Diminishing returns in next-generation sequencing (NGS) transcriptome data.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• RNA sequencing (RNA-seq) is a powerful tool for studying gene expression at a genome-wide scale.
• High sequencing depth is often required for accurate transcript abundance, but it incurs significant costs.
• Cost-effective experimental design is crucial for the routine application of RNA-seq.

Purpose of the Study:

• To determine the minimal sequencing depth required for accurate gene expression analysis using RNA-seq.
• To establish correlations between sequencing depth and accuracy across diverse model organisms.
• To guide cost-effective experimental design in next-generation sequencing (NGS) research.

Main Methods:

• Analyzed 36 RNA-seq datasets from six model organisms (yeast, human, fruit fly, worm, mouse, and Arabidopsis).
• Subsampled reads from each dataset to simulate varying sequencing depths.
• Mapped reads to reference genomes and assessed transcript abundance accuracy at different coverage levels.

Main Results:

• A sequencing depth of as low as one million reads achieved high accuracy (r=0.99) comparable to >30 million reads for highly-expressed genes.
• This finding was consistent across all six model organisms studied.
• Highly-expressed genes, often critical for metabolic and pathological studies, can be accurately quantified with reduced sequencing depth.

Conclusions:

• Minimal sequencing depth significantly reduces RNA-seq costs without compromising accuracy for highly-expressed genes.
• These findings provide practical guidance for optimizing RNA-seq experimental design for cost-efficiency.
• The results support the broader adoption of RNA-seq in biological research by lowering financial barriers.