Evidence map›Paper›PMID 32211031›Full record

ArticleFrontiers in genetics2020

Testing Proximity of Genomic Regions to Transcription Start Sites and Enhancers Complements Gene Set Enrichment Testing.

Christopher Lee, Kai Wang, Tingting Qin, Maureen A Sartor

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 10 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Christopher LeeDepartment of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI, United States.
Kai WangDepartment of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI, United States.
Tingting QinDepartment of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI, United States.
Maureen A SartorDepartment of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI, United States.
University of Michigan–Ann Arbor · US

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 1988 to 2026
$178.2M
Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Dana Dolinoy · 2011 to 2026
$21.3M
Strategies to Overcome Immune Resistance in Head and Neck CancersP01CA240239 · NCI · YALE UNIVERSITY · PI PAI, SARA ISABEL · 2019 to 2023
$8.4M
NCI NIH HHS P01 CA240239NCI NIH HHS P30 CA046592NIEHS NIH HHS P30 ES017885
6 · The paper itself

Abstract

Large sets of genomic regions are generated by the initial analysis of various genome-wide sequencing data, such as ChIP-seq and ATAC-seq experiments. Gene set enrichment (GSE) methods are commonly employed to determine the pathways associated with them. Given the pathways and other gene sets (e.g., GO terms) of significance, it is of great interest to know the extent to which each is driven by binding near transcription start sites (TSS) or near enhancers. Currently, no tool performs such an analysis. Here, we present a method that addresses this question to complement GSE methods for genomic regions. Specifically, the new method tests whether the genomic regions in a gene set are significantly closer to a TSS (or to an enhancer) than expected by chance given the total list of genomic regions, using a non-parametric test. Combining the results from a GSE test with our novel method provides additional information regarding the mode of regulation of each pathway, and additional evidence that the pathway is truly enriched. We illustrate our new method with a large set of ENCODE ChIP-seq data, using the

Indexed as

ChIP-seq data analysisgene set enrichment testgenomic regionsnon-parametric testpathway analysis

Identifiers

PMID32211031
PMCPMC7069355
OpenAlexW3009317529

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.