Estimating gross transcription rates from RNA level fluctuation data and the effects of sampling time intervals

Transcription rates are key biological parameters, but the estimation of transcription rates from RNA level fluctuation data by current methods is still problematic, considering in particular the derived relationship between RNA fragments from different samples and the neglect of the effects of sampling time intervals. Based on defining the gross transcription rate as the amount of converted complete nascent RNA divided by time, the present study developed an algorithm that calculated the cumulative transcription amount and RNA abundance at each time point by simulating moving windows to estimate gross transcription rates from RNA level fluctuation data and explore the effects of sampling time intervals on the estimation. The results showed that the gross transcription rates could be calculated from RNA level fluctuation data with the models fitting the experimental data well. In the analysis of 384 yeast genes, the genes with the highest gross transcription rates mainly played roles in cell division regulation and DNA replication, and the gene utilizing the most cellular resources for gene expression during the experiment was YNR016c, whose main functions are fatty acid biosynthesis and transporting proteins into the nucleus. The shapes of the RNA level curves affected the estimation of gross transcription rates, and the crests and valleys of the RNA level curves responded to higher gross transcription rates. Different scenarios of sampling time intervals could change the shapes of the RNA level curves, resulting in different estimation values of gross transcription rates. Given the potential applications of the present method, further improvements are expected.