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Abstract Transcription by RNA polymerases is an exquisitely regulated step of the central dogma. Transcription is the primary determinant of cell-state, and most cellular perturbations impact transcription by altering polymerase activity. Thus, detecting changes in polymerase activity yields insight into most cellular processes. Nascent run-on sequencing provides a direct readout of polymerase activity, but no tools exist to model all aspects of this activity at genes. We focus on RNA polymerase II—responsible for transcribing protein-coding genes. We present the first model to capture the complete process of gene transcription. For individual genes, this model parameterizes each distinct stage of transcription—loading, initiation, elongation, and termination, hence LIET—in a biologically interpretable Bayesian mixture, which is applied to nascent run-on data. Our improved modeling of loading/initiation demonstrates these stages are characteristically different between sense and antisense strands. Applying LIET to 24 human cell-types, our analysis indicates the position of dissociation (the last step of termination) appears to be highly consistent, indicative of a tightly regulated process. Furthermore, by applying LIET to perturbation experiments, we demonstrate its ability to detect specific changes in pausing (5′ end), strand-bias, and dissociation location (3′ end)—opening the door to differential assessment of transcription at individual stages of individual genes. 

Facultative parthenogenesis (FP) has historically been regarded as rare in vertebrates, but in recent years incidences have been reported in a growing list of fish, reptile, and bird species. Despite the increasing interest in the phenomenon, the underlying mechanism and evolutionary implications have remained unclear. A common finding across many incidences of FP is either a high degree of homozygosity at microsatellite loci or low levels of heterozygosity detected in next-generation sequencing data. This has led to the proposal that second polar body fusion following the meiotic divisions restores diploidy and thereby mimics fertilization. Here, we show that FP occurring in the gonochoristicAspidoscelisspeciesA. marmoratusandA. arizonaeresults in genome-wide homozygosity, an observation inconsistent with polar body fusion as the underlying mechanism of restoration. Instead, a high-quality reference genome forA. marmoratusand analysis of whole-genome sequencing from multiple FP and control animals reveals that a post-meiotic mechanism gives rise to homozygous animals from haploid, unfertilized oocytes. Contrary to the widely held belief that females need to be isolated from males to undergo FP, females housed with conspecific and heterospecific males produced unfertilized eggs that underwent spontaneous development. In addition, offspring arising from both fertilized eggs and parthenogenetic development were observed to arise from a single clutch. Strikingly, our data support a mechanism for facultative parthenogenesis that removes all heterozygosity in a single generation. Complete homozygosity exposes the genetic load and explains the high rate of congenital malformations and embryonic mortality associated with FP in many species. Conversely, for animals that develop normally, FP could potentially exert strong purifying selection as all lethal recessive alleles are purged in a single generation. 

Abstract Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introducemuMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.