Evidence map›Paper›PMID 38711367›Full record

ArticleBriefings in bioinformatics2024

Removing unwanted variation between samples in Hi-C experiments.

Kipper Fletez-Brant, Yunjiang Qiu, David U Gorkin, Ming Hu, Kasper D Hansen

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 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.

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  7. Mapping the 3D genome architecture.Computational and structural biotechnology journal · 2025
    Review
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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

5 authors.

Kipper Fletez-BrantMcKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
Yunjiang QiuBioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.
David U GorkinLudwig Institute for Cancer Research, New York, NY 10016, USA.
Ming HuDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44196, USA.
Kasper D HansenMcKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.

Funding

San Diego Center for 4D Nucleome ResearchU54DK107977 · NIDDK · LUDWIG INSTITUTE FOR CANCER RES LTD · PI MURRE, CORNELIS, REN, BING · 2015 to 2019
$9.4M
Cancer Genomics:Integrative and Scalable Solutions in R / BioconductorU24CA180996 · NCI · ROSWELL PARK CANCER INSTITUTE CORP · PI MORGAN, MARTIN T, WALDRON, LEVI · 2014 to 2023
$6.9M
Center for Integrated Multi-modal and Multi-scale Nucleome ResearchUM1HG011585 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DULAC, CATHERINE, LEIN, ED · 2020 to 2024
$6.7M
Model-based methods for single cell chromatin interactomic dataR35HG011922 · NHGRI · CLEVELAND CLINIC LERNER COM-CWRU · PI HU, MING · 2021 to 2025
$2.4M
Analysis of genomics datasets at a massive scaleR01GM121459 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI HANSEN, KASPER DANIEL · 2017 to 2021
$2.3M
Data Science Tools to Increase Insight in Genomics DataR35GM149323 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Kasper Daniel Hansen · 2023 to 2026
$1.8M
NCI NIH HHS U24 CA180996NHGRI NIH HHS R35 HG011922NHGRI NIH HHS UM1 HG011585NIDDK NIH HHS U54 DK107977NIGMS NIH HHS R01 GM121459NIGMS NIH HHS R35 GM149323NIH HHS U54DK107977
6 · The paper itself

Abstract

Hi-C data are commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation change across the contact map. We present band-wise normalization and batch correction, a method for normalization and batch correction of Hi-C data and show that it substantially improves comparisons across samples, including in a quantitative trait loci analysis as well as differential enrichment across cell types.

Indexed as

Quantitative Trait LociComputational BiologyHumansbioinformaticsHi-C

Identifiers

PMID38711367
PMCPMC11074651

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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.