Internal and external normalization of nascent RNA sequencing run-on experiments

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Nascent RNA sequencing requires normalization for accurate analysis, often relying on external spike-ins.
• Unlike standard RNA-seq, spike-in methods for nascent RNA are not standardized and assume constant reaction efficiency.

Purpose of the Study:

• To evaluate the variability of published nascent RNA spike-ins across different normalization methods.
• To develop a novel Bayesian model for estimating errors in spike-in normalization.

Main Methods:

• Analysis of a large dataset of published nascent RNA spike-ins.
• Development and application of a biologically-informed Bayesian model termed Virtual Spike-In (VSI).
• Utilized both external spike-ins and reads from the 3' end of long genes for VSI application.

Main Results:

• Published spike-ins in nascent RNA experiments are frequently under-sequenced, exhibiting significant inter-sample variability.
• The high variability observed in spike-in estimates can substantially impact downstream analyses.
• This variability complicates the biological interpretation of nascent RNA sequencing results.

Conclusions:

• Existing spike-in normalization methods for nascent RNA sequencing are prone to high variability.
• The Virtual Spike-In (VSI) model offers a more robust estimation of normalization errors.
• Addressing spike-in variability is crucial for reliable biological insights from nascent RNA studies.