Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) library preparation protocols rely on enzymatic reactions.
• These enzymatic reactions can introduce biases in sequencing read coverage along and between transcripts.
• Understanding and mitigating these biases is crucial for accurate transcript abundance quantification.

Purpose of the Study:

• To investigate the mechanistic basis of coverage biases in RNA-seq and scRNA-seq library preparation.
• To develop a modeling framework to link enzyme kinetics to observed coverage patterns.
• To identify strategies for improving the accuracy and sensitivity of RNA-seq experiments.

Main Methods:

• Development of an integrated modeling framework to analyze enzyme reaction dynamics during library preparation.
• Analysis of RNA-seq and scRNA-seq data from six different library preparation protocols.
• Experimental manipulation of incubation temperatures to assess impact on polymerase processivity and library yield.

Main Results:

• Coverage biases in RNA-seq and scRNA-seq libraries are mechanistically linked to polymerase processivity.
• Lowering incubation temperature during library preparation enhances polymerase processivity, increases yield, and improves scRNA-seq sensitivity.
• Correction factors derived from the model can improve transcript quantification accuracy in existing datasets.

Conclusions:

• Polymerase processivity is a key determinant of coverage bias in RNA-seq and scRNA-seq.
• Optimizing reaction conditions, such as lowering incubation temperature, can significantly enhance library preparation efficiency and data quality.
• The developed framework and correction factors provide valuable tools for more accurate representation of in vivo transcript abundances.