Robust adjustment of sequence tag abundance.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Next-generation sequencing (NGS) relies on DNA/RNA amplification, which introduces significant bias in read counts.
• This amplification bias complicates accurate gene expression level estimation.
• Existing methods struggle to fully correct for these biases, particularly gene length dependency.

Purpose of the Study:

• To develop and validate a novel procedure to correct for amplification bias in sequencing data.
• To demonstrate the method's effectiveness in mitigating gene length bias in gene expression estimation.
• To improve the accuracy of differential gene expression analysis.

Main Methods:

• A new procedure, RASTA (Read-count Amplification bias הסכמה), was developed to account for amplification bias.
• The method was tested using both simulated and real RNA-sequencing (RNA-seq) datasets.
• Statistical analysis and gene set enrichment analyses were performed.

Main Results:

• Simulations showed the RASTA procedure more accurately captures true gene expression compared to traditional methods.
• The method led to increased statistical power in differential expression testing, especially for shorter genes with high expression.
• Application to an Arabidopsis RNA-seq dataset revealed distinct gene ontologies.

Conclusions:

• The RASTA procedure effectively corrects for amplification bias in RNA-seq data.
• This correction improves gene expression quantification and enhances differential expression analysis.
• The freely available R code facilitates broader adoption and application of the method.