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.</p

The comparison of baseline expression.

<p>(A) Comparison of earliest expression stage between down_l versus down_h (left) and up_l versus up_h (right). (B) The correlation between tissue expression specificity and up/down-regulation number. The correlation curve is plotted by using the LOESS smoothing techniques and the shade indicates the confidence interval.</p

Forest plot comparing the changes in cardiorespiratory fitness between the exercise and control groups.

<p>Forest plot comparing the changes in cardiorespiratory fitness between the exercise and control groups.</p

The ROC curves measuring the discriminative capability of the ubiquitination site indicators.

<p>The indicators include the sequence pattern, the structural propensities (local conformation, residue propensities in the microenvironment, accessibility and centrality) and their combination. For combination, individual indicators were combined by a weighted summing scheme (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083167#pone.0083167.s009" target="_blank">Table S2</a> for the weights). The AUC values were calculated according to the structural propensities, the likelihood scores derived via five-fold cross-validation of the corresponding models or their combinations (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083167#pone.0083167.s010" target="_blank">Text S1</a> for details). The larger the AUC value, the stronger the indicator.</p

Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data

<div><p>Gene expression is perturbated by drugs to different extent. Analyzing genes whose expression is frequently regulated by drugs would be useful for the screening of candidate therapeutic targets and genes implicated in side effect. Here, we obtained the differential expression number (DEN) for genes profiled in Affymetrix microarrays from the Connectivity Map project, and conducted systemic comparative computational analysis between high DEN genes and other genes. Results indicated that genes with higher down-/up-regulation number (down_h/up_h) tended to be clustered in genome, and have lower homologous gene number, higher SNP density and more disease-related SNP. Down_h and up_h were significantly enriched in cancer related pathways, while genes with lower down-/up-regulation number (down_l/up_l) were mainly involved in the development of nervous system diseases. Besides, up_h had lower interaction network degree, later developmental stage to express, higher tissue expression specificity than up_l, while down_h showed reversed tendency in comparison with down_l. Together, our analysis suggests that genes frequently regulated by drugs are more likely to be associated with disease-related functions, but the extensive activation of conserved and widely expressed genes by drugs is disfavored.</p></div

Interaction network degree analysis.

<p>(A) Comparison of degree in PPI network of down_l versus down_h (left) and up_l versus up_h (right). (B) The correlation between degree and up/down-regulation number. The correlation curve is plotted by using the LOESS smoothing techniques and the shade indicates the confidence interval.</p

The overall view of the analysis.

<p>(A) The pipeline for the calculation of DEN of every gene from the CMAP dataset and the following computational analysis. (B) The distribution of down-regulation number (left) and up-regulation number (right) among the analyzed genes.</p

Forest plot comparing the changes in muscle strength between the exercise and control groups.

<p>Forest plot comparing the changes in muscle strength between the exercise and control groups.</p

Risk of bias summary.

<p>Risk of bias summary.</p

Basic Characteristics of the Included Studies.

<p>Basic Characteristics of the Included Studies.</p