Evidence map›Paper›PMID 37067481›Full record

ArticleBioinformatics (Oxford, England)2023

excluderanges: exclusion sets for T2T-CHM13, GRCm39, and other genome assemblies.

Jonathan D Ogata, Wancen Mu, Eric S Davis, Bingjie Xue, J Chuck Harrell, Nathan C Sheffield, Douglas H Phanstiel, Michael I Love, Mikhail G Dozmorov

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
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

17 citing papers in PubMed.

  1. Machine learning reveals sequence and genomic context features underlyingbioRxiv : the preprint server for biology · 2026
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  15. Impact of genome build on RNA-seq interpretation and diagnostics.medRxiv : the preprint server for health sciences · 2024
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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

9 authors.

Jonathan D OgataDepartment of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, United States.
Wancen MuDepartment of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, United States.
Eric S DavisCurriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.ORCID 0000-0003-4051-3217
Bingjie XueCenter for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, United States.
J Chuck HarrellDepartment of Pathology, Virginia Commonwealth University, Richmond, VA 23284, United States.ORCID 0000-0003-3541-8418
Nathan C SheffieldCenter for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, United States.
Douglas H PhanstielCurriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.ORCID 0000-0003-2123-0051
Michael I LoveDepartment of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, United States.ORCID 0000-0001-8401-0545
Mikhail G DozmorovDepartment of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, United States.ORCID 0000-0002-0086-8358

Funding

Mechanisms of Dynamic Chromatin Looping During Differentiation - Common Fund Data SupplementR35GM128645 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Douglas H. Phanstiel · 2018 to 2026
$3.4M
NIGMS NIH HHS R35 GM128645
6 · The paper itself

Abstract

summaryExclusion regions are sections of reference genomes with abnormal pileups of short sequencing reads. Removing reads overlapping them improves biological signal, and these benefits are most pronounced in differential analysis settings. Several labs created exclusion region sets, available primarily through ENCODE and Github. However, the variety of exclusion sets creates uncertainty which sets to use. Furthermore, gap regions (e.g. centromeres, telomeres, short arms) create additional considerations in generating exclusion sets. We generated exclusion sets for the latest human T2T-CHM13 and mouse GRCm39 genomes and systematically assembled and annotated these and other sets in the excluderanges R/Bioconductor data package, also accessible via the BEDbase.org API. The package provides unified access to 82 GenomicRanges objects covering six organisms, multiple genome assemblies, and types of exclusion regions. For human hg38 genome assembly, we recommend hg38.Kundaje.GRCh38_unified_blacklist as the most well-curated and annotated, and sets generated by the Blacklist tool for other organisms. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/excluderanges/. Package website: https://dozmorovlab.github.io/excluderanges/.

Indexed as

Genome, HumanSoftwareAnimalsHumansMiceUncertainty

Identifiers

PMID37067481
PMCPMC10126321

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.