Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• RNA sequencing (RNA-seq) is crucial for transcriptome studies, with numerous analysis tools available.
• Existing assessments often lack a comprehensive evaluation of RNA-seq analysis workflows.
• Unleashing the full potential of RNA-seq requires a broader analytical scope beyond expression analysis.

Purpose of the Study:

• To conduct an extensive analysis of a wide spectrum of RNA-sequencing workflows.
• To evaluate RNA variant-calling, RNA editing, and RNA fusion detection techniques alongside expression analysis.
• To propose a comprehensive and accurate RNA-seq analysis protocol and computational pipeline.

Main Methods:

• Examined short- and long-read RNA-seq technologies.
• Assessed 39 analysis tools, leading to approximately 120 combinations.
• Conducted ~490 analyses using 15 diverse samples (germline, cancer, stem cell data).

Main Results:

• Reported the performance of various RNA-seq analysis tools and workflows.
• Developed and validated the RNACocktail protocol and computational pipeline.
• Demonstrated high accuracy in transcriptome analysis using the proposed methods.

Conclusions:

• The RNACocktail protocol offers a comprehensive approach to RNA-seq analysis.
• The associated computational pipeline achieves high accuracy for diverse transcriptome studies.
• This protocol enables researchers to extract more biologically relevant predictions through broad transcriptome analysis.