Evidence map›Paper›PMID 32424124›Full record

ArticleNature communications2020

Determinants of transcription factor regulatory range.

Chen-Hao Chen, Rongbin Zheng, Collin Tokheim, Xin Dong, Jingyu Fan, Changxin Wan, Qin Tang, Myles Brown, Jun S Liu, Clifford A Meyer and 1 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
4.3field-weighted citation impact, top 4% 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

44 citing papers in PubMed, 89 citations in OpenAlex.

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  10. Altered histone modifications inbioRxiv : the preprint server for biology · 2025
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  15. p53motifDB: integration of genomic information and tumour suppressor p53 binding motifs.Database : the journal of biological databases and curation · 2025
    Article
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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

11 authors at 3 institutions in 2 countries.

Chen-Hao ChenDepartment of Data Sciences, Dana-Farber Cancer Institute. Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Rongbin ZhengClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
Collin TokheimDepartment of Data Sciences, Dana-Farber Cancer Institute. Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Xin DongClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
Jingyu FanClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
Changxin WanClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.ORCID http://orcid.org/0000-0001-7345-8221
Qin TangCenter for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
Myles BrownCenter for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
Jun S LiuDepartment of Statistics, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-4450-7239
Clifford A MeyerDepartment of Data Sciences, Dana-Farber Cancer Institute. Harvard T.H. Chan School of Public Health, Boston, MA, USA. cliff_meyer@ds.dfci.harvard.edu.
X Shirley LiuDepartment of Data Sciences, Dana-Farber Cancer Institute. Harvard T.H. Chan School of Public Health, Boston, MA, USA. xsliu@ds.dfci.harvard.edu.
Harvard University · USTongji University · CNDana-Farber Cancer Institute · US

Funding

EDAC: ENCODE Data Analysis CenterU24HG009446 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GERSTEIN, MARK BENDER, WENG, ZHIPING · 2017 to 2022
$10.4M
Developing Informatics Technologies to Model Cancer Gene RegulationU24CA237617 · NCI · DANA-FARBER CANCER INST · PI MEYER, CLIFFORD · 2019 to 2023
$3.6M
NCI NIH HHS U24 CA237617NHGRI NIH HHS U24 HG009446
6 · The paper itself

Abstract

Characterization of the genomic distances over which transcription factor (TF) binding influences gene expression is important for inferring target genes from TF chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here we systematically examine the relationship between thousands of TF and histone modification ChIP-seq data sets with thousands of gene expression profiles. We develop a model for integrating these data, which reveals two classes of TFs with distinct ranges of regulatory influence, chromatin-binding preferences, and auto-regulatory properties. We find that the regulatory range of the same TF bound within different topologically associating domains (TADs) depend on intrinsic TAD properties such as local gene density and G/C content, but also on the TAD chromatin states. Our results suggest that considering TF type, binding distance to gene locus, as well as chromatin context is important in identifying implicated TFs from GWAS SNPs.

Indexed as

Gene Expression RegulationAcetylationAnimalsCell LineChromatinGenome-Wide Association StudyHistonesLysineMiceModels, GeneticPolymorphism, Single NucleotideProtein BindingQuantitative Trait LociTranscription FactorsTranscription Initiation SiteChromatinHistonesLysineTranscription Factors

Identifiers

PMID32424124
PMCPMC7235260
OpenAlexW3024498749

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.