Evidence map›Paper›PMID 30795793›Full record

ArticleEpigenetics & chromatin2019

Combined analysis of dissimilar promoter accessibility and gene expression profiles identifies tissue-specific genes and actively repressed networks.

Rebekah R Starks, Anilisa Biswas, Ashish Jain, Geetu Tuteja

Open access · goldAbstract read
In one paragraph

Article in Epigenetics & chromatin, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 1 pooled it
17.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

67 citing papers in PubMed, 1 synthesis or guideline pooled it, 106 citations in OpenAlex.

  1. Context-Dependent Associations of theInternational journal of molecular sciences · 2026
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  11. Mitigating Cell Cycle Effects in Multi-Omics Data: Solutions and Analytical Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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7 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Rebekah R StarksGenetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
Anilisa BiswasGenetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
Ashish JainGenetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
Geetu TutejaGenetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA. geetu@iastate.edu.
Iowa State University · US

Funding

Transcriptional regulatory networks govering placental development.R01HD096083 · NICHD · IOWA STATE UNIVERSITY · PI TUTEJA, GEETU · 2018 to 2022
$1.5M
The role of HIF regulated genes and genomic regions in trophoblast invasionR00HD079545 · NICHD · IOWA STATE UNIVERSITY · PI TUTEJA, GEETU · 2015 to 2017
$745k
NICHD NIH HHS R00 HD079545NICHD NIH HHS R00HD079545NICHD NIH HHS R01 HD096083NICHD NIH HHS R01HD096083
6 · The paper itself

Abstract

backgroundThe assay for transposase-accessible chromatin (ATAC-seq) is a powerful method to examine chromatin accessibility. While many studies have reported a positive correlation between gene expression and promoter accessibility, few have investigated the genes that deviate from this trend. In this study, we aimed to understand the relationship between gene expression and promoter accessibility in multiple cell types while also identifying gene regulatory networks in the placenta, an understudied organ that is critical for a successful pregnancy.

resultsWe started by assaying the open chromatin landscape in the mid-gestation placenta, when the fetal vasculature has started developing. After incorporating transcriptomic data generated in the placenta at the same time point, we grouped genes based on their expression levels and ATAC-seq promoter coverage. We found that the genes with the strongest correlation (high expression and high coverage) are likely involved in housekeeping functions, whereas tissue-specific genes were highly expressed and had only medium-low coverage. We also predicted that genes with medium-low expression and high promoter coverage were actively repressed. Within this group, we extracted a protein-protein interaction network enriched for neuronal functions, likely preventing the cells from adopting a neuronal fate. We further confirmed that a repressive histone mark is bound to the promoters of genes in this network. Finally, we ran our pipeline using ATAC-seq and RNA-seq data generated in ten additional cell types. We again found that genes with the strongest correlation are enriched for housekeeping functions and that genes with medium-low promoter coverage and high expression are more likely to be tissue-specific. These results demonstrate that only two data types, both of which require relatively low starting material to generate and are becoming more commonly available, can be integrated to understand multiple aspects of gene regulation.

conclusionsWithin the placenta, we identified an active placenta-specific gene network as well as a repressed neuronal network. Beyond the placenta, we demonstrate that ATAC-seq data and RNA-seq data can be integrated to identify tissue-specific genes and actively repressed gene networks in multiple cell types.

Indexed as

Chromatin Assembly and DisassemblyGene Regulatory NetworksPromoter Regions, GeneticAnimalsFemaleGene Expression ProfilingHistone CodeMiceNeuronsOrgan SpecificityPlacentaPregnancyRNA, MessengerRNA, MessengerATAC-seqGene networksGene regulationPromoter coverageRNA-seqTissue-specific genes

Identifiers

PMID30795793
PMCPMC6385419
OpenAlexW2917237286

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.