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

Abstract

The precise biological interpretation of oligo(dT)-based RNA sequencing (RNA-seq) datasets, particularly in single-cell RNA-seq (scRNA-seq), is invaluable for understanding complex biological systems. However, the presence of biases can lead to misleading results in downstream analysis. This study has now identified two additional biases that are not accounted for in established bias models: poly(A)-tail length bias and fixed-position GC-content bias. These biases have a significant negative impact on the overall quality of oligo(dT)-based RNA-seq data. To address these biases, we have developed a universal bias-mitigating method based on the lower-affinity binding of short and nonanchored oligo(dT) primers to poly(A) tails. This method significantly reduces poly(A) length bias and completely eliminates fixed-position GC bias. Furthermore, the use of short oligo(dT) with impartial binding behavior toward the diverse poly(A) tails renders RNA-seq with more reliable measurements. The findings of this study are particularly beneficial for scRNA-seq datasets, where accurate benchmarking is critical.