MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features

RNA modifications play crucial roles in various biological processes and diseases. Accurate prediction of RNA modification sites is essential for understanding their functions. In this study, we propose a hybrid approach that fuses a pre-trained sequence representation with various sequence features to predict multiple types of RNA modifications in one combined prediction framework. We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. MRM-BERT was evaluated on multiple datasets of 12 commonly occurring RNA modifications, including m6A, m5C, m1A and so on. The results demonstrate that our hybrid model outperforms other models in terms of area under receiver operating characteristic curve (AUC) for all 12 types of RNA modifications. MRM-BERT is available as an online tool (http://117.122.208.21:8501) or source code (https://github.com/abhhba999/MRM-BERT), which allows users to predict RNA modification sites and visualize the results. Overall, our study provides an effective and efficient approach to predict multiple RNA modifications, contributing to the understanding of RNA biology and the development of therapeutic strategies.

Accurately identifying hemagglutinin using sequence information and machine learning methods

Introduction

Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA.

Methods

In this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm.

Results and discussion

The model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA.

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome

Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively rely on DNA sequence information for silencer identification fail to account for the cell specificity of silencers, resulting in diminished accuracy. Despite the discovery of several transcription factors and epigenetic modifications associated with silencers on the genome, there is still no definitive biological signal or combination thereof to fully characterize silencers, posing challenges in selecting suitable biological signals for their identification. Therefore, we propose a sophisticated deep learning framework called DeepICSH, which is based on multiple biological data sources. Specifically, DeepICSH leverages a deep convolutional neural network to automatically capture biologically relevant signal combinations strongly associated with silencers, originating from a diverse array of biological signals. Furthermore, the utilization of attention mechanisms facilitates the scoring and visualization of these signal combinations, whereas the employment of skip connections facilitates the fusion of multilevel sequence features and signal combinations, thereby empowering the accurate identification of silencers within specific cells. Extensive experiments on HepG2 and K562 cell line data sets demonstrate that DeepICSH outperforms state-of-the-art methods in silencer identification. Notably, we introduce for the first time a deep learning framework based on multi-omics data for classifying strong and weak silencers, achieving favorable performance. In conclusion, DeepICSH shows great promise for advancing the study and analysis of silencers in complex diseases. The source code is available at https://github.com/lyli1013/DeepICSH.

Automatic Pavement Crack Detection Transformer Based on Convolutional and Sequential Feature Fusion

To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and proposed an efficient pavement crack detection method. Firstly, the scalable Swin-Transformer network and the residual network are used as two parallel channels of the backbone network to extract the long-sequence global features and the underlying visual local features of the pavement cracks, respectively, which are concatenated and fused to enrich the extracted feature information. Then, the encoder and decoder of the transformer detection framework are optimized; the location and category information of the pavement cracks can be obtained directly using the set prediction, which provided a low-code method to reduce the implementation complexity. The research result shows that the highest AP (Average Precision) of this method reaches 45.8% on the COCO dataset, which is significantly higher than that of DETR and its variants model Conditional DETR where the AP values are 36.9% and 42.8%, respectively. On the self-collected pavement crack dataset, the AP of the proposed method reaches 45.6%, which is 3.8% higher than that of Mask R-CNN (Region-based Convolution Neural Network) and 8.8% higher than that of Faster R-CNN. Therefore, this method is an efficient pavement crack detection algorithm.

XGEM: Predicting Essential miRNAs by the Ensembles of Various Sequence-Based Classifiers With XGBoost Algorithm

MicroRNAs (miRNAs) play vital roles in gene expression regulations. Identification of essential miRNAs is of fundamental importance in understanding their cellular functions. Experimental methods for identifying essential miRNAs are always costly and time-consuming. Therefore, computational methods are considered as alternative approaches. Currently, only a handful of studies are focused on predicting essential miRNAs. In this work, we proposed to predict essential miRNAs using the XGBoost framework with CART (Classification and Regression Trees) on various types of sequence-based features. We named this method as XGEM (XGBoost for essential miRNAs). The prediction performance of XGEM is promising. In comparison with other state-of-the-art methods, XGEM performed the best, indicating its potential in identifying essential miRNAs.

How do I get the most out of my protein sequence using bioinformatics tools?

Biochemical and biophysical experiments are essential for uncovering the three-dimensional structure and biological role of a protein of interest. However, meaningful predictions can frequently also be made using bioinformatics resources that transfer knowledge from a well studied protein to an uncharacterized protein based on their evolutionary relatedness. These predictions are helpful in developing specific hypotheses to guide wet-laboratory experiments. Commonly used bioinformatics resources include methods to identify and predict conserved sequence motifs, protein domains, transmembrane segments, signal sequences, and secondary as well as tertiary structure. Here, several such methods available through the MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) are described and how their combined use can provide meaningful information on a protein of unknown function is demonstrated. In particular, the identification of homologs of known structure using HHpred, internal repeats using HHrepID, coiled coils using PCOILS and DeepCoil, and transmembrane segments using Quick2D are focused on.

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.

iCysMod: an integrative database for protein cysteine modifications in eukaryotes

As important post-translational modifications, protein cysteine modifications (PCMs) occurring at cysteine thiol group play critical roles in the regulation of various biological processes in eukaryotes. Due to the rapid advancement of high-throughput proteomics technologies, a large number of PCM events have been identified but remain to be curated. Thus, an integrated resource of eukaryotic PCMs will be useful for the research community. In this work, we developed an integrative database for protein cysteine modifications in eukaryotes (iCysMod), which curated and hosted 108 030 PCM events for 85 747 experimentally identified sites on 31 483 proteins from 48 eukaryotes for 8 types of PCMs, including oxidation, S-nitrosylation (-SNO), S-glutathionylation (-SSG), disulfide formation (-SSR), S-sulfhydration (-SSH), S-sulfenylation (-SOH), S-sulfinylation (-SO2H) and S-palmitoylation (-S-palm). Then, browse and search options were provided for accessing the dataset, while various detailed information about the PCM events was well organized for visualization. With human dataset in iCysMod, the sequence features around the cysteine modification sites for each PCM type were analyzed, and the results indicated that various types of PCMs presented distinct sequence recognition preferences. Moreover, different PCMs can crosstalk with each other to synergistically orchestrate specific biological processes, and 37 841 PCM events involved in 119 types of PCM co-occurrences at the same cysteine residues were finally obtained. Taken together, we anticipate that the database of iCysMod would provide a useful resource for eukaryotic PCMs to facilitate related researches, while the online service is freely available at http://icysmod.omicsbio.info.

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

WITMSG: Large-scale Prediction of Human Intronic m6A RNA Methylation Sites from Sequence and Genomic Features

INTRODUCTION

N 6-methyladenosine (m6A) is one of the most widely studied epigenetic modifications. It plays important roles in various biological processes, such as splicing, RNA localization and degradation, many of which are related to the functions of introns. Although a number of computational approaches have been proposed to predict the m6A sites in different species, none of them were optimized for intronic m6A sites. As existing experimental data overwhelmingly relied on polyA selection in sample preparation and the intronic RNAs are usually underrepresented in the captured RNA library, the accuracy of general m6A sites prediction approaches is limited for intronic m6A sites prediction task.

METHODOLOGY

A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m6A RNA methylation sites in humans has been proposed here for the first time. Based on the random forest algorithm and using only known intronic m6A sites as the training data, WITMSG takes advantage of both conventional sequence features and a variety of genomic characteristics for improved prediction performance of intron-specific m6A sites.

RESULTS AND CONCLUSION

It has been observed that WITMSG outperformed competing approaches (trained with all the m6A sites or intronic m6A sites only) in 10-fold cross-validation (AUC: 0.940) and when tested on independent datasets (AUC: 0.946). WITMSG was also applied intronome-wide in humans to predict all possible intronic m6A sites, and the prediction results are freely accessible at http://rnamd.com/intron/.

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine

Antioxidant proteins play important roles in countering oxidative damage in organisms. Because it is time-consuming and has a high cost, the accurate identification of antioxidant proteins using biological experiments is a challenging task. For these reasons, we proposed a model using machine-learning algorithms that we named AOPs-SVM, which was developed based on sequence features and a support vector machine. Using a testing dataset, we conducted a jackknife cross-validation test with the proposed AOPs-SVM classifier and obtained 0.68 in sensitivity, 0.985 in specificity, 0.942 in average accuracy, 0.741 in MCC, and 0.832 in AUC. This outperformed existing classifiers. The experiment results demonstrate that the AOPs-SVM is an effective classifier and contributes to the research related to antioxidant proteins. A web server was built at http://server.malab.cn/AOPs-SVM/index.jsp to provide open access.