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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Updated: Feb 26, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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REDAC: RNA-seq expression data analysis chatbot.

Giovanni Maria De Filippis1, Pranoy Sahu2, Pasqualina Ambrosio2

  • 1Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80125, Italy.

Bioinformatics Advances
|February 25, 2026
PubMed
Summary
This summary is machine-generated.

REDAC simplifies RNA-seq expression data analysis with natural language queries. This web-based R application enhances data exploration, visualization, and interpretation, promoting reproducible research for all users.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA-seq expression data analysis presents significant challenges due to complex analytical processes and interpretation difficulties.
  • Researchers often require bioinformatician expertise for tasks like selecting statistical tests and performing data normalization and filtering.
  • Ensuring rigor and reproducibility in RNA-seq analysis remains a hurdle for many users.

Purpose of the Study:

  • To develop a user-friendly platform that simplifies and enhances RNA-seq expression data exploration and analysis.
  • To provide a straightforward method for performing differential RNA-seq analysis using natural language queries.
  • To integrate Large Language Models for biological interpretation of pathway enrichment results and promote research reproducibility.

Main Methods:

  • Development of REDAC, a web-based R application with an interactive platform.
  • Implementation of natural language query processing for RNA-seq analysis.
  • Integration of Large Language Models (Gemma, LLaMA) with a PubMed-based Retrieval-Augmented Generation module for pathway enrichment interpretation.
  • Automated generation of analysis reports to ensure reproducibility.

Main Results:

  • REDAC offers a simplified, rapid, and transparent approach to RNA-seq expression data analysis.
  • The application enables users to perform complete analyses, generate comprehensive visualizations, and obtain biological interpretations.
  • REDAC facilitates reproducible research through automated report generation.
  • The platform successfully integrates LLMs for enhanced biological interpretation of pathway enrichment.

Conclusions:

  • REDAC effectively addresses the complexity of RNA-seq data analysis, making it accessible to a wider range of researchers.
  • The tool enhances data exploration, analysis, and interpretation through an intuitive, natural language-driven interface.
  • REDAC promotes reproducibility in bioinformatics research by automating report generation and integrating advanced interpretation methods.