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

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 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. The approach is computationally rapid and enables inference of rates that correlate well with previously published datasets. 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. Wildtype mouse embryonic stem cells (mESCs) were labeled with 4-thiouridine for 10 minutes. Biochemical separation was then used to separate the pool of transcripts containing 4sU (newly transcribed RNA) from the preexisting RNA. Newly transcribed and preexisting RNA were sequenced on Illumina HiSeq 2500.