Current Bioinformatics - Volume 21, Issue 1, 2026

The MTA gene encodes a core component of m⁶A methyltransferase complex, which plays a crucial role in the post-transcriptional modification of RNA that influences many vital processes in plants. However, due to the constraint of embryonic lethality in MTA knockout mutation, the molecular function of MTA gene has yet to be comprehensively investigated.

The aim of this study is to investigate the expression and regulation of MTA in Arabidopsis.

A large-scale transcriptome and genome analysis were carried out for the expression and nsSNP (non-synonymous Single Nucleotide Polymorphism) studies. Structured-based virtual screening, molecular dynamics simulation, binding free energy calculation and m⁶A modification level assay were employed to mine and validate MTA regulators from COCONUT natural product database.

Tissue-specific expression and stress-responsive expression patterns of MTA were observed in Arabidopsis. nsSNPs from the 1,001 Arabidopsis project were not detected in the binding site of the methyl-donor substrate S-adenosylmethionine (SAM) in MTA. 10 small molecules were identified as potential regulators, among which CNP0251613 (adenosine diphosphate glucose, ADPG) was selected and validated to decrease m⁶A levels at 10 µM vs. the control in Arabidopsis.

Our results provide a new insight and chemical entity into the in-depth study of RNA m⁶A writer MTA in plants.

Echinococcosis, a parasitic disease caused by the larvae of the Echinococcus parasite, poses a serious threat to human health. Medication is an indispensable means of treatment for Echinococcosis; however, due to the less satisfactory efficacy of single drugs, identifying effective drug combinations for the treatment of Echinococcosis is essential. Yet, current predictive models for drug synergy in Echinococcosis face accuracy challenges due to data scarcity, method limitations, and insufficient feature representation.

This work aims to design an end-to-end method to predict drug synergistic combinations, which enables efficient and accurate identification of drug combinations against Echinococcosis.

In this work, an end-to-end method, named DSPE, is proposed for predicting anti-Echinococcosis drug synergistic combinations. In DSPE, a dataset of Echinococcosis drug synergistic combinations is constructed by retrieving and extracting information from related scientific articles. Further, DSPE employs a residual graph attention network to deeply analyze drug characteristics and their interactions, thereby enhancing the performance of deep learning models. It also explores the protein-protein interaction network related to Echinococcosis, using node2vec combined with an attention mechanism to efficiently encode disease features. Finally, it predicts the synergy of drug combinations based on the Bliss score by integrating drug combinations and disease features.

Experimental evidence shows that DSPE outperforms five state-of-the-art algorithms in predicting drug combination effects by leveraging disease-target information and single-agents for the treatment.

DSPE improves prediction accuracy and addresses the issue of data scarcity for new diseases, offering new insights and methods for the development of treatment plans for parasitic diseases in the future.

CNVviso is a web-based NGS data visualization tool designed to be user-friendly for medical researchers who desire visualization-based analysis of large amounts of data with no-code technology.

Unlike small tools that require extensive programming skills, CNVviso is designed to minimize hard coding and repetitive operations for ultra-low coverage whole-genome sequencing data from preimplantation genetic testing.

CNVviso was completely programmed and developed based on the R programming language and Shiny framework. CNVviso source code is available for download under the MIT Licence from https://github.com/Anegin24/CNVviso.

The execution architecture of CNVviso allows users to input CNV data, which is then automatically visualized as graphs.

The charts display output of CNVviso are lively and interactive, enabling users to exploit the information effectively. The tool also provides tabular results containing aggregated information.

G-protein coupled receptors (GPCRs) represent a large family of membrane proteins, distinguished by their seven-transmembrane helical structures. These receptors play a pivotal role in numerous physiological processes. Nowadays, many researchers have proposed computational methods to identify GPCRs. In the past, we introduced a powerful method, EMCBOW-GPCR, which was designed for this purpose. However, the feature extraction technique employed is susceptible to out-of-vocabulary errors, indicating the potential for enhanced accuracy in GPCR identification.

To solve the challenges, we propose a novel approach termed GPCR-AFPN. This method leverages the FastText algorithm to effectively extract features from protein sequences. Additionally, it employs a powerful deep neural network as the predictive model to improve prediction accuracy.

To validate the efficacy of the proposed GPCR-AFPN method, we conducted five-fold cross-validation and independent tests, respectively. The experimental results indicate that GPCR-AFPN outperforms existing methods.

Overall, our proposed method, GPCR-AFPN, can improve the accuracy of GPCR identification. For the convenience of researchers interested in applying our latest advancements, a user-friendly webserver for GPCR-AFPN is available at www.lzzzlab.top/gpcrafpn/, and the corresponding code can be downloaded at https://github.com/454170054/GPCR-AFPN.

Genetic information about organisms' traits is stored and encoded in deoxyribonucleic acid (DNA) sequences. The fundamental inquiry into the storage mechanisms of this genetic information within genomes has long been of interest to geneticists and biophysicists.

The objective of this study was to investigate the distribution of coding sequence (CDS) lengths in species genomes across different kingdoms.

In this study, we used the maximum entropy principle and the gamma distribution model based on a comprehensive dataset including viruses, archaea, bacteria, and eukaryote species.

Our study result revealed unique patterns in CDS length distributions among kingdoms and CDS lengths exhibit a right-skewed distribution, with varying preferences among kingdoms. Eukaryotes displayed bimodal distributions, with CDS sequences longer than those of prokaryotes. Fitting the gamma distribution model revealed differences in shape and scale parameters among kingdoms, with eukaryotes exhibiting larger scale parameters, indicating longer CDS sequences. Additionally, analysis of moments highlighted the complexity of eukaryotic genomes relative to prokaryotes.

This study result deepens our understanding of genome evolution and provides valuable insights for biological research.