING07/iDNA_ABT).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A deep learning approach to automate whole-genome prediction of diverse epigenomic modifications in plants

Epigenetic modifications function in gene transcription, RNA metabolism, and other biological processes. However, multiple factors currently limit the scientific utility of epigenomic datasets generated for plants. Here, using deep-learning approaches, we developed a Smart Model for Epigenetics in Plants (SMEP) to predict six types of epigenomic modifications: DNA 5-methylcytosine (5mC) and N6-methyladenosine (6mA) methylation, RNA N6-methyladenosine (m⁶ A) methylation, and three types of histone modification. Using the datasets from the japonica rice Nipponbare, SMEP achieved 95% prediction accuracy for 6mA, and also achieved around 80% for 5mC, m⁶ A, and the three types of histone modification based on the 10-fold cross-validation. Additionally, > 95% of the 6mA peaks detected after a heat-shock treatment were predicted. We also successfully applied the SMEP for examining epigenomic modifications in indica rice 93-11 and even the B73 maize line. Taken together, we show that the deep-learning-enabled SMEP can reliably mine epigenomic datasets from diverse plants to yield actionable insights about epigenomic sites. Thus, our work opens new avenues for the application of predictive tools to facilitate functional research, and will almost certainly increase the efficiency of genome engineering efforts.

4mC-RF: Improving the prediction of 4mC sites using composition and position relative features and statistical moment

N4-methylcytosine (4 mC) is an important epigenetic modification that occurs enzymatically by the action of DNA methyltransferases. 4 mC sites exist in prokaryotes and eukaryotes while playing a vital role in regulating gene expression, DNA replication, and cell cycle. The efficient and accurate prediction of 4 mC sites has a significant role in the insight of 4 mC biological properties and functions. Therefore, a sequence-based predictor is proposed, namely 4 mC-RF, for identifying 4 mC sites through the integration of statistical moments along with position, and composition-dependent features. Relative and absolute position-based features are computed to extract optimal features. A popular machine learning classifier Random Forest was used for training the model. Validation results were obtained through rigorous processes of self-consistency, 10-fold cross-validation, Independent set testing, and Jackknife yielding 95.1%, 95.2%, 97.0%, and 94.7% accuracies, respectively. Our proposed model depicts the highest prediction accuracies as compared to existing models. Subsequently, the developed 4 mC-RF model was constructed into a web server. A significant and more accurate predictor of 4 mC Methylcytosine sites helps experimental scientists to gather faster, efficient, and cost-effective results.

Recent development of machine learning methods in sumoylation sites prediction

Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico also attracted much attention for its accuracy, convenience and speed. At present, many computational prediction models have been used to identify SUMO sites, but these contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We will briefly summarize the development of bioinformatics methods on sumoylation site prediction. We will mainly focus on the benchmark dataset construction, feature extraction, machine learning method, published results and online tools. We hope the review will provide more help for wet-experimental scholars.

scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network

Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes or RNA-seq profiles, resulting in poor extrapolation. Still, the accurate cell-type annotation for single-cell transcriptomic data remains a great challenge. Here, we introduce scDeepSort (https://github.com/ZJUFanLab/scDeepSort), a pre-trained cell-type annotation tool for single-cell transcriptomics that uses a deep learning model with a weighted graph neural network (GNN). Using human and mouse scRNA-seq data resources, we demonstrate the high performance and robustness of scDeepSort in labeling 764 741 cells involving 56 human and 32 mouse tissues. Significantly, scDeepSort outperformed other known methods in annotating 76 external test datasets, reaching an 83.79% accuracy across 265 489 cells in humans and mice. Moreover, we demonstrate the universality of scDeepSort using more challenging datasets and using references from different scRNA-seq technology. Above all, scDeepSort is the first attempt to annotate cell types of scRNA-seq data with a pre-trained GNN model, which can realize the accurate cell-type annotation without additional references, i.e. markers or RNA-seq profiles.

Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli

N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.

Identification of gene signature associated with type 2 diabetes mellitus by integrating mutation and expression data

Type 2 diabetes mellitus (T2DM) is a chronic disease. The molecular diagnosis should be helpful for the treatment of T2DM patients. With the development of sequencing technology, a large number of differentially expressed genes were identified from expression data. However, the method of machine learning can only identify the local optimal solution as the signature. The mutation information obtained by inheritance can better reflect the relationship between genes and diseases. Therefore, we need to integrate mutation information to more accurately identify the signature. To this end, we integrated genome-wide association study (GWAS) data and expression data, combined with expression quantitative trait loci (eQTL) technology to get T2DM predictive signature (T2DMSig-10). Firstly, we used GWAS data to obtain a list of T2DM susceptible loci. Then, we used eQTL technology to obtain risk single nucleotide polymorphisms (SNPs), and combined with the pancreatic β-cells gene expression data to obtain 10 protein-coding genes. Next, we combined these genes with equal weights. After receiver operating characteristic (ROC), single-gene removal and increase method, gene ontology function enrichment and protein-protein interaction network were used to verify the results that showed that T2DMSig-10 had an excellent predictive effect on T2DM (AUC=0.99), and was highly robust. In short, we obtained the predictive signature of T2DM, and further verified it.

i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning

As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) plays a crucial role in controlling gene replication, expression, cell cycle, DNA replication, and differentiation. The accurate identification of 4mC sites is necessary to understand biological functions. In the paper, we use ensemble learning to develop a model named i4mC-EL to identify 4mC sites in the mouse genome. Firstly, a multifeature encoding scheme consisting of Kmer and EIIP was adopted to describe the DNA sequences. Secondly, on the basis of the multifeature encoding scheme, we developed a stacked ensemble model, in which four machine learning algorithms, namely, BayesNet, NaiveBayes, LibSVM, and Voted Perceptron, were utilized to implement an ensemble of base classifiers that produce intermediate results as input of the metaclassifier, Logistic. The experimental results on the independent test dataset demonstrate that the overall rate of predictive accurate of i4mC-EL is 82.19%, which is better than the existing methods. The user-friendly website implementing i4mC-EL can be accessed freely at the following.

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning

Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.

4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism

DNA methylation is one of the most extensive epigenetic modifications. DNA 4mC modification plays a key role in regulating chromatin structure and gene expression. In this study, we proposed a generic 4mC computational predictor, namely, 4mCPred-MTL using multi-task learning coupled with Transformer to predict 4mC sites in multiple species. In this predictor, we utilize a multi-task learning framework, in which each task is to train species-specific data based on Transformer. Extensive experimental results show that our multi-task predictive model can significantly improve the performance of the model based on single task and outperform existing methods on benchmarking comparison. Moreover, we found that our model can sufficiently capture better characteristics of 4mC sites as compared to existing commonly used feature descriptors, demonstrating the strong feature learning ability of our model. Therefore, based on the above results, it can be expected that our 4mCPred-MTL can be a useful tool for research communities of interest.

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.

Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method

N4-methylcytosine (4mC) is a kind of DNA modification which could regulate multiple biological processes. Correctly identifying 4mC sites in genomic sequences can provide precise knowledge about their genetic roles. This study aimed to develop an ensemble model to predict 4mC sites in the mouse genome. In the proposed model, DNA sequences were encoded by k-mer, enhanced nucleic acid composition and composition of k-spaced nucleic acid pairs. Subsequently, these features were optimized by using minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) and five-fold cross-validation. The obtained optimal features were inputted into random forest classifier for discriminating 4mC from non-4mC sites in mouse. On the independent dataset, our model could yield the overall accuracy of 85.41%, which was approximately 3.8% -6.3% higher than the two existing models, i4mC-Mouse and 4mCpred-EL respectively. The data and source code of the model can be freely download from https://github.com/linDing-groups/model_4mc.

iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool

Motivation

DNA N4-methylcytosine (4mC) and N6-methyladenine (6mA) are two important DNA modifications and play crucial roles in a variety of biological processes. Accurate identification of the modifications is essential to better understand their biological functions and mechanisms. However, existing methods to identify 4mA or 6mC sites are all single tasks, which demonstrates that they can identify only a certain modification in one species. Therefore, it is desirable to develop a novel computational method to identify the modification sites in multiple species simultaneously.

Results

In this study, we proposed a computational method, called iDNA-MT, to identify 4mC sites and 6mA sites in multiple species, respectively. The proposed iDNA-MT mainly employed multi-task learning coupled with the bidirectional gated recurrent units (BGRU) to capture the sharing information among different species directly from DNA primary sequences. Experimental comparative results on two benchmark datasets, containing different species respectively, show that either for identifying 4mA or for 6mC site in multiple species, the proposed iDNA-MT outperforms other state-of-the-art single-task methods. The promising results have demonstrated that iDNA-MT has great potential to be a powerful and practically useful tool to accurately identify DNA modifications.

Identification of long noncoding RNAs with machine learning methods: a review

Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.