Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic experiment

Abstract

Over the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted. Several computational methods to estimate RNA processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate RNA synthesis, processing and degradation rates from a single sample. To this end, we use the Zeisel model and take advantage of its analytical solution, reducing the problem to solving a univariate non-linear equation on a bounded domain. This makes our method computationally efficient, while enabling inference of rates that correlate well with previously published data sets. Using our approach on a single sample, we were able to reproduce and extend the observation that dynamic biological processes such as transcription or chromatin modifications tend to involve genes with higher metabolic rates, while stable processes such as basic metabolism involve genes with lower rates.

In addition to saving experimental work and computational time, having a sample-based rate estimation has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and perform quality control. Finally the method and theoretical results described here are general enough to be useful in other settings such as nucleotide conversion methods.