Evidence map›Paper›PMID 40838784›Full record

ArticleBriefings in bioinformatics2025

Selecting ChIP-seq normalization methods from the perspective of their technical conditions.

Sara Colando, Danae Schulz, Johanna Hardin

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Sara ColandoDepartment of Statistics & Data Science, Carnegie Mellon University, 4909 Frew St., Pittsburgh, PA 15213, United States.ORCID 0009-0004-6166-0827
Danae SchulzDepartment of Biology, Harvey Mudd College, 301 Platt Blvd., Claremont, CA 91711, United States.ORCID 0000-0002-0392-7245
Johanna HardinDepartment of Mathematics & Statistics, Pomona College, 610 N. College Ave, Claremont, CA 91711, United States.ORCID 0000-0001-6251-1955

Funding

NIH HHS GM112625NSF CAREER 2041395Pomona College SURP program and Kenneth Cooke Summer Research Fellowship
6 · The paper itself

Abstract

Chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) provides insights into both the genomic location occupied by the protein of interest and the difference in DNA occupancy between experimental states. Given that ChIP-seq data are collected experimentally, an important step for determining regions with differential DNA occupancy between states is between-sample normalization. While between-sample normalization is crucial for downstream differential binding analysis, the technical conditions underlying between-sample normalization methods have yet to be examined for ChIP-seq. We identify three important technical conditions underlying ChIP-seq between-sample normalization methods: balanced differential DNA occupancy, equal total DNA occupancy, and equal background binding across states. To illustrate the importance of satisfying the selected normalization method's technical conditions for downstream differential binding analysis, we simulate ChIP-seq read count data where different combinations of the technical conditions are violated. We then externally verify our simulation results using experimental data. Based on our findings, we suggest that researchers use their understanding of the ChIP-seq experiment at hand to guide their choice of between-sample normalization method. Alternatively, researchers can use a high-confidence peakset, which is the intersection of the differentially bound peaksets obtained from using different between-sample normalization methods. In our two experimental analyses, roughly half of the called peaks were called as differentially bound for every normalization method. High-confidence peaks are less sensitive to one's choice of between-sample normalization method, and thus could be a more robust basis for identifying genomic regions with differential DNA occupancy between experimental states when there is uncertainty about which technical conditions are satisfied.

Indexed as

Chromatin ImmunoprecipitationChromatin Immunoprecipitation SequencingHigh-Throughput Nucleotide SequencingSequence Analysis, DNAComputer SimulationDNAHumansDNAbetween-sample normalizationChIP-seqCUT&RUN dataDiffBinddifferential binding analysisDNA occupancy

Identifiers

PMID40838784
PMCPMC12368857

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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.