ArticleG3 (Bethesda, Md.)2014
Large-scale quality analysis of published ChIP-seq data.
Article in G3 (Bethesda, Md.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 2 of them syntheses that pooled it.
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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.
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.
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Who cites it
85 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- ChIP-Hub provides an integrative platform for exploring plant regulome.Nature communications · 2022Pooled it
- Meta-analysis and Consolidation of Farnesoid X Receptor Chromatin Immunoprecipitation Sequencing Data Across Different Species and Conditions.Hepatology communications · 2021Pooled it
- Epigenetic profiling identifies markers of endocrine resistance and therapeutic options for metastatic castration-resistant prostate cancer.Cell reports. Medicine · 2025Trial
- GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors.Nature methods · 2026Article
- Occupancy-based mechanism is the chief mode of ROS1 function in preventing DNA hypermethylation.Nature plants · 2026Article
- Accurate prediction of cohesin and RNA Polymerase II-associated chromatin interactions using convolutional neural networks.Nucleic acids research · 2026Article
- Many roads lead to a plant cistrome: mapping and interpreting transcription factor binding in plants.Genome biology · 2026Review
- A rapid test for protein-DNA interactions.Nucleic acids research · 2026Article
- Epigenetic Signatures of Social Defeat Stress Varying Duration.International journal of molecular sciences · 2025Article
- Comprehensive multimodal and multiomic profiling reveals epigenetic and transcriptional reprogramming in lung tumors.Communications biology · 2025Article
- An updated compendium and reevaluation of the evidence for nuclear transcription factor occupancy over the mitochondrial genome.PloS one · 2025Article
- Guidelines to Analyze ChIP-Seq Data: Journey Through QC and Analysis Considerations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- aChIP is an efficient and sensitive ChIP-seq technique for economically important plant organs.Nature plants · 2024Article
- Antipsychotic-induced epigenomic reorganization in frontal cortex of individuals with schizophrenia.eLife · 2024Article
- Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets.Genome research · 2023Article
- Titration-based normalization of antibody amount improves consistency of ChIP-seq experiments.BMC genomics · 2023Article
- NCommunications biology · 2022Article
- PCRMS: a database of predicted cis-regulatory modules and constituent transcription factor binding sites in genomes.Database : the journal of biological databases and curation · 2022Article
- Neural network modeling of differential binding between wild-type and mutant CTCF reveals putative binding preferences for zinc fingers 1-2.BMC genomics · 2022Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ∼25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e., no immunoprecipitation and mock immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.
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Registered trials
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.