Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (665 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
A technique for identifying specific DNA sequences that are bound, in vivo, to proteins of interest. It involves formaldehyde fixation of chromatin to crosslink the dna-binding proteins to the DNA. After shearing the DNA into small fragments, specific DNA-protein complexes are isolated by immunoprecipitation with protein-specific antibodies. Then, the DNA isolated from the complex can be identified by PCR amplification and sequencing.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Immunoprecipitation, Chromatin.
Where it fits in the MeSH classification
- Broader topics
- Genetic Techniques; Immunoprecipitation
- Narrower topics
- Chromatin Immunoprecipitation Sequencing
Related concepts
- Chromatin
- Dna-Binding Proteins
- Antibodies
Research landscape
PubMed indexes 12,556 records tagged with the MeSH heading “Chromatin Immunoprecipitation” (counts retrieved 2026-10-29). 1,752 of them (14.0%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 7,750 records.
Reviews account for 322 records (2.6%) and meta-analyses for 13. Randomized controlled trials number 1 (0.0%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 3,192 |
| 2010-2019 | 7,750 |
| 2020 onward | 1,752 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 322 | 2.6% |
| Meta-analyses | 13 | 0.1% |
| Randomized controlled trials | 1 | 0.0% |
Method: PubMed query "Chromatin Immunoprecipitation"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in genetics and genomics
Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation | 2020 | Research article Design not recorded in structured metadata | 677 | cc by PMID 32848148 · DOI · PMC7449963 |
| ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data | 2018 | Research article Design not recorded in structured metadata | 674 | cc by PMID 30413482 · DOI · PMC6280645 |
| Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling | 2019 | Research article Design not recorded in structured metadata | 526 | cc by PMID 31300027 · DOI · PMC6624997 |
| scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data | 2026 | Research article Design not recorded in structured metadata | 8 | cc by PMID 41284927 · DOI · PMC13568823 |
ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation [1]
Abstract reading summary — source excerpts
- Opening excerpt
- While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method.
- Closing excerpt
- As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation in both humans and mice, and propose how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (123 words)
While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. We validate TOBIAS using paired ATAC-seq and ChIP-seq data, and find that TOBIAS outperforms existing methods for bias correction and footprinting. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation in both humans and mice, and propose how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 32848148 · DOI · PMC7449963
ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data [2]
Abstract reading summary — source excerpts
- Opening excerpt
- We have fully integrated public chromatin chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq data (n > 70,000) derived from six representative model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast), and have devised a data-mining platform-designated ChIP-Atlas (http://chip-atlas.
- Closing excerpt
- ChIP-Atlas is superior to other platforms in terms of data number and functionality for data mining across thousands of ChIP-seq experiments, and it provides insight into gene regulatory networks and epigenetic mechanisms.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (161 words)
We have fully integrated public chromatin chromatin immunoprecipitation sequencing (ChIP-seq) and DNase-seq data (n > 70,000) derived from six representative model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast), and have devised a data-mining platform-designated ChIP-Atlas (http://chip-atlas.org). ChIP-Atlas is able to show alignment and peak-call results for all public ChIP-seq and DNase-seq data archived in the NCBI Sequence Read Archive (SRA), which encompasses data derived from GEO, ArrayExpress, DDBJ, ENCODE, Roadmap Epigenomics, and the scientific literature. All peak-call data are integrated to visualize multiple histone modifications and binding sites of transcriptional regulators (TRs) at given genomic loci. The integrated data can be further analyzed to show TR-gene and TR-TR interactions, as well as to examine enrichment of protein binding for given multiple genomic coordinates or gene names. ChIP-Atlas is superior to other platforms in terms of data number and functionality for data mining across thousands of ChIP-seq experiments, and it provides insight into gene regulatory networks and epigenetic mechanisms.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 30413482 · DOI · PMC6280645
Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling [3]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundCUT&RUN is an efficient epigenome profiling method that identifies sites of DNA binding protein enrichment genome-wide with high signal to noise and low sequencing requirements.
- Closing excerpt
- Its ease of use and performance in comparison with existing peak calling strategies make it an ideal choice for analyzing CUT&RUN data.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (173 words)
BackgroundCUT&RUN is an efficient epigenome profiling method that identifies sites of DNA binding protein enrichment genome-wide with high signal to noise and low sequencing requirements. Currently, the analysis of CUT&RUN data is complicated by its exceptionally low background, which renders programs designed for analysis of ChIP-seq data vulnerable to oversensitivity in identifying sites of protein binding.ResultsHere we introduce Sparse Enrichment Analysis for CUT&RUN (SEACR), an analysis strategy that uses the global distribution of background signal to calibrate a simple threshold for peak calling. SEACR discriminates between true and false-positive peaks with near-perfect specificity from "gold standard" CUT&RUN datasets and efficiently identifies enriched regions for several different protein targets. We also introduce a web server ( http://seacr.fredhutch.org ) for plug-and-play analysis with SEACR that facilitates maximum accessibility across users of all skill levels.ConclusionsSEACR is a highly selective peak caller that definitively validates the accuracy of CUT&RUN for datasets with known true negatives. Its ease of use and performance in comparison with existing peak calling strategies make it an ideal choice for analyzing CUT&RUN data.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 31300027 · DOI · PMC6624997
scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis.
- Closing excerpt
- io/.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (208 words)
Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. One key analytical task is to determine cell type identity based on epigenetic data. Here, we introduce scATAnno, a Python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates reference atlases from publicly available datasets, enabling accurate cell type annotation by integrating query data with reference atlases without the use of single-cell RNA sequencing (scRNA-seq) data. To enhance annotation accuracy, we incorporated k-nearest neighbors (KNN)-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compared and benchmarked scATAnno against five other published cell annotation approaches, demonstrating its superior performance across multiple datasets and metrics. We further showcased the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cells (PBMCs), triple-negative breast cancer (TNBC), and basal cell carcinoma (BCC), and demonstrated that scATAnno accurately annotates cell types across diverse biological conditions. Overall, scATAnno is a useful tool for scATAC-seq reference atlas construction and cell type annotation and can facilitate the interpretation of new scATAC-seq datasets in complex biological systems. scATAnno is publicly available at https://scatanno-main.readthedocs.io/.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41284927 · DOI · PMC13568823
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article ChIP-seq: advantages and challenges of a maturing technology [5]
- Research article Sequencing depth and coverage: key considerations in genomic analyses [6]
- Research article ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions [7]
- Research article Chromatin accessibility profiling by ATAC-seq [8]
- Research article From reads to insight: a hitchhiker's guide to ATAC-seq data analysis [9]
- Research article Computation for ChIP-seq and RNA-seq studies [10]
References
- Bentsen M, Goymann P, Schultheis H, Klee K, Petrova A, Wiegandt R, et al.. ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation. . 2020. PMID 32848148 · DOI · PMC7449963
- Oki S, Ohta T, Shioi G, Hatanaka H, Ogasawara O, Okuda Y, et al.. ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. . 2018. PMID 30413482 · DOI · PMC6280645
- Meers MP, Tenenbaum D, Henikoff S. Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. . 2019. PMID 31300027 · DOI · PMC6624997
- Jiang Y, Hu Z, Lu F, Lynch AW, Jiang J, Zhu A, et al.. scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data. . 2026. PMID 41284927 · DOI · PMC13568823
- Park PJ. ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet. 2009. PMID 19736561 · DOI · PMC3191340
- Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014. PMID 24434847 · DOI
- Furey TS. ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet. 2012. PMID 23090257 · DOI · PMC3591838
- Grandi FC, Modi H, Kampman L, Corces MR. Chromatin accessibility profiling by ATAC-seq. Nat Protoc. 2022. PMID 35478247 · DOI · PMC9189070
- Yan F, Powell DR, Curtis DJ, Wong NC. From reads to insight: a hitchhiker's guide to ATAC-seq data analysis. Genome Biol. 2020. PMID 32014034 · DOI · PMC6996192
- Pepke S, Wold B, Mortazavi A. Computation for ChIP-seq and RNA-seq studies. Nat Methods. 2009. PMID 19844228 · DOI · PMC4121056
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.