RNA sequencing has become a standard method for measuring gene expression across thousands of genes at once. While the technology itself is powerful, analyzing the resulting data can be complex. Researchers often need bioinformatics expertise to select appropriate statistical tests, perform normalization, filter low quality data, and interpret results correctly. For many laboratories, this creates a barrier between generating RNA sequencing data and extracting meaningful biological insight.

A new web based tool called REDAC aims to lower that barrier. A team led by researchers at the University of Naples Federico II and the CNR-Institute of Endotypes in Oncology, Metabolism and Immunology, Italy have developed an interactive R application designed to make RNA sequencing expression analysis more accessible. REDAC allows users to perform differential gene expression analysis through natural language queries, meaning researchers can describe what they want to do instead of writing code.

REDAC main interface

The platform guides users through essential steps such as normalization, filtering, and statistical testing. It also generates visualizations that help users explore their data more clearly. One notable feature of REDAC is its integration with large language models, including Gemma and LLaMA, combined with a PubMed based retrieval system. This setup helps provide biological interpretation of pathway enrichment results by drawing on relevant scientific literature.

In addition to simplifying analysis, REDAC emphasizes reproducibility. The application automatically generates analysis reports, ensuring that all steps, parameters, and outputs are documented. This is important for transparency, collaboration, and publication.

By combining an intuitive interface with automated statistical workflows and AI assisted interpretation, REDAC provides a practical solution for researchers who want to analyze RNA sequencing expression data without advanced programming skills. Tools like this may help bridge the gap between experimental biology and computational analysis, making transcriptomic research more widely accessible.

Availability – REDAC is available for local (https://github.com/franruss/REDAC) and online use (https://frusso.shinyapps.io/REDAC).