Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries.

BACKGROUND: A variety of protocols exist for producing whole genome run-on transcription datasets. However, little is known about how differences between these protocols affect the signal within the resulting libraries. RESULTS: Using run-on transcription datasets generated from the same biological system, we show that a variety of GRO- and PRO-seq preparation methods leave identifiable signatures within each library. Specifically we show that the library preparation method results in differences in quality control metrics, as well as differences in the signal distribution at the 5 ' end of transcribed regions. These shifts lead to disparities in eRNA identification, but do not impact analyses aimed at inferring the key regulators involved in changes to transcription. CONCLUSIONS: Run-on sequencing protocol variations result in technical signatures that can be used to identify both the enrichment and library preparation method of a particular data set. These technical signatures are batch effects that limit detailed comparisons of pausing ratios and eRNAs identified across protocols. However, these batch effects have only limited impact on our ability to infer which regulators underlie the observed transcriptional changes.

A transcription factor (TF) inference method that broadly measures TF activity and identifies mechanistically distinct TF networks.

TF profiler is a method of inferring transcription factor regulatory activity, i.e. when a TF is present and actively regulating transcription, directly directly from nascent sequencing assays such as PRO-seq and GRO-seq. Transcription factors orchestrate transcription and play a critical role in cellular maintenance, identity and response to external stimuli. While ChIP assays have measured DNA localization, they fall short of identifying when and where transcription factors are actively regulating transcription. Our method, on the other hand, uses RNA polymerase activity to infer TF activity across hundreds of data sets and transcription factors. Based on these classifications we identify three distinct classes of transcription factors: ubiquitous factors that play roles in cellular homeostasis, driving basal gene programs across tissues and cell types, tissue specific factors that act almost exclusively at enhancers and are themselves regulated at transcription, and stimulus responsive TFs which are regulated post-transcriptionally but act predominantly at enhancers. TF profiler is broadly applicable, providing regulatory insights on any PRO-seq sample for any transcription factor with a known binding motif.

Post-meiotic mechanism of facultative parthenogenesis in gonochoristic whiptail lizard species.

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 gonochoristic Aspidoscelis species A. marmoratus and A. arizonae results in genome-wide homozygosity, an observation inconsistent with polar body fusion as the underlying mechanism of restoration. Instead, a high-quality reference genome for A. marmoratus and 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.

Atlas of nascent RNA transcripts reveals enhancer to gene linkages.

Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in unstable, non-coding bidirectional transcription. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as an SQL database which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.

Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment.

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 introduce muMerge, 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.

Pof8 is a La-related protein and a constitutive component of telomerase in fission yeast.

Telomerase reverse transcriptase (TERT) and the non-coding telomerase RNA subunit (TR) constitute the core of telomerase. Here we now report that the putative F-box protein Pof8 is also a constitutive component of active telomerase in fission yeast. Pof8 functions in a hierarchical assembly pathway by promoting the binding of the Lsm2-8 complex to telomerase RNA, which in turn promotes binding of the catalytic subunit. Loss of Pof8 reduces TER1 stability, causes a severe assembly defect, and results in critically short telomeres. Structure profile searches identified similarities between Pof8 and telomerase subunits from ciliated protozoa, making Pof8 next to TERT the most widely conserved telomerase subunits identified to date.