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INSPEcT-GUI Reveals the Impact of the Kinetic Rates of RNA Synthesis, Processing, and Degradation, on Premature and Mature RNA Species

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The abundance of RNA species and their response to perturbations are set by the kinetics rates of RNA synthesis, processing, and degradation. However, the visualization, interpretation, and manipulation of these data require familiarity with mathematical modeling and command line tools. INSPEcT-GUI is an R-Shiny interface that allows researchers without specific training to effortlessly explore how the fine kinetic regulation of the RNA life cycle can shape gene expression programs. In particular, it allows to: (i) interactively visualize gene-level RNA dynamics; (ii) refine the model fit of experimental data; (iii) test alternative regulatory models; (iv) explore, independently from the availability of data, how the combined action of the RNA kinetic rates impacts on premature and mature RNA. INSPEcT-GUI is freely available within the R/Bioconductor package INSPEcT at http://bioconductor.org/packages/INSPEcT/. An HTML vignette including documentation on the tool startup and usage, executable examples, and a video demonstration, are available at: http://bioconductor.org/packages/release/bioc/vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html.
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fgene-11-00759 July 15, 2020 Time: 17:7 # 1
TECHNOLOGY AND CODE
published: 17 July 2020
doi: 10.3389/fgene.2020.00759
Edited by:
Philipp Kapranov,
Huaqiao University, China
Reviewed by:
David Langenberger,
ecSeq Bioinformatics GmbH,
Germany
Tzu Pin Lu,
National Taiwan University, Taiwan
*Correspondence:
Stefano de Pretis
stefano.depretis@iit.it
Mattia Pelizzola
mattia.pelizzola@iit.it
Specialty section:
This article was submitted to
RNA,
a section of the journal
Frontiers in Genetics
Received: 07 May 2020
Accepted: 26 June 2020
Published: 17 July 2020
Citation:
de Pretis S, Furlan M and
Pelizzola M (2020) INSPEcT-GUI
Reveals the Impact of the Kinetic
Rates of RNA Synthesis, Processing,
and Degradation, on Premature
and Mature RNA Species.
Front. Genet. 11:759.
doi: 10.3389/fgene.2020.00759
INSPEcT-GUI Reveals the Impact of
the Kinetic Rates of RNA Synthesis,
Processing, and Degradation, on
Premature and Mature RNA Species
Stefano de Pretis*, Mattia Furlan and Mattia Pelizzola*
Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia, Milan, Italy
The abundance of RNA species and their response to perturbations are set by
the kinetics rates of RNA synthesis, processing, and degradation. However, the
visualization, interpretation, and manipulation of these data require familiarity with
mathematical modeling and command line tools. INSPEcT-GUI is an R-Shiny interface
that allows researchers without specific training to effortlessly explore how the fine kinetic
regulation of the RNA life cycle can shape gene expression programs. In particular, it
allows to: (i) interactively visualize gene-level RNA dynamics; (ii) refine the model fit of
experimental data; (iii) test alternative regulatory models; (iv) explore, independently from
the availability of data, how the combined action of the RNA kinetic rates impacts on
premature and mature RNA. INSPEcT-GUI is freely available within the R/Bioconductor
package INSPEcT at http://bioconductor.org/packages/INSPEcT/. An HTML vignette
including documentation on the tool startup and usage, executable examples, and a
video demonstration, are available at: http://bioconductor.org/packages/release/bioc/
vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html.
Keywords: transcription, mathematical modeling, graphical user interface (GUI), RNA synthesis, RNA processing,
RNA degradation
INTRODUCTION
The RNA life-cycle is composed by three main steps - the synthesis of premature RNA, its
processing into the mature form, and the degradation of the latter. The dynamics of transcripts
metabolism are set by the rates governing the kinetics of those steps, ultimately setting the
abundance of premature and mature RNA species (Shalem et al., 2008;Friedel et al., 2009;
Rabani et al., 2011;Eser et al., 2013;de Pretis et al., 2017), and shaping their temporal response
following a perturbation (Zeisel et al., 2011). The dynamics of a given transcript are described
by a system of ordinary differential equations, which includes the abundance of RNA species
(Pand M, the concentrations of premature and mature RNA) and the RNA kinetic rates
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de Pretis et al. INSPEcT-GUI RNA Kinetic Rates
(k13, the rates of RNA synthesis, processing, and degradation):
dP
dt
=k1k2·P(1)
dM
dt
=k2·P-k3·M
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. For example, how would a halved RNA
degradation impact on the expression of a gene? How would
the latter be affected if also the rate of RNA processing were
increased? A tool that enable scientists to explore and manipulate
these data, while not requiring specific training on mathematical
modeling or familiarity with command line scripting, is missing.
To lower the barrier to the field, we developed INSPEcT-
GUI, an R-Shiny interface that fully complements the analytical
framework implemented in INSPEcT (de Pretis et al., 2015).
INSPEcT-GUI greatly facilitates the visualization, interpretation
and manipulation of the RNA dynamics of individual genes,
and allows the user to explore de novo the role of the
RNA kinetic rates.
IMPLEMENTATION
The RNA kinetic rates (synthesis, processing, and degradation)
are modeled within INSPEcT-GUI with the following analytical
functions: constant, sigmoid, or impulse. Thus, one of these
functions, and the corresponding parameters, has to be
set for each rate.
At steady state, constant functions constrain the kinetic
rates to fixed values (Figures 1A,B), determining how a given
combination of rates set the steady-state abundance of RNA
species. Thus, the system in Eq. (1) reduces to:
P=
k1
k2
(2)
M=
k1
k3
Rather, when at least one of the three kinetic rates is set to
variable, the abundance of premature and mature RNA species
is determined through the numerical solution of the system of
FIGURE 1 | The functional forms used to parameterize transcriptional and post-transcriptional responses. (A) Legend for the terms used in the INSPEcT-GUI to
identify function’s parameters and the corresponding symbols in the figure. (B) Constant function. (C) Sigmoid function. (D) Impulse function.
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FIGURE 2 | Flowchart illustrating how INSPEcT-GUI integrates with and extend the functionalities implemented in INSPEcT. Black arrows trace the typical INSPEcT
pipeline, from raw RNA-seq data to an INSPEcT object containing, for each gene in the dataset, the modeled kinetic rates together with the goodness of fit statistics.
Dashed black arrows depict the Input of an INSPEcT object to INSPEcT-GUI and the output of the latter. Green arrows indicates how the GUI can be used, without
the need of any data, for the manipulation of model’s parameters to observe the effect on the profiles of RNA species and kinetic rates. Solid blue arrows indicate
how the GUI can be used to manipulate a model’s parameters, thus refining the INSPEcT model through the reassessment of goodness of fit statistics and
confidence intervals. Dashed blue arrows indicate how also the numerical optimization can optionally be updated. Red arrows indicate how the GUI can be used to
implement an alternative regulatory model (i.e., switching between the functions of Figure 1 for one of more rates), which is subsequently optimized and tested.
ordinary differential equations indicated in Eq. (1). Variable rates
can be described by sigmoid or impulse functions. Sigmoids
are S-shaped functions described by four parameters: starting
and final levels, time of transition between those, and slope
of the response (Figure 1C). Impulse functions allow a more
complex behavior, with two additional parameters that describe
time and levels of a second transition, possibly originating double
sigmoids or bell-shaped responses (Chechik and Koller, 2009)
(Figure 1D).
When INSPEcT-GUI is applied to RNA expression data, it
infers the parameters of the functional forms assigned to k13.
In the “user defined” mode the parameters of k13functions can
be freely assigned to model RNA kinetic rates and explore their
impact on premature and mature RNAs. Figure 2 illustrates the
analysis and statistical framework of INSPEcT, and depicts how
INSPEcT-GUI integrates with and extends the functionalities
implemented in INSPEcT.
Visualizing and Manipulating RNA
Dynamics Based on Experimental Data
INSPEcT modeling results for both steady state or time-course
experiments can be uploaded within INSPEcT-GUI for an
interactive visualization and for further analyses (Figure 3). To
facilitate the generation of the dataset, a wrapper function has
now been included in INSPEcT that allows running the tool
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FIGURE 3 | The interface of INSPEcT-GUI. The upper left part controls the upload of an INSPEcT dataset, generated with or without the profiling of nascent RNA,
and allows selecting a gene with a given regulatory model. In the lower left part, the user can choose whether to plot raw or smoothed data, and modeling statistics
are reported and updated in real time in case of changes. In the central part, the gene-level abundance of RNA species and the RNA kinetic rates are displayed. On
the right most part, the control panel allows setting the fitting of the model with specific functions.
with one single command, starting from BAM files or a single-
gene PCR data. 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.
INSPEcT-GUI allows controlling the INSPEcT analysis
framework to reassess and potentially refine the selected
regulatory model. Analyzed genes are divided according to
their modeled RNA dynamics, grouping together those that
have the same regulatory model. For example, “sd” stays for
variable synthesis, constant processing, and variable degradation
rates, while “sp” stays for variable synthesis, variable processing,
and constant degradation rates. Once a gene is selected, the
temporal or steady state profiles of premature and mature RNA
species are plotted, together with the corresponding profiles of
the kinetic rates.
All plots include standard deviation and/or 95% confidence
intervals, and experimental data can be smoothed. The functional
forms assigned to each kinetic rate, and the corresponding
parameter settings, are reported. Moreover, goodness of fit
statistics are indicated, expressed as two-tailed chi-squared test
p-value and Akaike information criterion. These metrics are
penalized for the model complexity, and can be used for the
comparative evaluation of alternative regulatory models (Browne
and Cudeck, 2016). Moreover, the chi-squared test p-value can be
used to assess whether the model under consideration adequately
explains the data. These metrics are evaluated in real-time,
helping the user assessing if a change in the model parameters
is lowering or increasing the ability to interpret the data.
The model fit can be reassessed and potentially improved by
providing additional minimization iterations, or by selecting a
different minimization algorithm [choosing among Nelder-Mead
(Lagarias et al., 2006) and BFGS (Browne and Cudeck, 2016)].
Finally, alternative regulatory models can be tested by
changing the functional forms assigned to one or more kinetic
rates, and/or by modifying the corresponding parameters.
Upon changes of functional forms or their parameters, the
corresponding plots and statistics are updated on real-time. This
allows the user to evaluate the impact and goodness of fit of
alternative regulatory models, without the necessity of explicitly
controlling the corresponding INSPEcT functionalities, which
are automatically exploited under the hood.
Exploring RNA Dynamics Without
Experimental Data: INSPEcT-GUI
Simulations
The previous section illustrated how INSPEcT-GUI can take
advantage of the INSPEcT analytical framework to explore or
manipulate RNA dynamics of individual genes. Alternatively,
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FIGURE 4 | Examples of temporal RNA dynamics produced by INSPEcT-GUI in the “user defined” mode. When INSPEcT-GUI is run in the “user defined” mode, the
impact of the RNA kinetic rates can be evaluated without the need of experimental data. Four examples are reported. (A) At steady-state constant RNA kinetic rates
set the abundance of RNA species, according to Eq. (2) (see text). (B) Given a constant rate of synthesis, premature RNA is reduced over time due to an increase in
the rate of RNA processing. (C) Given a constant rate of RNA processing, both premature and mature RNA species are increased over time due to an increase in the
rate of RNA synthesis. (D) As in (C) but concomitant with an increase in the rate of RNA degradation. As a result, the contrasting modulation of synthesis and
degradation rates levels the temporal profile of mature RNA.
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INSPEcT-GUI can be used to simulate how a given combination
of constant or variable RNA kinetic rates impact on the
temporal profiles of premature and mature RNAs. This
approach allows exploring the effect of complex transcriptional
and post-transcriptional regulations, without the need of
experimental data.
Using the same interface described in the previous section, and
depicted in Figure 3, a control panel allows defining the function
and setting the parameters for each kinetic rate. 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. (2); Figure 4A].
Figure 4B illustrates how the modulation of premature RNA
can be obtained independently from the transcriptional input,
i.e., despite a constant rate of RNA synthesis. Indeed, an
increasing rate of processing leads to lower level of premature
RNA. Noteworthy, mature RNA is only transiently affected
by the permanent change in processing dynamics. Figure 4C
shows how the modulation of the synthesis rate affects both
premature and mature RNA forms. Finally, Figure 4D illustrates
how the joint change of synthesis and degradation rates, if
modulated in the same direction, can lead to invariant levels of
mature RNA, while premature RNA abundance is permanently
increased. Various regulatory scenarios, originated through the
joint modulation of the kinetic rates, are illustrated in detail
in the online vignette of INSPEcT-GUI, and in the included
video demonstration.
DISCUSSION
The recent development of experimental and computational
methods able to dissect the dynamics of RNA metabolism is
allowing the study of layers of regulation that were previously
hard to characterize, which are revealing the detailed molecular
mechanisms at the basis of complex gene expression programs.
INSPEcT-GUI is seamlessly integrated with INSPEcT modeling
functionalities, covering steady state or time course studies with
or without the profiling of nascent RNA. Altogether, INSPEcT-
GUI facilitates the interactive visualization and manipulation
of gene level RNA dynamics, empowering scientists with no
specific training with the possibility of exploring the effects of the
combinatorial regulation of RNA kinetic rates.
DATA AVAILABILITY STATEMENT
INSPEcT-GUI is freely available within the R/Bioconductor
package INSPEcT at http://bioconductor.org/packages/
INSPEcT/. An HTML vignette including documentation
on the tool startup and usage, executable examples, and a
video demonstration, are available at: http://bioconductor.org/
packages/release/bioc/vignettes/INSPEcT/inst/doc/INSPEcT_
GUI.html.
AUTHOR CONTRIBUTIONS
SP and MP conceived the study. SP and MF developed the
software. All authors contributed discussing and writing the
manuscript.
REFERENCES
Browne, M. W., and Cudeck, R. (2016). Alternative ways of assessing model fit.
Sociol. Methods Res. 21, 230–258. doi: 10.1177/0049124192021002005
Chechik, G., and Koller, D. (2009). Timing of gene expression responses to
environmental changes. J. Comput. Biol. 16, 279–290. doi: 10.1089/cmb.2008.
13TT
de Pretis, S., Kress, T., Morelli, M. J., Melloni, G. E. M., Riva, L., Amati,
B., et al. (2015). INSPEcT: a computational tool to infer mRNA synthesis,
processing and degradation dynamics from RNA- and 4sU-seq time course
experiments. Bioinformatics 31, 2829–2835. doi: 10.1093/bioinformatics/
btv288
de Pretis, S., Kress, T. R., Morelli, M. J., Sabò, A., Locarno, C., Verrecchia, A.,
et al. (2017). Integrative analysis of RNA polymerase II and transcriptional
dynamics upon MYC activation. Genome Res. 27, 1658–1664. doi: 10.1101/gr.
226035.117
Dolken, L., Ruzsics, Z., Radle, B., Friedel, C. C., Zimmer, R., Mages, J., et al. (2008).
High-resolution gene expression profiling for simultaneous kinetic parameter
analysis of RNA synthesis and decay. RNA 14, 1959–1972. doi: 10.1261/rna.
1136108
Eser, P., Demel, C., Maier, K. C., Schwalb, B., Pirkl, N., Martin, D. E., et al. (2013).
Periodic mRNA synthesis and degradation co-operate during cell cycle gene
expression. Mol. Syst. Biol. 10:717. doi: 10.1002/msb.134886
Friedel, C. C., Dölken, L., Ruzsics, Z., Koszinowski, U. H., and Zimmer,
R. (2009). Conserved principles of mammalian transcriptional regulation
revealed by RNA half-life. Nucleic Acids Res. 37:e115. doi: 10.1093/nar/
gkp542
Furlan, M., de Pretis, S., Galeota, E., Caselle, M., and Pelizzola, M. (2019).
Dynamics of transcriptional regulation from total RNA-seq experiments.
bioRxiv [Preprint]. doi: 10.1101/520155
Gray, J. M., Harmin, D. A., Boswell, S. A., Cloonan, N., Mullen, T. E., Ling, J. J.,
et al. (2014). SnapShot-Seq: a method for extracting genome-wide, in vivo
mRNA dynamics from a single total RNA sample. PLoS One 9:e89673. doi:
10.1371/journal.pone.0089673
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., et al.
(2018). RNA velocity of single cells. Nature 560, 1–25. doi: 10.1038/s41586-018-
0414-6
Lagarias, J. C., Reeds, J. A., Wright, M. H., and Wright, P. E. (2006). Convergence
properties of the nelder–mead simplex method in low dimensions. SIAM J.
Optim. 9, 112–147. doi: 10.1137/S1052623496303470
Rabani, M., Levin, J. Z., Fan, L., Adiconis, X., Raychowdhury, R., Garber, M.,
et al. (2011). Metabolic labeling of RNA uncovers principles of RNA production
and degradation dynamics in mammalian cells. Nat. Biotechnol. 29, 436–442.
doi: 10.1038/nbt.1861
Rabani, M., Raychowdhury, R., Jovanovic, M., Rooney, M., Stumpo, D. J., Pauli,
A., et al. (2014). High-resolution sequencingand modeling identifies distinct
dynamic rna regulatory strategies. Cell 159, 1698–1710. doi: 10.1016/j.cell.2014.
11.015
Shalem, O., Dahan, O., Levo, M., Martinez, M. R., Furman, I., Segal, E., et al. (2008).
Transient transcriptional responses to stress are generated by opposing effects
of mRNA production and degradation. Mol. Syst. Biol. 4:223. doi: 10.1038/msb.
2008.59
Sun, M., Schwalb, B., Schulz, D., Pirkl, N., Etzold, S., Larivière, L., et al. (2012).
Comparative dynamic transcriptome analysis (cDTA) reveals mutual feedback
Frontiers in Genetics | www.frontiersin.org 6July 2020 | Volume 11 | Article 759
fgene-11-00759 July 15, 2020 Time: 17:7 # 7
de Pretis et al. INSPEcT-GUI RNA Kinetic Rates
between mRNA synthesis and degradation. Genome Res. 22, 1350–1359. doi:
10.1101/gr.130161.111
Uvarovskii, A., and Dieterich, C. (2017). pulseR: versatile computational
analysis of RNA turnover from metabolic labeling experiments.
Bioinformatics 33, 3305–3307. doi: 10.1093/bioinformatics/
btx368
Zeisel, A., Köstler, W. J., Molotski, N., Tsai, J. M., Krauthgamer, R., Jacob-Hirsch,
J., et al. (2011). Coupled pre-mRNA and mRNA dynamics unveil operational
strategies underlying transcriptional responses to stimuli. Mol. Syst. Biol. 7:529.
doi: 10.1038/msb.2011.62
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 de Pretis, Furlan and Pelizzola. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other forums is permitted, provided the
original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academicpractice. No
use, distribution or reproduction is permitted which does not comply with theseterms.
Frontiers in Genetics | www.frontiersin.org 7July 2020 | Volume 11 | Article 759
... Furthermore, computer algorithms based on RNA-Seq are still under continuous development. For example, INSPEcT [87] was recently designed to calculate RNA kinetic rates based on time course RNA-seq data, or to estimate stability by calculating the difference between premature and mature RNA expression [90]. Going forward, the stQTLs which are identi ed with more accurate mRNA stability pro les estimation may further our understanding of how genetic variants regulate gene expression. ...
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