bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.01.22.634269

In Silico Analysis of Non-Coding RNA Regulation in Human Gene Expression: A Systematic Computational Approach to Understanding Regulatory Networks

Abstract

The regulatory networks of miRNAs, lncRNAs, and their target genes play a crucial role in controlling various cellular processes, including cell growth, apoptosis, and immune responses. In this study, we performed an in silico analysis to explore the interactions between miRNAs, lncRNAs, and their target genes in the context of disease mechanisms. We utilized multiple computational approaches, including miRNA-target interaction prediction, lncRNA-target network analysis, differential expression analysis using RNA-seq data, and validation of interactions using miRNA target prediction tools. Our results highlight key genes involved in apoptosis, cell cycle regulation, and tumorigenesis, providing valuable insights into the molecular mechanisms underlying disease progression. Non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), have emerged as pivotal regulators of gene expression in human cells. Despite substantial research into their roles, there remains a critical gap in understanding how these molecules interact within complex regulatory networks. In this study, we employed a comprehensive bioinformatics approach to systematically identify and analyze ncRNA-mediated gene regulation in human cells. We utilized publicly available datasets from the ENCODE and GEO repositories, combined with computational tools such as miRBase, LNCipedia, TargetScan, and Cytoscape, to predict ncRNA-gene interactions and construct regulatory networks. Our analysis reveals several novel ncRNA regulators and their associated gene targets, which were further explored through pathway enrichment analysis. This study provides new insights into the regulatory networks of ncRNAs in human gene expression, offering a foundation for future functional studies and potential therapeutic applications.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Akbar, A.. 2025-01-24. In Silico Analysis of Non-Coding RNA Regulation in Human Gene Expression: A Systematic Computational Approach to Understanding Regulatory Networks. https://doi.org/10.1101/2025.01.22.634269

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗