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RAP: A Web Tool for RNA-Seq Data Analysis.

Mattia D'Antonio¹, Pietro Libro², Ernesto Picardi^3,4,5

¹SuperComputing Applications and Innovation Department, CINECA, Rome, Italy.

Methods in Molecular Biology (Clifton, N.J.)

|April 9, 2021

Summary

This summary is machine-generated.

Researchers can now easily analyze RNA sequencing data with RAP, a user-friendly web tool. This RNA-Seq Analysis Pipeline simplifies complex transcriptome analysis for identifying gene expression and other critical RNA features.

Keywords:

Alternative splicing sites Bioinformatics Fusion transcripts Genomics HPC RNA-Seq Transcriptomics

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Early RNA research focused on protein synthesis, but later discoveries revealed diverse functional roles for non-coding RNA.
Next-Generation Sequencing (NGS) and RNA-sequencing (RNA-seq) have dramatically reduced sequencing costs, enabling widespread transcriptome analysis.
Understanding transcriptome complexity and RNA's role in cellular processes has advanced significantly in the last decade.

Purpose of the Study:

To introduce RAP (RNA-Seq Analysis Pipeline), an automated, user-friendly web tool for comprehensive transcriptome analysis.
To provide researchers with a simplified method for analyzing RNA-seq data without requiring programming expertise or knowledge of high-performance computing.

Main Methods:

Development of a web-based platform implementing a detailed transcriptome analysis workflow.
Integration of computational bioinformatics methods for processing and interpreting RNA-seq data.
User interface designed for accessibility, abstracting underlying computational infrastructure.

Main Results:

RAP enables detection of differentially expressed genes and transcripts.
The tool identifies spliced junctions, and constitutive or alternative polyadenylation sites.
RAP facilitates the prediction of gene fusion events from RNA-seq data.

Conclusions:

RAP democratizes advanced transcriptome analysis, making complex RNA-seq data interpretation accessible to a broader research community.
The automated workflow empowers researchers to gain insights into transcriptome complexity and RNA functions without specialized computational skills.
This tool supports the identification of key RNA features, aiding in the understanding of cellular processes and disease mechanisms.