Evidence map›Paper›PMID 42776688›Full record

ArticleMethods and protocols2026

LAWS-HiC: A Locally Adaptive Weighting and Screening (LAWS) Approach to Improve Detection of Long-Range Chromatin Interactions from Hi-C Data.

Lingbo Zhou, Chang Chen, Jane Zizhen Zhao, Ming Hu, Yun Li

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In one paragraph

Article in Methods and protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lingbo ZhouDepartment of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, USA.ORCID 0009-0006-7787-7742
Chang ChenDepartment of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, USA.ORCID 0009-0001-4136-0876
Jane Zizhen ZhaoDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, 235 East Cameron Avenue, Chapel Hill, NC 27599, USA.ORCID 0009-0002-8016-6141
Ming HuDepartment of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA.ORCID 0000-0003-0987-2916
Yun LiDepartment of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, USA.ORCID 0000-0002-9275-4189

Funding

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
NHGRI NIH HHS R35 HG011922NIDA NIH HHS U01 DA052713NIH HHS 5R35HG011922-05NIH HHS 5U01DA052713-05
6 · The paper itself

Abstract

Hi-C technologies are widely used to study genome-wide chromosome spatial organization. Among various Hi-C data analyses, detecting long-range chromatin interactions (i.e., 3D peak calling) is particularly critical due to its direct relevance to gene regulation. However, most existing peak callers fail to account for spatial dependencies in high-resolution (e.g., ≤10 kb) Hi-C data, often resulting in suboptimal accuracy. To address this limitation, we introduce LAWS-HiC, a novel computational method based on the Locally Adaptive Weighting and Screening (LAWS) approach. LAWS-HiC adjusts

Indexed as

3D genome organizationchromatin interactionsfalse discovery rateHi-Cpeak callingspatial multiple testingtopologically associating domains

Identifiers

PMID42776688
PMCPMC13600098

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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.