ChIP-Hub is an integrative web-based/Shiny application for exploring plant regulome. It is a valuable resource for experimental biologists from various fields to comprehensively use all available epigenomic information to get novel insights into their specific questions.






Project Timeline

Recent Study

Hints: the tables in each tab below can be filtered by typing your keywords in the search or input box. Click rows for selection or deselection. Multiple rows could be selected.

Legend for Metrics


Visualization in EpiBrowser

Legend for Status

Low enrichment Low read depth No publication Single replicate Missing control Under processing

Legend for Metrics

FRIP SPOT REP QT NSC RSC Selected Filtered

Visualization in EpiBrowser

Select one row in the table above to see associated experiments for the selected gene

Overview of associated experiments

Experiment table

Select row(s) in the table above to visualize associated data tracks (none selection means ALL tracks)

Visualization in EpiBrowser

Select one row in the table above to visualize associated peak track

Overview of sequences correlation of peaks







Select one row in the table above to download associated Enhancer and Promoter for selected sample.

Visualization in EpiBrowser

Select rows in the table above to choose TFs and regulared Genes visualize associated GRN.

Select some row(s) in the table below for analysis
The table below can be filtered by typing your keywords in the search or input box. Click rows for selection or deselection. Only selected rows can be used for analysis. Due to the limitation of our computing resources, up to 10 rows (items) are supported in one run.
This tool is used to annotate the location of a set of peaks in terms of genomic features. It may be useful to find potential regulators functioning in TSS-distal regions (such as enhancers).


Genomic annotation

Distance to the TSS

This tool may be useful to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation (e.g., Chen et al. 2018 [doi:10.1038/s41467-018-06772-3] Yan et al. 2016 [doi:10.1016/j.pbi.2015.12.00].


Running Code

a=$(bedtools intersect -a $ds1 -b $ds2 -u | wc -l ) b=$(cat $ds1 | wc -l) rate=$(bc -l <<< "$a/$b") out=$(echo $bed | sed -e s/.bed$//g) cut -f11 $bed | awk '($1!=".")' | sed -e s/,/\n/g | sort | uniq > $out.gene

This code may take a long time, you can download code and run by yourself.

Download Code

Peak similarity

Target similarity

This tool is used to plot the distribution of ChIP-seq signal over a given set of genomic regions (such as annotated protein-coding regions).


Running Code

computeMatrix scale-regions -p 2 --afterRegionStartLength %s --beforeRegionStartLength %s -S %s -R *.bed --skipZeros -o plot.mat.gz

plotHeatmap -m plot.mat.gz --heatmapHeight 11 --heatmapWidth %s --colorMap %s --samplesLabel %s --whatToShow 'heatmap and colorbar' --startLabel start --endLabel end --refPointLabel center -out heatmap.pdf

This code may take a long time, you can download code and run by yourself.

Download Code

Summary plot

Heatmap plot

Sample category

Data contribution

Data timeline



with control?

with replicates?

with publication?

sufficient reads (>5M final reads)?

reasonable enrichment (SPOT>0.1)?


Data structure

Our data has three levels structure for you to download.
First is folders of all 43 species, each folder contains full information on the corresponding species:
├── aegilops_tauschii
├── arabidopsis_lyrata
├── arabidopsis_thaliana

├── triticum_urartu
├── vitis_vinifera
└── zea_mays

The second level contains experimental information for each species. Take rice(Oryza sativa) as an example:
├── DRP000207
├── ERP108685
├── ERP109752
├── CREs
├── Lastz
├── SRP005296

├── SRP300369
├── SRP303912
└── SRP308960
Note that there are two special folders named CREs and Lastz. This folders containers OpenChromatin information and comparative genomics information on the corresponding species. Some species have information on regulatory networks, and related data are stored in the corresponding species folder, named after species.regulation.rds

The last level corresponds to the various peak information or signal information contained in the experiment. Take DRP001345 as an example:
├── hammock
└── signal

Download link

Now, you can get access to data with

Last update

January 26, 2022, by Xinkai Zhou


Background and motivations

ChIP-seq and complementary assays are powerful methods to measure protein-DNA binding events and chemical modifications of histone proteins at genome-wide level. These technologies have become widely used to study gene-regulatory programs in animals and plants. Accordingly, a tremendous amount of data have been generated by several large consortiums (such as the ENCODE consortium in human[1] and mouse[2], as well as the modENCODE consortium in fly[3] and nematode[4]) or various smaller projects (such as the fruitENCODE project in flowering plants[5]). Several databases[6-8] were recently established for visualization and efficient deployment of public ChIP-seq data by the research community. However, no comprehensive resource is available for plant research. Another major bottleneck in current plant research is the lack of a standardized routine for evaluation and analysis of ChIP-seq data. Therefore, the comparison of data generated by different laboratories is not straightforward, hampering data integration to generate novel hypotheses for further investigation.

To this end, we launched a project at the middle of 2015 to fully reanalyze and expore ChIP-seq datasets in plants. We recently evaluated our analytical framework by an systematic reanalysis of ~100 ChIP-seq datasets for a set of floral regulators and provided a valuable resource to study regulatory circuits controlling floral organ development[9]. After this, we released the full reanalysis results to the public and developed an easy-to-use database and associated data-mining tools in a web-based platform called ChIP-Hub (


[1] ENCODE Consortium, T. E. P. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

[2] Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).

[3] modENCODE Consortium, T. et al. Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE. Science (80-. ). 330, 1787–1797 (2010).

[4] Gerstein, M. B. et al. Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project. Science 330, 1775–87 (2010).

[5] Lü, P. et al. Genome encode analyses reveal the basis of convergent evolution of fleshy fruit ripening. Nat. Plants 4, 784–791 (2018).

[6] Oki, S. et al. ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep. 19, e46255 (2018).

[7] Chèneby, J., Gheorghe, M., Artufel, M., Mathelier, A. & Ballester, B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments. Nucleic Acids Res. 46, D267–D275 (2018).

[8] Mei, S. et al. Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse. Nucleic Acids Res. 45, D658–D662 (2017).

[9] Chen, D., Yan, W., Fu, L.-Y. & Kaufmann, K. Architecture of gene regulatory networks controlling flower development in Arabidopsis thaliana. Nat. Commun. 9, 4534 (2018).


Q: How can I contact the researchers?

A: Please email Dr. Dijun Chen via for any related question about ChIP-Hub.

Q: How regularly is ChIP-Hub updated?

A: A routine to maintain and update ChIP-Hub has been established. according to our current plan, ChIP-Hub is updated monthly.

Q: How can I cite this work?

A: We're preparing our manuscript for this. Please cite the website (Chen et al., at the current stage.

Q: Could this platform be used to deposit data that are under consideration for publication?

A: Absolutely. If you'd like to talk, please get in contact with the main researcher, Dr. Dijun Chen, by email.

Q: Where can I get the data to use?

A: The meta data and result files can be downloaded by clicking on the Download or Action buttons in the app. Once the paper is finalised, we'd appreciate if you would cite it if you use the data.

Q: Could the pipeline be applied to other species?

A: Absolutely. In principle, the whole pipeling can easily be adapted to any species with available reference genomes.


SPOT: signal portion of tags

FRiP: fraction of reads in peaks

NSC: normalized strand cross-correlation coefficient

RSC: relative Strand cross-correlation coefficient

NRF: non-redundant fraction

PBC1: PCR bottlenecking coefficients 1

PBC2: PCR bottlenecking coefficients 2

QT: quality tags

RPKM: reads per kilobase per million mapped reads

CPM: counts per million mapped reads

BPM: bins per million mapped reads

RPGC: reads per genomic content normalized to 1x sequencing depth

MPR: mapping rate of reads

FUR: final used reads for peak calling

GC: GC content


We acknowledge the North-German Supercomputing Alliance (HLRN; and the Center for Information Technology and Media Management (ZIM) at Potsdam University ( for providing high performance computing (HPC) resources.

We would like to thank all the data contributors who make this project possible.

Last update

January 26, 2022, by Xinkai Zhou

Disclaimer: All orignal data are downloaded from public databases and reanalyzed automatically by our compuational pipeline. You should evaluate the original papers that are integrated by ChIP-Hub before making any interpretation.


Quick start

ChIP-Hub provides dynamic guidelines through a Getting Started button on top of the **Home** page, offering a quick start for exploring the features of ChIP-Hub. The nearby Samples and Experiments buttons provide links to the result page matching the last committed input, listed by samples or experiments respectively.

ChIP-Hub also provides a quick search function, users may use the dropdown menus and the search box to search for keywords. The left dropdown menu provides three options to search by samples, experiments or gene names. The right dropdown menu allows users to select species by clicking on the names, the list can be narrowed by typing a name inside the relative input field.

Clicking the Search button brings up a table of all available datasets matching the input, which will be shown in the Browser page for further manipulations.

Overview of the datasets

The Overview tab of the Home page presents an overview of datasets categorized by species. These statistical results can be filtered by the top-left dropdown list of the plant species, selecting on one or more species will lead to a real-time change of the pie charts and the timeline dot plot. Users can click on the legend of the timeline plot to filter for interested species.

The Recent Study tab shows the basic information of the two newest references in ChIP-Hub, including the title, authors, PubMed links, numbers of involving samples and experiments. A quick view of the samples or experiments in the article could be achieved by clicking on the corresponding image on the right side. The dataset will be shown on the Browser page.

Browsing data and visualization

ChIP-Hub provides multiple ways for data browsing. Besides the quick search of the **Home** page, users may directly click on the Browser button located in the top toolbar. Users may also change the way datasets are classified depending on their needs.

The **Browser** page shows the results in the Samples tab by default, but clicking one of the three tabs will bring users to another view. The two dropdown menus along the top of the page can help users to further filter interested species and BioProjects.

A data table containing details for each sample are presented in the Browser page. The data table include factor, sample type, accession number, reads information, metric score, sample title and attributes. Any of these categories can be used to filter the results, users also can enter a keyword in the top-right search box to display datasets that match the keyword. Click the column names to sort the datasets in ascending or descending order. The statics information of datasets about the current species will be displayed by clicking on the green leaf button.

The quality of the datasets in ChIP-Hub is measured by 7 metrics, which could be displayed by clicking on Plot Metrics button. ChIP-Hub provide a visualization function of interested samples by WashU EpiGenome Browser.

Also, some species have CREs information and networks information. On CREs page, users can get Promoters and Enhancers peaks of each related sample. After click “specific” peak, users can directly find their peak in epiBrowser by clicking on Visualize button.

In addition to obtaining the information of Promoters and Enhancers, users can obtain the correlation of CREs sequences of corresponding species on the CREs page. Different species are grouped by order(Taxonomic Rank, i.e., class, order, family, genus), and users can filter species according to the number of related peaks.

On Networks page, the network will be drawn based on the user's selection of TFs and associated genes after clicking the Draw Network button. Users can also see the names of nodes by clicking the Show Names button.

Browsing data and visualization

The Tools button in the top navigation bar will lead users to the Tools page, which includes three tools: Peak Annotation, Overlap Analysis and Signal Plot.

To apply these functions, users need to choose desired datasets (rows in the table). Selecting species and project in the dropdown list will narrow the data table, and keywords can be used for further filtering these datasets. Due to the limitation of our computing resources, up to 10 rows (items) are supported in one run.

After data selection, users may click on the Run Analysis button to run the analysis pipeline. The settings could be adjusted to satisfy specific needs. The plots, annotation files or tables could be downloaded by clicking the download button.

The tool “Peak Annotation” can annotate the location of a set of peaks in terms of genomic features, which may be useful to find potential regulators functioning in TSS-distal regions.

The tool “Overlap Analysis” is useful to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation.

The tool “Signal Plot” can be used to display the distribution of ChIP-seq signal over a given set of genomic regions (such as annotated protein-coding regions).


The overview of samples is presented in the Samples tab of the Statistics page. The detailed statistical results for each factor are displayed in the doughnut chart. The world map of data contribution and timeline of datasets are also shown in this page. Selecting species in the top-left dropdown list will simultaneously influence these interactive visualizations.

Similarly, the statistics of experiments are displayed in the Experiments tab.


The background and motivations of ChIP-Hub were shown on this page, including the FAQs and abbreviations.


The detailed methods of data collecting, processing and assignment procedures are well organized on the Methods page.


Currently, the datasets in ChIP-Hub were collected from ~360 reference literatures. A data table containing details (including PubMed links) for each reference are presented in the List tab of the References page.

The statistics related to all these references are shown in the Statistics tab, including their publishing journals, authors, keywords and the timeline data.

Last update

January 27, 2022, by Xinkai Zhou


Data source

Metadata of ChIP-seq and DAP-seq samples (equivalent to datasets, accession numbers start with SRX/ERX/DRX) and projects (start with SRP/ERP/DRP) were retrieved from NCBI SRA (, BioSample (, BioProject ( and/or GEO ( databases. ChIP-Hub has an focus on data in “green plants” (i.e., only considering plants in the taxonomy tree with a root ID 33090). Only data generated by Illumina platforms were kept. Firstly, each dataset was associated with publication(s) if available (more than 90% samples can be linked with publications). Then, each dataset was manually curated to determine its investigated factor (i.e., which TF or histone modification mark), its experimental type (whether ChIP or control) and its associated replicates (experiment may have several replicates), based on the metadata and the original publications. Note that it is important to manually check the metadata based on its corresponding publication since some metadata was misannotated in the database. For example, the dataset SRX4063234 in fact contains two different samples, one for ChIP experiment (SRR7142417) and another for control experiment (SRR7142416). In this case, “Run” accessions (start with SRR/ERR/DRR) were instead used as sample accessions (ca. 250 of such cases). For datasets without related publications so far, they were marked as a “unconfirmed” status and would be regularly checked in the future. In general, one experiment may contain replicate samples (i.e., datasets), ChIP sample(s) as well as input control sample(s) and it was designed to investigate regulation of a specific factor (e.g., TF or histone modification) of interest under specific conditions. In the analysis (see the section below), each experiment was processed independently. Furthermore, annotation information for investigated factors was also manually curated. Broadly, factors are grouped into “TFs and other proteins”, “histone-related” or “unclassified”. For TFs, their gene IDs and family information were also determined if applicable. Finally, a meta file was obtained for each experiment after curation, which is served as an input file for the ChIP-seq computation pipeline (see below).

Raw fastq files for each experiment were downloaded from the European Nucleotide Archive (ENA, database. If fastq files were not available at ENA, raw data in the SRA format were downloaded from the SRA database and converted into fastq format using the “fastq-dump” command provided by the SRA Toolkit (version 2.5.1). The “–split-files” option was used for paired-end reads. Fastq files were further checked for completeness before submitted to analysis.

Genome sequences and gene annotations were downloaded from public databases. Additional annotation data were also included in the ChIP-Hub database in order to better annotate the regulatory factors and their regulatory networks. Annotation for miRNA genes were obtained from miRBase[1] and their genomic locations were updated (by BLAST) based on current reference genomes. TF family information was retrieved from PlantTFDB[2]. TF DNA binding motifs were downloaded from the JASPAR[3], CIS-BP[4] and PlantTFDB[2] databases and were scanned for occurrences in the genome using FIMO[5]. These data were provided as separated data tracks in the genome browser.

ChIP-seq data processing

We followed the ChIP-seq data analysis guidelines[6] recommended by the ENCODE project to develop computational pipeline for ChIP-seq and DAP-seq data analysis. The analysis pipeline consists of quality control, read mapping, peak calling and assessment of reproducibility among biological replicates and was used to analyze all annotated experiments a standardized and uniform manner. Specifically, potential adapter sequences were removed from the sequencing reads using the Trim Galore program (version 0.4.1) and the quality of sequencing data was then evaluated by FastQC ( Clean reads were mapped to the corresponding reference genomes using Bowtie2 (version 2.2.6; ref.[7]) with parameters “-q –no-unal –threads 8 –sensitive”. The parameter “-k” was set to 1, 2 and 3 for diploid genomes (e.g., Oryza sativa), tetraploid genomes (e.g., Gossypium barbadense) and hexaploidy genomes (e.g., Triticum aestivum), respectively. Redundant reads and PCR duplicates were removed using Picard tools (v2.60; and SAMtools[8] (version 0.1.19).

Peak calling was performed using MACS2 (version 2.1.0; ref.[9]). Duplicated reads were not considered (“–keep-dup=1”) during peak calling in order to achieve a better specificity[10]. The shifting size (“–shift”) used in the model was determined by the analysis of cross-correlation scores using the phantompeakqualtools package ( The parameter “–call-summits” was used to call narrow peaks. For broad marks of histone modifications (including H3K36me3, H3K20me1, H3K4me1, H3K79me2, H3K79me3, H3K27me3, H3K9me3 and H3K9me1), broad peaks were also called by turning on the “–broad” parameter in MACS2. A relaxed threshold of p-value (p-value < 1e-2) was used in order to enable the correct computation of IDR (irreproducible discovery rate) values[6], because IDR requires input peak data across the entire spectrum of high confidence (signal) and low confidence (noise) so that a bivariate model can be fitted to separate signal from noise[11]. Following the recommendations for the analysis of self-consistency and reproducibility between replicates[11], replicate control samples (if available) were combined into one single control in the same experiment. Peak calling was applied to all replicates, pooled data (pooled replicates), pseudo-replicates (half subsample of reads) of each replicate and the pseudo-replicates of pooled sample using the same merged control as input (if applicable). By default, “reproducible” peaks across pseudo-replicates and true replicates with an IDR < 0.05 were recommend for analysis. Besides, peaks with different statistical thresholds are available upon request. For example, “significant” peaks were defined as a fold-change (fold enrichment above background) > 2 and a -log10 (q-value) > 3; while “lenient” peaks as a fold-change > 2 and a -log10 (q-value) > 2. “Relaxed” peaks without additional thresholding were also provided so that any custom threshold can be applied. All peak-based analyses in the pipeline (including peak overlapping, merging and summary) were performed using BEDTools (v2.25.0; ref.[12]).

Various metric scores were calculated to assess different aspects of the quality of experiments ( and For example, library complexity is measured using the non-redundant fraction (NRF) and PCR bottlenecking coefficients 1 and 2 (PBC1 and PBC2). The SPOT (signal portion of tags) score, characterizing the enrichment of signal for each experiment, was calculated by the Hotspot[13] algorithm by subsampling ten million reads. Fraction of reads in peaks (FRiP), another measure of enrichment, is highly correlated with the SPOT score. NSC and RSC (normalized and relative strand cross-correlation coefficient) are related measures of enrichment without dependence on pre-defined peaks, which were calculated by the phantompeakqualtools program.

For visualization purpose, wiggle tracks (using pooled data across replicates) were generated by DeepTools[14] with the “bamCoverage” program; different normalization methods (including RPKM [reads per kilobase per million mapped reads], CPM [counts per million mapped reads], BPM [bins per million mapped reads], RPGC [reads per genomic content normalized to 1x sequencing depth] and None) were used to generate different types of signal files. ChIP-seq tracks were visualized in the WashU Epigenome Browser[15].

Assignment of target genes

Regulatory elements (in layman's terms, called “peaks”) were assigned to putative target genes based on the following rules. For a regulatory region overlapping with any gene(s) (protein-coding genes or miRNAs), the overlapping gene(s) were considered as its targets. Otherwise, the regulatory element was assigned to its nearest annotated gene within up to N bp, where N is the median size of intergenic regions (N was set to 3000 if the median size exceeded 3000). The start of genes (i.e., the transcription start site [TSS] of protein-coding genes and the 5’ end of miRNA precursors [pre-miRNAs]) was used to calculate the distance. In general, this approach associates a single regulatory element with no more than two genes, with a few exceptions in the case of the regulatory element overlapping multiple genes. This procedure was performed in each species independently.


[1] Kozomara, A., Birgaoanu, M. & Griffiths-Jones, S. MiRBase: From microRNA sequences to function. Nucleic Acids Res. (2019). doi:10.1093/nar/gky1141

[2] Jin, J. et al. PlantTFDB 4.0: Toward a central hub for transcription factors and regulatory interactions in plants. Nucleic Acids Res. (2017). doi:10.1093/nar/gkw982

[3] Khan, A. et al. JASPAR 2018: Update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. (2018). doi:10.1093/nar/gkx1126

[4] Weirauch, M. T. et al. Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity. Cell 158, 1431–1443 (2014).

[5] Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–8 (2011).

[6] Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research 22, 1813–1831 (2012).

[7] Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

[8] Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

[9] Zhang, Y. et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

[10] Bailey, T. et al. Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. PLoS Comput. Biol. 9, (2013).

[11] Li, Q., Brown, J. B., Huang, H. & Bickel, P. J. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 5, 1752–1779 (2011).

[12] Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

[13] John, S. et al. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nature Genetics 43, 264–268 (2011).

[14] Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. DeepTools: A flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, (2014).

[15] Zhou, X. et al. The human epigenome browser at Washington University. Nature Methods 8, 989–990 (Nature Research, 2011).

Last update

August 12, 2019, by Dijun Chen


Information of Reference Genomes

Plant species Tax ID Common name Short ID (used for Figures) Genome release version Genome size (in Mb) Average intergenic size (bp) #miRNA genes #protein-coding genes
Aegilops tauschii 37682 Tauschs goatgrass ata Aet v4.0 4078.89 106088 86 39152
Arabidopsis lyrata 59689 Lyre-leaved rock-cress aly v1.0 200.93 4113 198 32667
Arabidopsis thaliana 3702 Mouse-ear cress ath TAIR10 119.15 2310 323 27206
Arabis alpina 50452 Alpine rock-cress aal v5.0 317.91 8123 64 34220
Beta vulgaris 161934 Sugar beet bau RefBeet-1.2.2 520.56 15871 0 26521
Brachypodium distachyon 15368 Purple false brome bdi v3.0 271.07 5052 313 34310
Brassica napus 3708 Rape bna v4.1 850.29 6452 88 101040
Brassica oleracea 3712 Savoy cabbage bol v1.0 385.01 9127 8 35400
Brassica rapa 3711 Turnip mustard bra v1.3 297.59 5298 84 40492
Carica papaya 3649 Papaya cpa ASGPBv0.4 288.98 9940 75 27769
Chlamydomonas reinhardtii 3055 Chlamydomonas reinhardtii cre v5.5 110.59 3540 40 17741
Citrullus lanatus 260674 Watermelon cla v1.0 355.25 12393 0 23440
Cucumis melo 3656 Muskmelon cme v4.0 417.00 11448 118 29980
Cucumis sativus 3659 Cucumber csa ASM407-v2 192.95 5119 5 23780
Eucalyptus grandis 71139 Flooded gum egr v2.0 651.05 15522 2 36349
Eutrema salsugineum 72664 Saltwater cress esa v1.0 238.95 6958 77 26351
Fragaria vesca 57918 Woodland strawberry fve v1.1 206.89 3439 118 32831
Glycine max 3847 Soybean gma Wm82.a2.v1 949.18 13704 671 56044
Gossypium arboreum 29729 Tree cotton gar BGI v2.0 1541.29 35944 1 40134
Gossypium barbadense 3634 Sea-island cotton gba HAU-SGI v1.0 2045.17 28144 7 82099
Gossypium hirsutum 3635 Upland cotton ghi NAU-NBI v1.1 2053.61 28285 72 70478
Gossypium raimondii 29730 New World cotton gra v2.1 751.27 17458 294 37505
Hordeum vulgare 4513 Barley hvu Hv_IBSC_PGSB_v2 4833.79 127710 52 39734
Lotus japonicus 34305 Lotus japonicus lja MG20_v3.0 446.89 8808 291 39648
Malus domestica 3750 Apple mdo GDDH13 v1.1 709.56 12587 254 45116
Medicago truncatula 3880 Barrel medic mtr Mt4.0v1 397.59 5360 653 50894
Musa acuminata 4641 Banana mac v1.0 472.96 9216 1 36528
Oryza sativa 39947 Japonica rice osa IRGSP-1.0 374.47 7145 589 39049
Phaseolus vulgaris 3885 French bean pvu v2.1 532.24 15480 8 27433
Physcomitrella patens 3218 Moss ppa v3.0 470.36 1821 244 32926
Populus trichocarpa 3694 Western balsam poplar ptr v3.0 422.50 7069 334 42950
Prunus persica 3760 Peach ppe v2.0 226.00 5504 180 26873
Pyrus bretschneideri 225117 Chinese white pear pbr v121010 500.23 36957 3 10974
Rosa chinensis 74649 China rose rch v1.0 518.52 10479 5 39669
Setaria italica 4555 Foxtail millet sit v2.0 403.32 9211 1 35831
Solanum lycopersicum 4081 Tomato sly ITAG2.4 823.94 20989 110 34725
Solanum tuberosum 4113 Potato stu v4.03 773.03 17472 221 39028
Sorghum bicolor 4558 Sorghum sbi v3.0.1 704.28 17686 205 34129
Triticum aestivum 4565 Wheat tae IWGSC_RefSeq_v1.0 14547.26 130533 107 110790
Triticum urartu 4572 Red wild einkorn tur WheatTU 4712.41 123641 1 41493
Vitis vinifera 29760 Grape vvi IGGP_12X 486.20 11990 161 26346
Zea mays 4577 Maize zma AGPv3 2066.43 49870 167 39295


The following list of publications would regularly be updated according to the collected datasets. Please let us know in case that your data are deposited in ChIP-Hub but your paper is not listed here.


Journal for Publication

Authors in Publication

Keywords in Abstract



Recent Update

Data Release






Dijun Chen
PhD in Bioinformatics
project leader
Liangyu Fu
Master in Bioinformatics
data collection and analysis
Peijing Zhang
PhD student in Bioinformatics
data annotation
Ming Chen
technical support
Kerstin Kaufmann
team leader
Tao Zhu
PhD student in Bioinformatics
data analysis
Ranran Yu
Master student in Bioinformatics
data analysis
Xinkai Zhou
Master student in Bioinformatics
data analysis


Dijun Chen
PhD in Bioinformatics
For any question about ChIP-Hub, please email:
Last updated on 2022-02-17
© 2016-2022. The ChIP-Hub team. All right reserved. This web application is maintained by the NJU Computational Biology Group.