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Montrone, C.

Publications and source records attributed to Montrone, C..

2 recordsLinked to original sources

scRAPID-web: a web server for predicting protein-RNA interactions from single-cell transcriptomics

SummarySingle-cell RNA sequencing (scRNA-seq) enables high-resolution studies of gene regulation, capturing gene expression at the individual cell level. We previously developed scRAPID, a computational pipeline for predicting protein-RNA interactions and identifying hub RNA-binding proteins (RBP) and RNAs through the integration of gene regulatory network (GRNs) inference from scRNA-seq data and catRAPID predictions. To make this tool accessible to a broader audience, we introduce scRAPID-web, a user-friendly web server supporting analysis of scRNA-seq data across eight model organisms. scRAPID-web offers customizable options to preprocess the input gene expression matrix, such as gene selection and cell type filtering. Users can choose from three GRN inference algorithms and decide whether to focus the analysis on specific gene types. Precompiled libraries allow fast filtering and motif-based validation of the inferred interactions. Results include detailed tables of predicted protein-RNA pairs and hubs, along with an interactive network visualization of potential RBP complexes built based on the inferred shared targets. scRAPID-web democratizes access to GRN-based analyses, providing insights into protein-RNA interactions and regulatory complexes in diverse cellular contexts. Availability and implementationscRAPID-web can be accessed at: https://tools.tartaglialab.com/scrapid.

bioinformatics↗

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

systems biology↗