Critical view on oligo(dT)-based RNA-seq: bias arising, modeling, and mitigating

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Accurate interpretation of RNA sequencing (RNA-seq) data is crucial for biological discovery.
• Oligo(dT)-based RNA-seq, especially single-cell RNA-seq (scRNA-seq), is widely used but susceptible to biases.
• Existing bias models do not fully account for all sources of error in RNA-seq data.

Purpose of the Study:

• To identify and characterize novel biases in oligo(dT)-based RNA sequencing.
• To develop a universal method for mitigating these identified biases.
• To enhance the reliability and accuracy of RNA-seq measurements, particularly for scRNA-seq.

Main Methods:

• Identification of poly(A)-tail length bias and fixed-position GC-content bias in oligo(dT)-based RNA-seq.
• Development of a novel bias-mitigation strategy utilizing short, nonanchored oligo(dT) primers.
• Evaluation of the method's efficacy in reducing bias and improving data quality.

Main Results:

• Two previously unaddressed biases, poly(A)-tail length bias and fixed-position GC-content bias, were identified.
• The developed method significantly reduces poly(A)-tail length bias.
• Fixed-position GC bias was completely eliminated by the new approach.

Conclusions:

• The novel bias-mitigation method improves the quality and reliability of oligo(dT)-based RNA-seq data.
• This approach is particularly beneficial for single-cell RNA sequencing (scRNA-seq) datasets.
• The findings contribute to more accurate biological interpretations from RNA-seq experiments.