Supplemental Material for Newman et al., 2018

<div>Supplementary figures, tables and files for Newman et. al, 2018, "Event Analysis: using transcript events to improve estimates of abundance in RNA-seq data", G3, manuscript# G3/2018/200373R1.</div><div><br></div><div>Additional File 1 contains Supplementary Figures 1-6 and Supplementaty Table 1</div><div><br></div><div>Supplementary File 1 contains the results of simulations to compare the Event Analysis approach to STAR (for junction detection) and iReckon (for transcript identification).</div><div><br></div><div>Supplementary File 2 contains the results of applying Event Analysis to eXpress, and the results of using iReckon to identify possible transcripts in the mouse neural data used in the study.<br></div><div><br></div><div>Supplementary File 3 contains the results of comparing Event Analysis (using Bowtie or SOAP2 as the aligner) against STAR for benchmarking junction detection using the mouse neural data. Results are benchmarked against the set of junctions observed in PacBio-sequenced transcripts in the mouse neural data.<br></div><div><br></div><div>Supplementary File 4 contains the comparison between Event Analysis and iReckon for the mouse neural data, benchmarked against PacBio-sequenced transcripts.</div

Supplemental Material for Newman et al., 2018

Comparison of Event analysis with RSEM to Event Analysis with eXpress and these approaches to transcript estimation with iRecko

Observed and expected PTVs in the study population.

<p><b>A</b>: Fraction of genes with at least one stop-gain or frameshift variant as a function of the number of sampled PTVs. The gray curve shows the expected number of genes under a model of neutral de novo mutation rate [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004647#pcbi.1004647.ref012" target="_blank">12</a>] representing the null hypothesis (no deleterious effects). The green curve shows the number of genes observed with at least one PTV. The orange curve limits the number of observed genes to those hosting highly damaging variants [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004647#pcbi.1004647.ref013" target="_blank">13</a>]. The purple curve shows the predicted number of genes with at least one PTV under the estimated best-fit parameters under model A–bootstrap replicas of this fit is shown by pale gray (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004647#sec007" target="_blank">Methods</a>). <b>B</b>: Extrapolation of the observed number of genes with at least one PTV assuming a model that includes the possibility of finding PTVs due to biological and technical noise. The purple curve shows the predicted number of genes with at least one PTV under the estimated best-fit parameters, while the green curve shows the observed data. Decomposition of the observed and predicted number of genes with at least one PTV: variants in non-haploinsufficient genes (blue) saturate early; variants found in haploinsufficient genes (red) continue to accumulate PTVs due to the constant contribution of biological and technical noise.</p