Biology subjects

Stanley, J. T.

Publications and source records attributed to Stanley, J. T..

5 recordsLinked to original sources

bioRxiv2025

Base Composition Influences the Position andPrecision of RNA Polymerase II Disassociation inBasal and Perturbed Conditions.

RNA Polymerase II (Pol II) transcribes all protein-coding and many non-protein coding genes in the genome. Pol II transcription termination is crucial for mRNA maturation and, when disrupted, can lead to altered mRNA processing and mRNA export. Termination involves two intertwined processes: pre-mRNA cleavage and Pol II release from the DNA (disassociation). Despite its importance, the exact mechanisms underlying Pol II disassociation from the DNA remain poorly understood. Moreover, under certain cellular stress conditions, there is a partial failure of cleavage, leading to a shift of the position of disassociation further downstream, a phenomenon known as run-on transcription. We performed the first-ever systematic analysis of Pol II termination across cell types and species and provide novel insights into the mechanism of disassociation. Using a probabilistic mixture model to quantify Poll II dynamics across an entire gene body from nascent RNA sequencing data, we discovered that genes have two types of conserved regions near the disassociation site: one characterized by a T-rich region upstream of disassociation, and another characterized by a GC-rich region surrounding disassociation. Strikingly, the GC-rich disassociation regions have more accessible chromatin and higher levels of phospho-threonine 4 on the CTD of Pol II. Additionally, we find that upstream T-rich genes are preferentially affected by perturbations that alter disassociation, including heat-shock, viral infection, kinase inhibition, and arsenic treatment. Thus, our work has determined there are two types of Pol II disassociation regions, which are differentially affected by perturbation of cellular homeostasis.

genomics↗

bioRxiv2025

Improving confidence of differential transcription calls in enhancers

MotivationMost disease-associated genetic variants reside within transcribed regulatory elements (tREs). Patterns of differential transcription at tREs can be leveraged to identify upstream regulators and link enhancers to their target genes. But the low transcription levels and high variability in tREs makes identifying high confidence differentially transcribed elements challenging. ResultsWe present Mu Counts and TFEA-LE, two algorithms for robust identification of differentially transcribed tREs. The first step in accurate identification of differentially transcribed tREs is to obtain accurate RNA lengths and therefore counts over these regions. To this end we developed a method of accurate length inference (LIET-EMG) as wll as a rapid method for counting reads over tREs (Mu Counts). Armed with newly identified and quantified tREs, TFEA-LE then integrates motif information to simultaneously identify responsive tREs and their likely upstream regulators. We show improved precision and recall over general-purpose tools (e.g. DESeq2) in detecting p53-responsive tREs. We then clarify TF-specific responses within multi-TF perturbations in lung cells. Finally we show that the TFEA-LE approach improves TF activity inference, including in complex perturbations where many TFs respond. TFEALE is especially effective in technically challenging datasets, whether due to highly specific or broad responses, outliers, or high GC content. Ultimately, these 1 methods advance the systematic characterization of individual tREs, enabling their integration with regulators, target genes, and disease-associated variants for translational research. Availability and ImplementationTFEA-LE: https://github.com/Dowell-Lab/TFEA/tree/Lead_edge. Nextflow pipeline to run Mu Counts: https://github.com/Dowell-Lab/Bidir_Counting_Analysis. LIET (including modifications for tREs): https://github.com/Dowell-Lab/LIET/tree/LIET_EMGtoo. Source code for this work: https://github.com/Dowell-Lab/Improving_tRE_Analysis_Paper Contactrobin.dowell@colorado.edu

bioinformatics↗

bioRxiv2024

LIET Model: Capturing the kinetics of RNA polymerase from loading to termination

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 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 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 highly regulated process. Furthermore, 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.

bioinformatics↗

bioRxiv2023

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.

genomics↗

bioRxiv2020

Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment

1Detecting differential activation of transcription factors (TFs) in response to perturbation provides insight into cellular processes. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects differential activity of hundreds of TFs given any set of perturbation data. TFEA draws inspiration from GSEA and detects positional motif enrichment within a list of ranked regions of interest (ROIs). As ROIs are typically inferred from the data, 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 (eg. PRO-Seq), CAGE, ChIP-Seq, and accessibility (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.

bioinformatics↗