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Background <p>Single-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 <i>cat</i>RAPID predictions. To make this tool accessible to a broader audience, we introduce scRAPID-web, a user-friendly web server for the prediction of RBP–RNA and RBP–RBP interactions from scRNA-seq data across eight model organisms.</p> Results <p>scRAPID-web offers customizable options to preprocess the input gene expression matrix, such as count-based 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.</p> Conclusions <p>scRAPID-web democratizes access to GRN-based analyses, providing insights into protein–RNA interactions and regulatory complexes based on single-cell level expression data analysis and binding propensity estimation. scRAPID-web can be freely accessed at <a href="https://tools.tartaglialab.com/scrapid">https://tools.tartaglialab.com/scrapid</a>.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Jonathan Fiorentino,
  • Alexandros Armaos,
  • Chiara Montrone,
  • Alessio Colantoni,
  • Gian Gaetano Tartaglia

摘要

Background

Single-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 for the prediction of RBP–RNA and RBP–RBP interactions from scRNA-seq data across eight model organisms.

Results

scRAPID-web offers customizable options to preprocess the input gene expression matrix, such as count-based 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.

Conclusions

scRAPID-web democratizes access to GRN-based analyses, providing insights into protein–RNA interactions and regulatory complexes based on single-cell level expression data analysis and binding propensity estimation. scRAPID-web can be freely accessed at https://tools.tartaglialab.com/scrapid.