Current Bioinformatics - Online First

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.

Long non-coding RNAs (lncRNAs) are a category of more extended RNA strands that lack protein-coding abilities. Although they are not involved in the translation of proteins, studies have shown that they play essential regulatory functions in cells, regulating gene expression and cell biological processes. However, it is both costly and inefficient to determine the associations between lncRNAs and diseases through biological experiments. Therefore, there is an urgent need to develop convenient and fast computational methods to predict lncRNA-disease associations (LDAs) more efficiently.

Predicting disease-associated lncRNAs can help explore the mechanisms of action of lncRNAs in diseases, and this is crucial for early intervention and treatment of diseases.

In this paper, we propose an enhanced heterogeneous graph representation method for predicting LDAs, named GCGALDA. The GCGALDA first obtains the topological structure features of nodes by a biased random walk. Based on this, the neighboring nodes of a node are weighted using the attention mechanism to further mine the semantic association relationships between nodes in the graph data. Then, a graph convolution network (GCN) is used to transfer the neighborhood features of the node to the central node and combine them with the node's features so that the final node representation contains not only structural information but also semantic association information. Finally, the association score between lncRNA and disease is obtained by multilayer perceptron (MLP).

As evidenced by the experimental findings, the GCGALDA outperforms other advanced models in terms of prediction accuracy on openly accessible databases. In addition, case studies on several human diseases further confirm the predictive ability of the GCGALDA.

In conclusion, the proposed GCGALDA model extracts multi-perspective features, such as topology, semantic association, and node attributes, obtains high-quality heterogeneous graph node representations, and effectively improves the performance of the LDA prediction model.

With the rapid evolution of single-cell RNA sequencing technology, the study of cellular heterogeneity in complex tissues has reached an unprecedented resolution. One critical task of the technology is cell-type annotation. However, challenges persist, particularly in annotating novel cell types.

Current methods rely heavily on well-annotated reference data, using correlation comparisons to determine cell types. However, identifying novel cells remains unstable due to the inherent complexity and heterogeneity of scRNA-seq data and cell types. To address this problem, we propose scADCA, a method based on anomaly detection, for identifying novel cell types and annotating the entire dataset.

The convolutional modules and fully connected networks are integrated into an autoencoder, and the reference dataset is trained to obtain the reconstruction errors. The threshold based on these errors can distinguish between novel and known cells in the query dataset. After novel cells are identified, a multinomial logistic regression model fully annotates the dataset.

Using a simulation dataset, three real scRNA-seq pancreatic datasets, and a real scRNA-seq lung cancer cell line dataset, we compare scADCA with six other cell-type annotation methods, demonstrating competitive performance in terms of distinguished accuracy, full accuracy, -score, and confusion matrix.

In conclusion, the scADCA method can be further improved and expanded to achieve better performance and application effects in cell type annotation, which is helpful to improve the accuracy and reliability of cytology research and promote the development of single-cell omics.