Current Bioinformatics - Online First

The heterogeneity in tumours poses significant challenges to the accurate prediction of cancer stages, necessitating the expertise of highly trained medical professionals for diagnosis. Over the past decade, the integration of deep learning into medical diagnostics, particularly for predicting cancer stages, has been hindered by the black-box nature of these algorithms, which complicates the interpretation of their decision-making processes.

This study seeks to mitigate these issues by leveraging the complementary attributes found within functional genomics datasets (including mRNA, miRNA, and DNA methylation) and stained histopathology images. We introduced the Extended Squeeze- and-Excitation Multiheaded Attention (ESEMA) model, designed to harness these modalities. This model efficiently integrates and enhances the multimodal features, capturing biologically pertinent patterns that improve both the accuracy and interpretability of cancer stage predictions.

Our findings demonstrate that the explainable classifier utilised the salient features of the multimodal data to achieve an area under the curve (AUC) of 0.9985, significantly surpassing the baseline AUCs of 0.8676 for images and 0.995 for genomic data.

Furthermore, the extracted genomics features were the most relevant for cancer stage prediction, suggesting that these identified genes are promising targets for further clinical investigation.

An uncommon neurological condition known as Guillain-Barre syndrome (GBS) develops when the body's immunological system unintentionally targets peripheral nerves.

This work aimed to compare scRNA-seq and transcriptome data to find novel gene biomarkers linked to CD4⁺ T cells and B cells that might potentially be utilized for the diagnosis and assessment of GBS. It aimed to employ scRNA-seq data and bioinformatics tools analysis to identify cell-specific biomarkers for GBS diagnosis and prognosis.

scRNA-seq and microarray datasets from the GEO database were utilized to identify differentially expressed genes (DEGs). Pathway enrichment, identification of potential hub genes, and gene regulatory studies were employed using FunRich, DAVID, STRING, and NetworkAnalyst tools.

After integrating the DEGs and performing a comparative analysis, it was discovered that there were 84 DEGs shared between scRNA-seq and microarray datasets. The presence of signal transduction, immune system, cytokine signaling, NOD-like receptor signaling, and focal adhesion was detected in the most significant gene ontology and metabolic pathways. After generating a protein-protein interaction (PPI) network, we used eleven topological algorithms of the cytoHubba plugin for identifying six key hub genes, including CDC42, PTPRC, SRSF1, HNRNPA2B1, NIPBL, and FOS. Several crucial transcription factors (CHD1, IRF1, FOXC1, GATA2, YY1, E2F1, and CREB1) and two significant microRNAs (hsa-mir-20a-5p and hsa-mir-16-5p) were also discovered as hub gene regulators. The receiver operating characteristics (ROC) curve was used to evaluate the prognostic, expression, and diagnostic capabilities of the six major hub genes, indicating a good scoring value.

Finally, functional enrichment pathway analysis, PPI, and regulatory networks analysis demonstrated the critical functions of the identified key hub genes. After further wet lab research is validated, our research work may offer useful predicted potential biomarkers for the diagnosis and prognosis of GBS.

The 5' UTR plays a crucial role in gene regulation, which may be through its interaction with introns. Hence, there is a need to further study this interaction.

This study aimed to investigate the interactions between 5' UTR and introns and their correlation with species evolution.

The optimally matched segments between 5' UTR and introns were identified using Smith-Waterman local similarity matching, and the biological statistical methods were applied to compare the optimally matched segments between different species.

The interactions between 5' UTR and introns were found to be primarily mediated by weak bonds and demonstrated a directional change with species evolution. Additionally, a large proportion of the optimally matched segments were very similar to miRNA and siRNA in terms of length and matching rate characteristics.

The weak bonds in the interactions between the 5' UTR and the introns could enhance the flexibility of expression regulation, and an important correlation was found between the characteristic distributions of the optimally matched segments and species evolution. Additionally, the length and matching rate of a large proportion of optimally matched segments were very similar to those of miRNA and siRNA. In conclusion, it is highly probable that quite a few of the optimally matched segments are some kinds of functional non-coding RNAs.

Multi-omics data integration has transformed personalized medicine, providing a comprehensive understanding of disease mechanisms and informed precision therapeutic options. Multi-omics data generated for the same samples/patients can help in getting insights into the flow of biological information at several levels, thereby providing in-depth information regarding the molecular mechanisms underlying pathological conditions. Multi-omics integration plays a pivotal role in personalized medicine by providing comprehensive insights into the complex biological systems of individual patients. This review provides a comprehensive account of the current and future progress brought into multi-omics methodologies, promising to refine diagnostics and therapeutic strategy by integrating genomic, transcriptomic analyses, proteomics approaches and metabolome screens.

A literature search was performed in PubMed using keywords like genomics, proteomics, transcriptomics, metabolomics, multi-omics, and precision medicine to identify published research articles. A thorough review of all results was then conducted, and their results and conclusions were compiled and summarized.

By analyzing various omics layers, such as genomics, transcriptomics, proteomics, and metabolomics, multi-omics approaches enable the identification of patient-specific molecular traits and the discovery of new clinical therapeutics for diseases. Integration of various data types augments diagnostics, optimizes therapeutic regimens and supports personalized medicine according to an individual patient profile.

Integration of multi-omics data and its applications in various fields, such as cancer research, helps in optimizing patient-specific treatment and improvement of patient health. With time, as these technologies reach more people, they stand to democratize precision medicine and hopefully bridge health disparities. In conclusion, the present review highlights multiomics data integration as a transformative step towards personalized medicine and ultimately changing patient care from empirical-based to precision or individualized.

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.

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 10µM 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.