Search bioRxivSearch

bioRxiv · 10.1101/170126

miRNAtools: advanced training using the miRNA web of knowledge

Abstract

miRNAs are small non-coding RNAs, that act as negative regulators of the genomic output. Their intrinsic importance within cell biology and human disease is well known. Their mechanism of action based on the base pairing binding to their cognate targets, have helped the development of many computer applications for the prediction of miRNA target recognition. More recently, many other computer applications have appeared with the objective of studying miRNA roles in many contexts, trying to dissect and predict their functions in a specific biological process. Learning about miRNA function needs a practical training in the use of specific computer and web-based applications that are complementary to the wet-lab studies. In the last seven years we have been involved in the organization of advanced training courses about the in silico functional analysis of miRNAs and non-coding RNAs, for students ranging from the postgraduate to the post-doctoral level. In order to guide the learning process about miRNAs, we have created miRNAtools (http://mirnatools.eu), a web repository of miRNA tools and tutorials. This page is a compilation of tools to analyze miRNAs and their regulatory action that intends to collect and organize the information that is dispersed in the web. The miRNAtools webpage is completed by a collection of tutorials that can be used by students and tutors engaged in advanced training courses. The tutorials follow the rationale of the analysis of the function of selected miRNAs, starting from their nomenclature and genomic localization and finishing by assessing their involvement in specific cellular functions.\n\nDESCRIPTION OF THE AUTHORFrancisco J. Enguita is assistant professor at the Faculty of Medicine, University of Lisbon and senior investigator at the Instituto de Medicina Molecular within the same University.\n\nKEY POINTSO_LI- We have designed a web-page, mIRNAtools3 (http://mirnatools.eu) specifically devoted for advanced teaching purposes within the miRNA field.\nC_LIO_LI- The webpage constitutes also a repository of web-tools for the analysis of miRNA function in several contexts.\nC_LIO_LI- The webpage contains tutorials that cover many aspects related with miRNAs including nomenclature, target prediction and validation, and functional analysis.\nC_LI

Source connections

Explore related subjects

Keep this discovery

BibTeXRIS

Enguita, F. J.. 2017-07-30. miRNAtools: advanced training using the miRNA web of knowledge. https://doi.org/10.1101/170126

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