ChIP-Atlas is the database and its web interface to provide the result of analysis processed from the entire ChIP-Seq data archived in Sequence Read Archive. We have curated metadata described by original data submitter to enable further data analysis. See details here: https://github.com/inutano/chip-atlas/wiki

The data provided here are part of a Galaxy tutorial that analyzes ChIP-seq data from a study published by Wu et al., 2014 (DOI:10.1101/gr.164830.113). The goal of this study was to investigate "the dynamics of occupancy and the role in gene regulation of the transcription factor Tal1, a critical regulator of hematopoiesis, at multiple stages of hematopoietic differentiation." To this end, ChIP-seq experiments were performed in multiple mouse cell types including a G1E cell line and megakaryocytes, the two cell types represented here. The dataset contains biological replicate Tal1 ChIP-seq and input control experiments (*.fastqsanger files). Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to chromosome 19 and a subset of interesting genomic loci (ChIPseq_regions_of_interest_v4.bed) pulled from the Wu et al. publication. Also included is a gene annotation file (RefSeq_gene_annotations_mm10.bed) with gene names added for viewing in a genome browser.

This dataset gather ChIP-seq data produced by immunoprecipitating CTCF factor in own laboratory in MM.1S cell line in EtOH and Dex conditions. It also gather ChIP-seq dataset produced by external laboratory (Active Motif) for H3K27ac mark and GR transcription factor in same cell line and conditions ( MM.1S ETOH/Dex)

The number of experiments in which gene was up/down regulated in RNA-seq data and the average of ChIP-seq MACS2 values of HIF1A and EPAS1(HIF2A) in ChIP-Atlas database.Both were calculated from public NGS database (SRA).For up/donw regulated gene selection, 2 fold threshold was adopted.

ChIP-seq (chromatin immunoprecipitation followed by sequencing) is commonly used to identify genome-wide protein-DNA interactions. However, ChIP-seq often gives a low yield, which is not ideal for quantitative outcomes. An alternative method to ChIP-seq is ChEC-seq (Chromatin endogenous cleavage with high-throughput sequencing). In this method, the endogenous TF (transcription factor) of interest is fused with MNase (micrococcal nuclease) that non-specifically cleaves DNA near binding sites. Compared to the original ChEC-seq method, the modified version requires far less amplification. Since MACS3 failed to identify peaks in data generated from the modified ChEC-seq method, a new peak finder has been developed specifically for it. There are three functions in the peak_finder/. callpeaks() is used to identify peaks from BAM files. goanalysis() is used to make GO (Gene Ontology) term plots from peaks. bedtomeme() is a wrapper function to perform MEME analysis in R after MEME Suite is installed locally. Methods ****EXCERPTED FROM BIORXIV PREPRINT; SEE PREPRINT OR PUBLISHED PAPER FOR REFERENCES AND DETAILS**** Yeast strains All yeast strains were derived from BY4741. A C-terminal micrococcal nuclease fusion was introduced to the protein of interest through transformation and homologous recombination of PCR-amplified DNA. Primers were designed with 50-bp of homology to the 3’ end of the coding sequence of interest. The 3xFLAG-MNase with a KanR marker was amplified from pGZ108 (Zentner et al., 2015) and transformed into BY4741 as previously described. Successful transformation was confirmed by immunoblotting and PCR, followed by sequencing. Lyophilized DNA oligonucleotides were resuspended in molecular-grade water to a concentration of 100 µM. For ligation, the following pair of oligonucleotides were annealed to produce the Y-adapter: Tn5ME-A (5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3’) and Y-Adapt-i5 R (5’-CTGTCTCTTATACACATCTTCATAGTAATCATC-3’). For Tn5 Tagmentation, the following i7 oligonucleotides were annealed: Tn5ME-B (5’ -GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3’) and Tn5MErev, (5’-PO4-CTGTCTCTTATACACATCT-3’). Pairs of oligonucleotides were annealed as follows: 45 µl of each oligo (100 µM) was combined with 10 µl of 1 M Potassium Acetate, 300 mM HEPES, pH 7.5 in a 0.2 ml PCR tube. In a thermocycler, the mixture was heated to 95˚C for 4 minutes, cooled 1°C/minute until 50°C, incubated at 50°C for 5 minutes, and then cooled 1°C/minute until 4°C. Hybridized oligos were stored in 15 µl aliquots at -20˚C. Tn5 purification and adapter loading Tn5 E54K L372P was purified as previously described (Hennig et al., 2017). We found that Tn5 was sufficiently pure following purification on Ni2+-chromatography and we therefore omitted the final gel filtration step. Purified Tn5 was aliquoted and stored at -80°C. Optimal Tn5 activity was determined by cleaving genomic DNA and assessing fragmentation using the Femto Pulse (Figure S2d), and resulting DNA libraries were confirmed to be of appropriate length for Illumina Sequencing by TapeStation (Figure S2e). Tn5 was thawed on ice and 100 µl Tn5 was added to 10 µl i7 (45 µM) in a 1.7 ml tube and mixed by gently pipetting. The mixture was incubated at 23°C, mixing at 350 rpm for 45 minutes. Adapter-loaded Tn5 was stored at -20°C and used within 24 hours.

Chromatin endogenous cleavage detailed protocol Chromatin digestion 1. Grow cells in 10ml overnight at 30°C, 200 rpm. 2. Dilute cultures into 50ml media to OD600 ~ 0.1. 3. When cultures reach OD600 = 0.5 - 0.8, harvest 25 ODs (i.e. 50ml if the OD600 = 0.5) of cells by centrifugation at 2500 x g for 1 minute. 4. Resuspend cells in 1 ml Buffer A and transfer to a 1.5 ml tube. 5. Pellet cells by centrifugation at 2500 x g for 1 minute, remove supernatant. 6. Wash cells 2 x 1 ml Buffer A, removing supernatant. 7. Resuspend cells in 600 µl Buffer A + 0.1% Digitonin. 8. Transfer tube to a 30°C heat block and incubate for 5 minutes. 9. Add 5 µl of 333 mM CaCl2, mix by inverting, incubate at 30°C for the appropriate cleavge time (determined empirically for each protein). 10. To stop the reaction, remove 200 µl cells and combine with 200 µl 2x Stop Buffer. 11. Add 8 µl Proteinase K (20 µg/µl) and mix. 12. Incubate at 50°C, agitating 800 rpm for 30 minutes in a thermomixer. 13. Remove samples from thermomixer and cool at room temperature for 5 minutes. 14. Add 400 µl Phenol-Chloroform-Isoamyl Alcohol (25:24:1), pH 7.8, mix. 15. Centrifuge at 24,000 x g, 5 minutes. 16. Transfer aqueous phase to a phase-lock tube. 17. Add 200 µl Phenol-Chloroform-Isoamyl Alcohol (25:24:1), pH 7.8. 18. Invert 10x to mix. 19. Centrifuge at 24,000 x g for 5 minutes. 20. Transfer aqueous phase to a tube containing 1 ml 100% Ethanol. 21. Add 2 µl of linear acrylamide (5 µg/µl). 22. Invert 10x to mix. 23. Incubate at -80°C for 30 minutes. 24. Centrifuge at 24,000 x g, 4°C for 10 minutes. 25. Pour off supernatant. 26. Wash DNA pellet in 1 ml of 70% ethanol. 27. Centrifuge at 24,000 x g for 1 minute. 28. Pour off supernatant. Collect residual ethanol by centrifugation and remove by pipetting. 29. Dry DNA pellet until all ethanol had evaporated. 30. Add 58 µl of 10 mM Tris-HCl, pH 8.5 to DNA pellet. 31. Incubate overnight at room temperature. 32. Incubate at 37°C for 30 minutes. 33. Add 2 µl RNase A (10 µg/µl) to DNA. 34. Incubate at 37°C for 30 minutes. 35. Evaluate molecular weight of DNA by gel electrophoresis, 0.8% agarose or TapeStation. 36. Quantify DNA concentration with the Qubit double-stranded DNA, Broad Range Assay. 37. Stored DNA at 4°C until library preparation was performed (up to a month), then stored at -20°C.

Buffer A 15 mM Tris-HCl, pH 7.5 80 mM KCl 0.1 mM EGTA 1.0 mM PMSF 0.5 mM Spermidine 0.2 mM Spermine -Add 1 EDTA-Free Protease Inhibitor Tab per 50 ml Buffer A (Roche; Sigma # 11873580001)

2x Stop Buffer 400 mM NaCl 20 mM EDTA 4 mM EGTA

Bioinformatic analysis Quality Control, Trimming, and Mapping Read quality and sequencer performance was evaluated with FASTQC. Reads were adapter and quality trimmed with Trimmomatic (Bolger et al., 2014) using single-end settings. Bases at either end of a read were trimmed if base-call quality was less than 30, and only reads of length ≥25 bp were retained. Trimmed reads were mapped to the Saccharomyces cerevisiae genome (Engel et al., 2013), version R64-4-1 with Bowtie2 (Langmead and Salzberg, 2012)and mapped reads with a MAPQ <10 were removed with Samtools (Li et al., 2009). DoubleChEC identification of high-confidence TF binding sites For peak calling analysis, BAM files for three or more biological replicates of the TF-MNase and soluble MNase were read and trimmed to the first base pair. Unnormalized counts and normalized counts per million (CPMn) were tallied for each base pair in the yeast genome and the average CPMn values among replicates were calculated for each position. Next, mean CPMn values were smoothed using a sliding window of 3 and a step size of 2. Windows with CPMn values less than three times the genome average were filtered out. After this filtering, local maxima (windows with values greater than their immediate neighbors) were identified. Unnormalized reads were smoothed, retaining positions that were identified as local maxima, and inputted them in DESeq2 (version 1.36.0) to identify windows with values significantly higher than those in the soluble MNase control. Only TF-MNase peaks with a greater log2-fold change of 1.7 and an adjusted p-value less than 0.0001 over soluble MNase were retained. Finally, the peaks were filtered again to identify doublet peaks that are between 15bp and 50bp apart, which were merged to single peaks. GO term plot A list of genes whose 700bp upstream regions overlap with peaks identified by the peak finder was input to enrichGO (Wu et al., 2021) to generate GO term plots based on biological functions. The 10 most significant GO terms with adjusted p-values less than 0.05 were plotted. MEME analyses The MEME Suite (version 5.5.1) was installed onto the local computer and two custom wrapper functions were written in R for the local bed2fasta and meme programs. These functions were then used to convert bed files, generated from peak calling, into FASTA files. These FASTA files were subsequently to generate motif logos. Both bed2fasta and meme programs were run using their default parameter values.

Transcriptomic profiling is an immensely powerful hypothesis generating tool. However, accurately predicting the transcription factors (TFs) and cofactors that drive transcriptomic differences between samples is challenging. A number of algorithms draw on ChIP-seq tracks to define TFs and cofactors behind gene changes. These approaches assign TFs and cofactors to genes via a binary designation of ‘target’, or ‘non-target’ followed by Fisher Exact Tests to assess enrichment of TFs and cofactors. ENCODE archives 2314 ChIP-seq tracks of 684 TFs and cofactors assayed across a 117 human cell lines under a multitude of growth and maintenance conditions. The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an a priori binary classification of genes as targets or non-targets. When compared to other TF mining resources, MAGIC displayed favourable performance in predicting TFs and cofactors that drive gene changes in 4 settings: 1) A cell line expressing or lacking single TF, 2) Breast tumors divided along PAM50 designations 3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype 4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels. In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.

Chromatin immunoprecipitation and sequencing (ChIP-seq) has been widely used to map DNA-binding proteins, histone proteins and their modifications. ChIP-seq data contains redundant reads termed duplicates, referring to those mapping to the same genomic location and strand. There are two main sources of duplicates: polymerase chain reaction (PCR) duplicates and natural duplicates. Unlike natural duplicates that represent true signals from sequencing of independent DNA templates, PCR duplicates are artifacts originating from sequencing of identical copies amplified from the same DNA template. In analysis, duplicates are removed from peak calling and signal quantification. Nevertheless, a significant portion of the duplicates is believed to represent true signals. Obviously, removing all duplicates will underestimate the signal level in peaks and impact the identification of signal changes across samples. Therefore, an in-depth evaluation of the impact from duplicate removal is needed. Using eight public ChIP-seq datasets from three narrow-peak and two broad-peak marks, we tried to understand the distribution of duplicates in the genome, the extent by which duplicate removal impacts peak calling and signal estimation, and the factors associated with duplicate level in peaks. The three PCR-free histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq data had about 40% duplicates and 97% of them were within peaks. For the other datasets generated with PCR amplification of ChIP DNA, as expected, the narrow-peak marks have a much higher proportion of duplicates than the broad-peak marks. We found that duplicates are enriched in peaks and largely represent true signals, more conspicuous in those with high confidence. Furthermore, duplicate level in peaks is strongly correlated with the target enrichment level estimated using nonredundant reads, which provides the basis to properly allocate duplicates between noise and signal. Our analysis supports the feasibility of retaining the portion of signal duplicates into downstream analysis, thus alleviating the limitation of complete deduplication.

Target genes of transcription factors from published ChIP-chip, ChIP-seq, and other transcription factor binding site profiling studies

This dataset contains CTCF ChIP-sequencing data from seven samples (six tumor samples and one tumor derived cell line). Following library amplification, DNA fragments were sequenced using Illumina HiSeq 2000 paired-end sequencing resulting in 14 FASTQ files.

The data provided here are part of a Galaxy Training Network tutorial that analyzes ChIP-seq data from a study published by Ross-Inness et al., 2012 (DOI:10.1038/nature10730) to identify the binding sites of the Estrogen receptor, a transcription factor known to be associated with different types of breast cancer.

Shown are the number of peaks called and the total number of bp covered by each peak set for H3K4me3, H3K36me3, and H3K9me3 using the original Sole-search program or the program which has been modified to identify broad regions covered by modified histones. Also shown in the increase in genome coverage (fold difference) that results when using the modified peak calling program. Both the original and the modified program can be accessed at http://chipseq.genomecenter.ucdavis.edu/cgi-bin/chipseq.cgi.

Table of HN-scores and ChIP-seq scores (MACS2 score) for each gene. The genes listed in this data are the only human and mouse gene symbols that can be converted.The ChIP-seq score is retrieved from the ChIP-Atlas database (http://chip-atlas.org) (accessed on February 2023). Using the "Target Genes" feature, data were obtained for HSF1, HSF2, and PPARGC1A.

This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Philip Cayting mailto:pcayting@stanford.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu).This track shows probable binding sites of the specified transcription factors (TFs) in the given cell types as determined by chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq). Included for each cell type is the input signal, which represents the control condition where no antibody targeting was performed. For each experiment (cell type vs. antibody) this track shows a graph of enrichment for TF binding (Signal), along with sites that have the greatest evidence of transcription factor binding (Peaks).For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Further preparations were similar to those previously published (Euskirchen et al., 2007) with the exceptions that the cells were unstimulated and sodium orthovanadate was omitted from the buffers. For details on the chromatin immunoprecipitation protocol used, see Euskirchen et al. (2007) and Rozowsky et al. (2009).DNA recovered from the precipitated chromatin was sequenced on the Illumina (Solexa) sequencing platform and mapped to the genome using the Eland alignment program. ChIP-seq data was scored based on sequence reads (length ~30 bps) that align uniquely to the human genome. From the mapped tags a signal map of ChIP DNA fragments (average fragment length ~ 200 bp) was constructed where the signal height is the number of overlapping fragments at each nucleotide position in the genome.For each 1 Mb segment of each chromosome a peak height threshold was determined by requiring a false discovery rate <= 0.05 when comparing the number of peaks above threshold as compared the number obtained from multiple simulations of a random null background with the same number of mapped reads (also accounting for the fraction of mapable bases for sequence tags in that 1 Mb segment). The number of mapped tags in a putative binding region is compared to the normalized (normalized by correlating tag counts in genomic 10 kb windows) number of mapped tags in the same region from an input DNA control. Using a binomial test, only regions that have a p-value <= 0.05 are considered to be significantly enriched compared to the input DNA control.

These data are the results from three independent runs of DCSsim and DCSsub for TF, sharp and broad mark signals in 50:50 regulation scenarios for four (sim) and three (sub) different FRIP ranges. Simulated data from DCSsim: simulated_ChIP-seq_data.zip Set13: TF 50:50 x0.5 background Set14: TF 50:50 x2 background Set15: TF 50:50 x3 background Set25: TF 50:50 x1 background Set16: Sharp mark 50:50 x0.5 background Set17: Sharp mark 50:50 x2 background Set18: Sharp mark 50:50 x3 background Set26: Sharp mark 50:50 x1 background Set19: Broad mark 50:50 x0.5 background Set20: Broad mark 50:50 x2 background Set21: Broad mark 50:50 x3 background Set27: Broad mark 50:50 x1 background Sub-sampled data from DCSsub: sub-sampled_ChIP-seq_data.zip Set1: PU1-ChIP-seq 50:50 Set2: STAT6-ChIP-seq 50:50 Set8: C/EBPa-ChIP-seq 50:50 Set4: H3K4me3-ChIP-seq 50:50 Set9: H3K27ac-ChIP-seq 50:50 Set15: H3K9ac-ChIP-seq 50:50 Set6: H3K27me3-ChIP-seq 50:50 Set10: H3K36me3-ChIP-seq 50:50 Set11: H3K79me2-ChIP-seq 50:50 {"references": ["Eder, T., Grebien, F. Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection. Genome Biol 23, 119 (2022). https://doi.org/10.1186/s13059-022-02686-y"]}

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis was performed to evaluate whether CgCrzA plays a role in regulating CWI-related genes. Compared to the control, the ChIP-seq samples exhibited enrichment of CgCrzA-bound DNA fragments under CFW conditions

We developed a method, ChIP-sequencing (ChIP-seq), combining chromatin immunoprecipitation (ChIP) and massively parallel sequencing to identify mammalian DNA sequences bound by transcription factors in vivo. We used ChIP-seq to map STAT1 targets in interferon stimulated and unstimulated human HeLa S3 cells, and compared the method's performance to ChIP-PCR and to ChIP-chip for four chromosomes. By ChIP-seq, using 15.1 and 12.9 million uniquely mapped sequence reads, and an estimated false discovery rate of less than 0.001, we identified 41,582 and 11,004 putative STAT1-binding regions in stimulated and unstimulated cells, respectively. Of the 34 loci known to contain STAT1 interferon-responsive binding sites, ChIP-seq found 24 (71%). ChIP-seq targets were enriched in sequences similar to known STAT1 binding motifs. Comparisons with two ChIP-PCR data sets suggested that ChIP-seq sensitivity was between 70% and 92% and specificity was at least 95%. Use ChIP-seq to map STAT1 targets in interferon-gamma (IFN-gamma)-stimulated and unstimulated human HeLa S3 cells, and compared the method's performance to ChIP-PCR and to ChIP-chip for four chromosomes

This repository contains datasets necessary for using the Virtual ChIP-seq software.

Virtual ChIP-seq requires the following datasets to predict transcription factor binding:

chipExpDir_AtoH_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters A-H.

chipExpDir_ItoZ_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters I-Z.

refTables_V1.1.0.tar.gz: PhastCons genomic conservation, FIMO PWM scores for JASPAR motifs, and ChIP-seq data of ENCODE and Cistrome database.

hg38_chrsize.tsv: Length of chromosomes in hg38

trainedModels_V1.0.0.tar.gz: Virtual ChIP-seq scikit-learn trained models saved in joblib format

.tar.gz: Pre-calculated matrices suitable for training with other algorithms or re-training with Virtual ChIP-seq.

Some predictive features of TF binding are the same in each cell type and are stored together for simplicity in refTables_V1.0.0.tar.gz. You can use datasets from other cell types (named here as .tar.gz) for the purpose of re-training the model. The .tar.gz files contain pre-calculated predictive features of transcription factor binding in 4 chromosomes (5, 10, 15, 20).

These features include:

PhastCons genomic conservation

FIMO score for sequence motifs of TF in the JASPAR database

Chromatin accessibility

TF binding in ENCODE + Cistrome DB datasets

Virtual ChIP-seq expression score

The advent of high-throughput sequencing has allowed genome wide profiling of histone modifications by Chromatin ImmunoPrecipitation (ChIP) followed by sequencing (ChIP-seq). In this assay the histone mark of interest is enriched through a chromatin pull-down assay using an antibody for the mark. Due to imperfect antibodies and other factors, many of the sequenced fragments do not originate from the histone mark of interest, and are referred to as background reads. Background reads are not uniformly distributed and therefore control samples are usually used to estimate the background distribution at any given genomic position. The Encyclopedia of DNA Elements (ENCODE) Consortium guidelines suggest sequencing a whole cell extract (WCE, or “input”) sample, or a mock ChIP reaction such as an IgG control, as a background sample. However, for a histone modification ChIP-seq investigation it is also possible to use a Histone H3 (H3) pull-down to map the underlying distribution of histones. In this paper we generated data from a hematopoietic stem and progenitor cell population isolated from mouse foetal liver to compare WCE and H3 ChIP-seq as control samples. The quality of the control samples is estimated by a comparison to pull-downs of histone modifications and to expression data. We find minor differences between WCE and H3 ChIP-seq, such as coverage in mitochondria and behaviour close to transcription start sites. Where the two controls differ, the H3 pull-down is generally more similar to the ChIP-seq of histone modifications. However, the differences between H3 and WCE have a negligible impact on the quality of a standard analysis. WCE and histone H3 ChIP-seq samples are compared to H3K27me3 ChIP-seq and RNA-seq.

ChIP-Seq data for 7 Acute myeloid leukemia sample(s). 23 run(s), 23 experiment(s), 23 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

Numbers are from the analysis of two merged replicate datasets.