CV² and LS fits for the dispersion parameter <i>φ</i> for each dataset under raw and normalized conditions.

<p>R² values are also included for the quality of the corresponding fit to the raw data.</p

Comparing Beta-Binomial Dispersion with DESeq2 and EdgeR Dispersion Estimates.

<p>Each panel reflects one of the following datasets tested: LINCS UMI (A) and Gierlinski WT (B). The remaining two datasets are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157828#pone.0157828.s003" target="_blank">S3 Fig</a>. Each panel shows a density scatter plot of mean versus dispersion values for each gene in each sample. The black line represents our fit showing the non-asymptotic relationship between mean and variance (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157828#sec002" target="_blank">Methods</a>). The brown line shows the DESeq2 dispersion fit while the magenta line shows the EdgeR dispersion fit (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157828#sec002" target="_blank">Methods</a>).</p

Mean-Variance Relationship for Raw and Normalized mRNAseq Data.

<p>Each column of three panels reflects one of the following datasets tested: LINCS UMI (A-B) and Gierlinski WT (D-F). The two remaining datasets are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157828#pone.0157828.s002" target="_blank">S2 Fig</a>. For each column of three panels, the first panel (A,D) shows the CV² fit (solid blue line) and Least Squares fit (dashed green line) to the raw data points plotting mean vs variance (black x’s). The second panel (B,E) shows the same fits for the normalized data. The third panel (C,F) shows the respective R² values for the CV² and Least Squares (LS) fits for the raw and normalized data.</p

Differential p-values for Negative Binomial vs. Beta-Binomial Dispersion Methods.

<p>Each panel reflects a comparison of p-values for beta binomial-based dispersion or negative binomial-based dispersion generated from the UMI count data, CTRL vs SOR (A-C), or the Gierlinski data, WT vs ∆snf2 (D-F). Each panel is a scatter plot of the base-10 logarithm of the maximum normalized mean (maximum of the CTRL mean or SOR mean for UMI or the WT mean or ∆snf2 mean for Gierlinski) against the difference in base-10 logarithm of the corresponding p-values being compared for each gene. Color indicates density of points. The top row compares the beta binomial formulation versus DESeq2 (A,D). The second row compares beta binomial versus EdgeR (B,E). The third row compares EdgeR and DESeq2 (C,F).</p

Estimated α and β values Plotted Against the Mean for each Gene.

<p>Each panel is a log-scale scatter plot of mean vs α and β over all genes for one of the following datasets tested: LINCS UMI (A) and Gierlinski WT (B). The results for the two remaining datasets are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157828#pone.0157828.s001" target="_blank">S1 Fig</a>. The x’s reflect α values and the circles reflect β values with color dependent upon the density of points in the scatter plot.</p

Schematic of the General mRNAseq Process.

<p>There are three main steps depicted here, from top to bottom. First is obtaining RNA samples, which contain full length transcripts. Different samples are denoted by different color circles, and transcripts by straight lines within those circles. We highlight one transcript blue to enable following it through the process. Next, library preparation converts the transcripts in each sample to a library of fragments that can be sequenced. Finally, the libraries are sequenced by choosing fragments from the library, and the number of reads that align to particular transcripts are counted for the readout of expression.</p

Comparing Influence of Two RNA Effects on Averaged Assembly Rate.

Simulated light scattering curves for CCMV capsid assembly under all combinations of two RNA effects as well as the hollow capsid and combined RNA effects case. Fig 7A shows the entire simulation time course while Fig 7B shows the first five seconds. Time on the x axis is shown on a log scale.</p

Modeling Effects of RNA on Capsid Assembly Pathways via Coarse-Grained Stochastic Simulation

<div><p>The environment of a living cell is vastly different from that of an in vitro reaction system, an issue that presents great challenges to the use of in vitro models, or computer simulations based on them, for understanding biochemistry in vivo. Virus capsids make an excellent model system for such questions because they typically have few distinct components, making them amenable to in vitro and modeling studies, yet their assembly can involve complex networks of possible reactions that cannot be resolved in detail by any current experimental technology. We previously fit kinetic simulation parameters to bulk in vitro assembly data to yield a close match between simulated and real data, and then used the simulations to study features of assembly that cannot be monitored experimentally. The present work seeks to project how assembly in these simulations fit to in vitro data would be altered by computationally adding features of the cellular environment to the system, specifically the presence of nucleic acid about which many capsids assemble. The major challenge of such work is computational: simulating fine-scale assembly pathways on the scale and in the parameter domains of real viruses is far too computationally costly to allow for explicit models of nucleic acid interaction. We bypass that limitation by applying analytical models of nucleic acid effects to adjust kinetic rate parameters learned from in vitro data to see how these adjustments, singly or in combination, might affect fine-scale assembly progress. The resulting simulations exhibit surprising behavioral complexity, with distinct effects often acting synergistically to drive efficient assembly and alter pathways relative to the in vitro model. The work demonstrates how computer simulations can help us understand how assembly might differ between the in vitro and in vivo environments and what features of the cellular environment account for these differences.</p></div

Comparing Influence of Individual RNA Effects on Averaged Assembly Rate.

Simulated light scattering curves for CCMV capsid assembly under each individual RNA effect as well as the hollow capsid and combined RNA effects case. Fig 6A shows the entire simulation time course while Fig 6B shows the first second. Time on the x axis is shown on a log scale.</p

Frequency matrix plots for representative combinations of RNA effects.

Frequency matrix plots for (a) hollow CCMV capsid assembly, (b) CCMV capsid assembly with all combined RNA effects, (c) CCMV capsid assembly under both negative RNA effects (1100), and (d) CCMV capsid assembly under both positive RNA effects. In each plot, each row corresponds to a product size and each column to reactant sizes that produce that product. Pixel color in each position corresponds to the frequency with which the given reactant size is used to produce the given product size. Insets within each plot expand the upper-left corner of the main plot, corresponding to products of size 20 or smaller, to better visualize pathways involved in production of small oligomers.</p