Evidence map›Paper›PMID 35413804›Full record

ArticleBMC genomics2022

GenomicDistributions: fast analysis of genomic intervals with Bioconductor.

Kristyna Kupkova, Jose Verdezoto Mosquera, Jason P Smith, Michał Stolarczyk, Tessa L Danehy, John T Lawson, Bingjie Xue, John T Stubbs, Nathan LeRoy, Nathan C Sheffield

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

19 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. mNature communications · 2026
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  11. Proceedings of the National Academy of Sciences of the United States of America · 2025
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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

10 authors at 2 institutions in 1 country.

Kristyna Kupkova *Center for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0002-6774-2709
Jose Verdezoto Mosquera *Center for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0002-2816-2250
Jason P SmithCenter for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0002-2688-0988
Michał StolarczykCenter for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0003-2101-9061
Tessa L DanehyCenter for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0003-3711-8697
John T LawsonCenter for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0002-2723-5972
Bingjie XueCenter for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0002-9947-6484
John T StubbsCenter for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0002-5575-952X
Nathan LeRoyCenter for Public Health Genomics, University of Virginia, Charlottesville, USA.ORCID http://orcid.org/0000-0002-7354-7213
Nathan C SheffieldCenter for Public Health Genomics, University of Virginia, Charlottesville, USA. nsheffield@virginia.edu.ORCID http://orcid.org/0000-0001-5643-4068
University of Virginia · USUniversity of Virginia Health System · US

Funding

A modular data analysis ecosystem using portable encapsulated projectsR35GM128636 · NIGMS · UNIVERSITY OF VIRGINIA · PI SHEFFIELD, NATHAN · 2018 to 2022
$1.9M
NIGMS NIH HHS GM128636NIGMS NIH HHS R35 GM128636
6 · The paper itself

Abstract

backgroundEpigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data.

resultsGenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs.

conclusionsGenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html ).

Indexed as

GenomicsSoftwareChromatin Immunoprecipitation SequencingEpigenomicsGenomeBioconductorData visualizationGenomic regionsRegion set summaryR package

Identifiers

PMID35413804
PMCPMC9003978
OpenAlexW4223538079

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.