TRUFA: A User-Friendly Web Server for de novo RNA-seq Analysis Using Cluster Computing

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Next-generation sequencing (NGS) for transcriptome analysis (RNA-seq) is increasingly accessible.
• Many biological disciplines require RNA-seq data analysis.
• Bioinformatics expertise and computational resources are often limiting factors for researchers.

Purpose of the Study:

• To present TRUFA (TRanscriptome User-Friendly Analysis), an open informatics platform.
• To provide a web-based interface for de novo RNA-seq analysis and comparative transcriptomics.
• To enable researchers without extensive bioinformatics knowledge to perform complex transcriptome analyses.

Main Methods:

• TRUFA offers a comprehensive pipeline for raw read cleaning, transcript assembly, annotation, and expression quantification.
• The platform is highly parallelized and utilizes high-performance computing resources for intensive analyses.
• TRUFA was validated using four previously published RNA-seq datasets.

Main Results:

• TRUFA produced globally similar results compared to original studies for validation datasets.
• The platform demonstrated superior performance in analyzing a specific green tea dataset.
• TRUFA enables fast, robust, and user-friendly RNA-seq data analysis.

Conclusions:

• TRUFA democratizes RNA-seq data analysis by providing an accessible and efficient platform.
• The tool addresses the gap in bioinformatics expertise and computational resources for researchers.
• TRUFA facilitates deeper biological insights from transcriptome data across various disciplines.