Abstract
Control of messenger RNA (mRNA) stability is an important aspect of gene regulation. The gold standard for measuring mRNA stability transcriptome-wide uses metabolic labeling, biochemical isolation of labeled RNA populations, and high-throughput sequencing. However, difficult normalization procedures have inhibited widespread adoption of this approach. Here, we present DRUID (for determination of rates using intron dynamics), a new computational pipeline that is robust, easy to use, and freely available. Our pipeline uses endogenous introns to normalize time course data and yields reproducible half-lives, even with data sets that were otherwise unusable. DRUID can handle data sets from a variety of organisms, spanning yeast to humans, and we even applied it retroactively on published data sets. We anticipate that DRUID will allow broad application of metabolic labeling for studies of transcript stability.