Evidence map›Paper›PMID 39222061›Full record

ArticleBriefings in bioinformatics2024

SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model.

Weifang Liu, Wujuan Zhong, Paola Giusti-Rodríguez, Zhiyun Jiang, Geoffery W Wang, Huaigu Sun, Ming Hu, Yun Li

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

8 authors.

Weifang LiuDepartment of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States.ORCID 0000-0003-2278-8946
Wujuan ZhongBiostatistics and Research Decision Sciences, Merck & Co., Inc., 126 East Lincoln Ave, Rahway, New Jersey 07065, United States.
Paola Giusti-RodríguezDepartment of Psychiatry, University of Florida, 1149 Newel Dr., Gainesville, FL 32611, United States.
Zhiyun JiangDepartment of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States.
Geoffery W WangDepartment of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States.
Huaigu SunDepartment of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States.
Ming HuDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44196, United States.ORCID 0000-0003-0987-2916
Yun LiDepartment of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States.ORCID 0000-0002-9275-4189

Funding

Preclinical CoreP50HD103573 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GABRIEL S DICHTER · 2020 to 2026
$9.7M
Beyond GWAS: High Throughput Functional Genomics & Epigenome Editing to Elucidate the Effects of Genetic Associations for SchizophreniaR01MH125236 · NIMH · DUKE UNIVERSITY · PI CRAWFORD, GREGORY E, GERSBACH, CHARLES A. · 2021 to 2025
$8.1M
Charting the 3D epigenome in human brain development and diseasesU01DA052713 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KRIEGSTEIN, ARNOLD, SHEN, YIN · 2020 to 2024
$3.4M
Model-based methods for single cell chromatin interactomic dataR35HG011922 · NHGRI · CLEVELAND CLINIC LERNER COM-CWRU · PI HU, MING · 2021 to 2025
$2.4M
NICHD NIH HHS P50 HD103573NIDA NIH HHS U01 DA052713NIH HHS R35HG011922NIMH NIH HHS R01 MH125236
6 · The paper itself

Abstract

Harnessing the power of single-cell genomics technologies, single-cell Hi-C (scHi-C) and its derived technologies provide powerful tools to measure spatial proximity between regulatory elements and their target genes in individual cells. Using a global background model, we propose SnapHiC-G, a computational method, to identify long-range enhancer-promoter interactions from scHi-C data. We applied SnapHiC-G to scHi-C datasets generated from mouse embryonic stem cells and human brain cortical cells. SnapHiC-G achieved high sensitivity in identifying long-range enhancer-promoter interactions. Moreover, SnapHiC-G can identify putative target genes for noncoding genome-wide association study (GWAS) variants, and the genetic heritability of neuropsychiatric diseases is enriched for single-nucleotide polymorphisms (SNPs) within SnapHiC-G-identified interactions in a cell-type-specific manner. In sum, SnapHiC-G is a powerful tool for characterizing cell-type-specific enhancer-promoter interactions from complex tissues and can facilitate the discovery of chromatin interactions important for gene regulation in biologically relevant cell types.

Indexed as

Enhancer Elements, GeneticGenome-Wide Association StudyPolymorphism, Single NucleotidePromoter Regions, GeneticSingle-Cell AnalysisAnimalsComputational BiologyGenomicsHumansMiceenhancer–promoter interactionsepigeneticsGWASHi-Csingle cell

Identifiers

PMID39222061
PMCPMC11367764

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Registered trials

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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.