Characterization of steady-state RNA dynamics: reanalysis of 620 RNA-seq datasets. (A) Median abundances of premature and mature RNAs per gene were compared for protein-coding (top panel), pseudo-(middle) and long non-coding genes (bottom). Density scatter plot were fitted with a linear model, whose slope is reported. (B) Heatmaps displaying the degree of post-transcriptional regulation for each gene (row) in each sample (column). The ratio between premature and mature RNA abundance for each genes in each sample were determined and compared to the global trend depicted in (A). Each gene is either not expressed (blue), not differentially post-transcriptional regulated (white; ratio between the dashed lines in (A)), or differentially post-transcriptional regulated (red; ratio above the dashed lines). Above the heatmaps, two colourbars indicate the tissue-type and disease conditions of each sample.

Characterization of steady-state RNA dynamics: reanalysis of 620 RNA-seq datasets. (A) Median abundances of premature and mature RNAs per gene were compared for protein-coding (top panel), pseudo-(middle) and long non-coding genes (bottom). Density scatter plot were fitted with a linear model, whose slope is reported. (B) Heatmaps displaying the degree of post-transcriptional regulation for each gene (row) in each sample (column). The ratio between premature and mature RNA abundance for each genes in each sample were determined and compared to the global trend depicted in (A). Each gene is either not expressed (blue), not differentially post-transcriptional regulated (white; ratio between the dashed lines in (A)), or differentially post-transcriptional regulated (red; ratio above the dashed lines). Above the heatmaps, two colourbars indicate the tissue-type and disease conditions of each sample.

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The kinetic rates of RNA synthesis, processing and degradation determine the dynamics of transcriptional regulation by governing both the abundance and the responsiveness to modulations of premature and mature RNA species. The study of RNA dynamics is largely based on the integrative analysis of total and nascent transcription, with the latter bein...

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... the recount2 project 24 , thus minimizing potential batch effects due to different analysis pipelines and normalization methods. What we found is that the amount of premature RNA increases with the abundance of mature RNA following a power-law that is substantially different depending on the type of gene: protein coding, pseudo or long non-coding (Fig. 4A). Significant deviations from these trends point to post-transcriptionally regulated ...
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... post-transcriptional regulation heatmaps revealed that changes in the k 2 /k 3 ratio automatically grouped together samples from similar types of tissues and diseases (Fig. 4B, Supplementary Fig. 4). This suggested that post-transcriptional regulation is coordinated across similar conditions, and revealed that some types of cells have a tendency to be markedly subjected to post-transcriptional regulation (Supplementary Fig. 5). Interestingly, ~30% of the information contained in the clustering derived from ...
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... post-transcriptional regulation heatmaps revealed that changes in the k 2 /k 3 ratio automatically grouped together samples from similar types of tissues and diseases (Fig. 4B, Supplementary Fig. 4). This suggested that post-transcriptional regulation is coordinated across similar conditions, and revealed that some types of cells have a tendency to be markedly subjected to post-transcriptional regulation (Supplementary Fig. 5). ...
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... confirmation of the consistency of our method came from the finding that several classes of miRNA targets were significantly enriched in genes found to be post-transcriptionally regulated (see methods). The frequency of post-transcriptional regulation varied significantly, with protein coding and pseudo genes being more regulated than non-coding (Fig. 4B). The 1000 protein-coding genes with the lowest frequencies of post-transcriptional regulation were found to be associated with basic cellular processes such as protein folding, organelle organization and metabolic processes. On the contrary, the 1000 genes with the highest frequencies turned out to be related either to various ...
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... copyright holder for this preprint . http://dx.doi.org/10.1101/520155 doi: bioRxiv preprint first posted online Jan. 14, 2019; The role of processing dynamics in the responsiveness of mature RNA The integrative analysis of premature and mature RNA species can provide information on the post-transcriptional dynamics (Fig. 4), and, more specifically, on the influence that processing rates can have on RNA responsiveness. Indeed, while RNA stability is currently considered to be the major determinant of responsiveness 3 , our analyses indicate that this can also be affected by RNA processing (Fig. ...
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... conditions exist where RNA maturation can be slowed down to the point of becoming an impediment to the fast response required in cases such as stress response. This prompted us to characterize this phenomenon in various cell types and disease conditions. Median í µí¼ and í µí»¥ were quantified for each sample in the dataset examined in Fig. 4. Tissue types whose samples had similar metrics were highlighted and colour coded for three classes of genes ( Fig. 5D-F). In comparison to protein-coding and pseudo genes, long non coding RNAs (lncRNAs) had higher metrics overall. For all three classes of genes, samples from smooth muscle, immune system, CD19+ B-cells, kidney and ...

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... This model is implemented, with various assumptions, by different tools [cDTA (Sun et al., 2012), DRiLL (Rabani et al., 2014), INSPEcT (de Pretis et al., 2015) and pulseR (Uvarovskii and Dieterich, 2017)], which rely on the quantification of both nascent and total RNA species, the former profiled through RNA metabolic labeling (Dolken et al., 2008). Recently, novel approaches are being developed that do not require the quantification of nascent RNA, to estimate the full set (Furlan et al., 2019), or a subset of the kinetic rates (Zeisel et al., 2011;Gray et al., 2014;La Manno et al., 2018). Despite the availability of these tools, anticipating the outcome of the joint contribution of various RNA life-cycle stages can be far from trivial. ...
... The INSPEcT object returned by the wrapper is ready to be imported in INSPEcT-GUI. Notably, INSPEcT can quantify the RNA kinetic rates without requiring the profiling of nascent RNA (Furlan et al., 2019), and these datasets are fully supported by INSPEcT-GUI. ...
... The results are updated in real time. Few output examples are reported here, illustrating that predicting the temporal pattern of the RNA species following a change in the kinetic rates is often non trivial, as it depends on both the rates' absolute value, and the magnitude and shape of their modulation (Furlan et al., 2019). Constant kinetic rates determine flat temporal profiles of premature and mature RNA, whose abundance is set according to the magnitude of the kinetic rates [Eq. ...
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