Combining signal and sequence to detect RNA polymerase initiation in ATAC-seq data

Tripodi, Ignacio J.; Chowdhury, Murad; Gruca, Margaret; Dowell, Robin D.

doi:10.1371/journal.pone.0232332

Cited by 3 publications

(2 citation statements)

References 23 publications

(31 reference statements)

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“…Likewise, H3K4me 1/2/3 has been shown to correlate with transcription levels 31 but flank the site of initiation 45 . Finally, as nearly all sites of RNA polymerase loading and initiation originate within open chromatin regions, ATAC-seq data (and related accessibility metrics) are also informative 45 but at lower positional precision and with more false positives (open regions without transcription) 67 .…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…To evaluate existing deconvolution methods on nascent sequencing data, we first identified a number of high quality nascent sequencing data sets from a variety of cell types (see Table 2.1). Samples were processed using a standardized analysis pipeline 23 which includes quality control, mapping and bidirectional transcript identification. These bidirectional transcripts originate from both gene start sites and regulatory elements such as enhancers (Figure 1A).…”

Section: Deconvolution On Annotated Genesmentioning

confidence: 99%

Deconvolution of Nascent Sequencing Data Using Transcriptional Regulatory Elements

Maas,

Sigauke,

Dowell

2023

Preprint

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The problem of microdissection of heterogeneous tissue samples is of great interest for both fundamental biology and biomedical research. Until now, microdissection in the form of supervised deconvolution of mixed sequencing samples has been limited to assays measuring gene expression (RNA-seq) or chromatin accessibility (ATAC-seq). We present here the first attempt at solving the supervised deconvolution problem for run-on nascent sequencing data (GRO-seq and PRO-seq), a readout of active transcription. Then, we develop a novel filtering method suited to the mixed set of promoter and enhancer regions provided by nascent sequencing, and apply best-practice standards from the RNA-seq literature, usingin-silicomixtures of cells. Using these methods, we find that enhancer RNAs are highly informative features for supervised deconvolution. In most cases, simple deconvolution methods perform better than more complex ones for solving the nascent deconvolution problem. Furthermore, undifferentiated cell types confound deconvolution of nascent sequencing data, likely as a consequence of transcriptional activity over the highly open chromatin regions of undifferentiated cell types. Our results suggest that while the problem of nascent deconvolution is generally tractable, stronger approaches integrating other sequencing protocols may be required to solve mixtures containing undifferentiated celltypes.

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Comprehensive Guide of Epigenetics and Transcriptomics Data Quality Control

Comendul,

Ruf-zamojski,

Ford

et al. 2024

Preprint

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Host response to environmental exposures such as pathogens and chemicals can cause modifications to the epigenome and transcriptome. Analysis of these modifications can reveal signatures with regards to the agent and timing of exposure. Exhaustive interrogation of the cascade of the epigenome and transcriptome requires analysis of disparate datasets from multiple assay types, often at single cell resolution, from the same biospecimen. Improved signature discovery has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. However, there remains a paucity of rigorous quality control standards of those datasets that reflect quality assurance of the underlying assay. This guide outlines a comprehensive suite of metrics that can be used to ensure quality from 11 different epigenetics and transcriptomics assays. Recommendations on mitigation approaches to address failed metrics and poor quality data are provided. The workflow consists of assessing dataset quality and reiterating benchwork protocols for improved results to generate accurate exposure signatures.

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Combining signal and sequence to detect RNA polymerase initiation in ATAC-seq data

Cited by 3 publications

References 23 publications

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

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

Deconvolution of Nascent Sequencing Data Using Transcriptional Regulatory Elements

Comprehensive Guide of Epigenetics and Transcriptomics Data Quality Control

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