MOTIVATION: DNA methylation (DNAme) is the best studied epigenetic mechanism that plays pivotal role in tissue differentiation and epigenetic disruption has been correlated to diverse disease types (e.g. cancer, metabolic disorders). While various DNAme array platforms have been discovered, data analysis remains a challenging task which often requires in-depth bioinformatic expertise. Here, we developed a user-friendly web-based application for data analysis and visualization that accommodates users ranging from early-career basic/translational researchers to experienced bioinformaticians. RESULTS: CpGene is a web application for analyzing DNA methylation array data. It supports Illumina 450K, EPIC, and EPICv2 methylation array platforms and processes .idat files with integrated preprocessing, normalization, and quality control. Biomarker discovery is available through either classic differential methylation point analysis or machine learning-based feature selection as well as gene enrichment analysis. Results are summarized with clear visualizations, to aid interpretation. By combining these functions in a unified interface, CpGene streamlines methylation analysis and helps identify CpG sites and genes with biological and clinical relevance. AVAILABILITY AND IMPLEMENTATION: CpGene is openly accessible as a web service through http://cpgene.duckdns.org:8001/ and it's source code is available on https://github.com/kostaslazaros/cpgenene.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder primarily linked to memory loss and cognitive decline, although less common clinical manifestations are being more frequently identified. Disruptions in gene expression and regulation have been increasingly linked to neurodegeneration. Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are powerful methods for analyzing transcriptomic diversity at the level of individual cells. Herein, analysis of snRNA-seq data derived from the entorhinal cortex of post-mortem brain samples from individuals either diagnosed with AD (Braak stage II) or serving as healthy controls was executed trying to identify potent biomarkers and molecular mechanisms in which these genes are involved. Preprocessing, integration, clustering and cell type annotation were performed using indicator-based methods followed by differentially expression analysis and enrichment analysis. UMAP was used for data visualization as well as Local Inverse Simpson's Index to evaluate the effectiveness of data integration. Leiden clustering was performed on the integrated dataset while the Wilcoxon rank-sum test was utilized for differential expression analysis. Cell-cell junctions were detected as the most significantly enriched category showing significant contribution to the structural integrity and signaling across the blood-brain barrier. Moreover, important pathways such as the Brain-Derived Neurotrophic Factor and Lglutamate transmembrane transport were detected emphasizing the role of synaptic dysfunction and neurodegeneration in AD. This approach supports the implementation of snRNA-seq data analysis in complex disorders such as neurodegenerative diseases, providing new challenges for identifying fundamental biological processes on a molecular perspective.

Alzheimer’s Disease (AD) remains a formidable challenge in neurodegenerative research, necessitating innovative approaches to uncover novel therapeutic strategies. This study presents a graph-based approach to integrate large-scale drug and protein data, aiming to identify potential drug repurposing candidates for AD. Our methodology constructs a comprehensive graph incorporating protein-protein interactions and drug-protein relations, providing a multifaceted view of the intricate relationships within the biological and pharmacological landscape. By leveraging this graph, we conduct an in-depth analysis to explore various drug repurposing possibilities, focusing on the alignment of AD-related single-cell transcriptomic data. Our approach enables the identification of promising drug candidates by examining the connectivity and interaction patterns within the graph, revealing potential therapeutic targets and drug synergies that may be beneficial for AD treatment. The integration of diverse data types allows for a more holistic understanding of the underlying molecular mechanisms and drug interactions. Through this graph-based analysis, we uncover several promising drug repurposing candidates, providing a foundation for further experimental validation and clinical investigation. This study underscores the potential of leveraging large-scale data and graph-based methodologies in drug repurposing efforts for neurodegenerative diseases, contributing to the advancement of therapeutic research in AD. Our findings illuminate the importance of integrative data analysis in biomedical research, paving the way for the development of more effective and targeted therapeutic interventions for AD.

In recent years, feature selection has become an increasingly active field of data science and machine learning research. Most of the datasets that are being used nowadays for various machine learning tasks consist of thousands of features (columns), which make them extremely complex and difficult to work with. In this paper, we propose a feature selection methodological pipeline that can be used to reduce the complexity of high dimensional datasets through the elimination of redundant and/or non-informative features as well as to improve the performance of machine learning models which are trained on high dimensional datasets. The proposed method has been applied to high-dimensional biomedical data and compared against a classic filter-based feature selection algorithm. Specifically, the method was applied to gene expression profiles of a single-cell RNA-seq dataset from healthy and infected by covid-19 human samples.