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Epigenomics ag chip seq data

Overview of Deep5hmC. ( A ) The training set of Deep5hmC can be derived from matched 5hmC-seq and other epigenetic data such as <t>histone</t> <t>ChIP-seq</t> or DNase-seq/ATAC-seq from one condition. Specifically, the 5hmC-seq data can be collected from tissue-specific human tissues, which include bladder, brain, breast, heart, kidney, liver, lung, marrow, ovary (female), pancreas, placenta (female), prostate (male), colon (sigmoid), colon (transverse), skin, stomach, and testis (male). The matched tissue-specific epigenetic data, such as histone ChIP-seq data profiling histone modification and DNase-seq/ATAC-seq profiling chromatin accessibility, can be collected from public consortiums such as Roadmap Epigenomics and ENCODE. In this context, Deep5hmC aims to predict genome-wide 5hmC modification in a single condition. ( B ) The training set of Deep5hmC can also be derived from matched 5hmC-seq and ChIP-seq from a case–control study (e.g. Alzheimer’s disease (AD) versus healthy control) for predicting differentially hydroxymethylated regions (DhMRs). ( C ) Deep5hmC is a multimodal deep learning model to improve the prediction of tissue/cell type-specific genome-wide 5hmC modification by leveraging both DNA sequence and epigenetic features such as histone modification and chromatin accessibility. Deep5hmC consists of four modules, including Deep5hmC-binary, Deep5hmC-cont, Deep5hmC-gene, and Deep5hmC-diff. Specifically, Deep5hmC-binary takes the labeled 5hmC peaks and non-peaks as the training set to identify the 5hmC-enriched regions. Deep5hmC-cont takes the normalized read counts in 5hmC peaks and aims to predict the continuous 5hmC modification genome-wide. By leveraging Deep5hmC-cont, Deep5hmC-gene aggregates the predictions of Deep5hmC-cont in the gene bodies as the surrogate for the predicted gene expression. Different from Deep5hmC-binary, Deep5hmC-diff takes the labeled DhMRs/non-DhMRs in a case–control design of 5hmC-seq as the training set to predict genome-wide DhMRs and may discover de novo DhMRs. ( D ) Model architecture of Deep5hmC. Deep5hmC consists of both sequence modality and epigenetic modality consisting of their own convolutional neural networks (CNNs) to derive separate feature representations, which will be joined later via the multi-modal factorized bilinear (MFB) pooling fusion layer. The output of the MFB fusion layer will further connect to fully connected layers and the output layer afterward.

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1) Product Images from "Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model"

Article Title: Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model

Journal: Bioinformatics

doi: 10.1093/bioinformatics/btae528

Overview of Deep5hmC. ( A ) The training set of Deep5hmC can be derived from matched 5hmC-seq and other epigenetic data such as histone ChIP-seq or DNase-seq/ATAC-seq from one condition. Specifically, the 5hmC-seq data can be collected from tissue-specific human tissues, which include bladder, brain, breast, heart, kidney, liver, lung, marrow, ovary (female), pancreas, placenta (female), prostate (male), colon (sigmoid), colon (transverse), skin, stomach, and testis (male). The matched tissue-specific epigenetic data, such as histone ChIP-seq data profiling histone modification and DNase-seq/ATAC-seq profiling chromatin accessibility, can be collected from public consortiums such as Roadmap Epigenomics and ENCODE. In this context, Deep5hmC aims to predict genome-wide 5hmC modification in a single condition. ( B ) The training set of Deep5hmC can also be derived from matched 5hmC-seq and ChIP-seq from a case–control study (e.g. Alzheimer’s disease (AD) versus healthy control) for predicting differentially hydroxymethylated regions (DhMRs). ( C ) Deep5hmC is a multimodal deep learning model to improve the prediction of tissue/cell type-specific genome-wide 5hmC modification by leveraging both DNA sequence and epigenetic features such as histone modification and chromatin accessibility. Deep5hmC consists of four modules, including Deep5hmC-binary, Deep5hmC-cont, Deep5hmC-gene, and Deep5hmC-diff. Specifically, Deep5hmC-binary takes the labeled 5hmC peaks and non-peaks as the training set to identify the 5hmC-enriched regions. Deep5hmC-cont takes the normalized read counts in 5hmC peaks and aims to predict the continuous 5hmC modification genome-wide. By leveraging Deep5hmC-cont, Deep5hmC-gene aggregates the predictions of Deep5hmC-cont in the gene bodies as the surrogate for the predicted gene expression. Different from Deep5hmC-binary, Deep5hmC-diff takes the labeled DhMRs/non-DhMRs in a case–control design of 5hmC-seq as the training set to predict genome-wide DhMRs and may discover de novo DhMRs. ( D ) Model architecture of Deep5hmC. Deep5hmC consists of both sequence modality and epigenetic modality consisting of their own convolutional neural networks (CNNs) to derive separate feature representations, which will be joined later via the multi-modal factorized bilinear (MFB) pooling fusion layer. The output of the MFB fusion layer will further connect to fully connected layers and the output layer afterward.

Figure Legend Snippet: Overview of Deep5hmC. ( A ) The training set of Deep5hmC can be derived from matched 5hmC-seq and other epigenetic data such as histone ChIP-seq or DNase-seq/ATAC-seq from one condition. Specifically, the 5hmC-seq data can be collected from tissue-specific human tissues, which include bladder, brain, breast, heart, kidney, liver, lung, marrow, ovary (female), pancreas, placenta (female), prostate (male), colon (sigmoid), colon (transverse), skin, stomach, and testis (male). The matched tissue-specific epigenetic data, such as histone ChIP-seq data profiling histone modification and DNase-seq/ATAC-seq profiling chromatin accessibility, can be collected from public consortiums such as Roadmap Epigenomics and ENCODE. In this context, Deep5hmC aims to predict genome-wide 5hmC modification in a single condition. ( B ) The training set of Deep5hmC can also be derived from matched 5hmC-seq and ChIP-seq from a case–control study (e.g. Alzheimer’s disease (AD) versus healthy control) for predicting differentially hydroxymethylated regions (DhMRs). ( C ) Deep5hmC is a multimodal deep learning model to improve the prediction of tissue/cell type-specific genome-wide 5hmC modification by leveraging both DNA sequence and epigenetic features such as histone modification and chromatin accessibility. Deep5hmC consists of four modules, including Deep5hmC-binary, Deep5hmC-cont, Deep5hmC-gene, and Deep5hmC-diff. Specifically, Deep5hmC-binary takes the labeled 5hmC peaks and non-peaks as the training set to identify the 5hmC-enriched regions. Deep5hmC-cont takes the normalized read counts in 5hmC peaks and aims to predict the continuous 5hmC modification genome-wide. By leveraging Deep5hmC-cont, Deep5hmC-gene aggregates the predictions of Deep5hmC-cont in the gene bodies as the surrogate for the predicted gene expression. Different from Deep5hmC-binary, Deep5hmC-diff takes the labeled DhMRs/non-DhMRs in a case–control design of 5hmC-seq as the training set to predict genome-wide DhMRs and may discover de novo DhMRs. ( D ) Model architecture of Deep5hmC. Deep5hmC consists of both sequence modality and epigenetic modality consisting of their own convolutional neural networks (CNNs) to derive separate feature representations, which will be joined later via the multi-modal factorized bilinear (MFB) pooling fusion layer. The output of the MFB fusion layer will further connect to fully connected layers and the output layer afterward.

Techniques Used: Derivative Assay, ChIP-sequencing, Modification, Genome Wide, Control, Sequencing, Labeling, Expressing

Distribution pattern of histone modification around 5hmC peaks. EB 5hmC peaks are collected from “Forebrain Organoid” 5hmC-seq data and ChIP-seq data in “Brain Angular Gyrus” from seven histone marks are collected from Roadmap Epigenomics. Histone features are obtained and averaged in the neighborhood of all 5hmC peaks for the positive and negative sets, respectively. Specifically, histone features are created by segmenting an extended genomic region of 10 kb both upstream and downstream of each 5hmC peak into 41 1 kb windows with a sliding size of 500 bp and counting reads for each 1 kb windows. For each histone mark, the Kolmogorov–Smirnov test is performed to test the distribution difference of histone features between positive and negative 5hmC peaks and the P -value is reported.

Figure Legend Snippet: Distribution pattern of histone modification around 5hmC peaks. EB 5hmC peaks are collected from “Forebrain Organoid” 5hmC-seq data and ChIP-seq data in “Brain Angular Gyrus” from seven histone marks are collected from Roadmap Epigenomics. Histone features are obtained and averaged in the neighborhood of all 5hmC peaks for the positive and negative sets, respectively. Specifically, histone features are created by segmenting an extended genomic region of 10 kb both upstream and downstream of each 5hmC peak into 41 1 kb windows with a sliding size of 500 bp and counting reads for each 1 kb windows. For each histone mark, the Kolmogorov–Smirnov test is performed to test the distribution difference of histone features between positive and negative 5hmC peaks and the P -value is reported.

Techniques Used: Modification, ChIP-sequencing

Comparison of unimodal and multimodal Deep5hmC for predicting binary 5hmC modification sites. When using histone modification in the epigenetic modality, two unimodal models of Deep5hmC: Deep5hmC-Seq using only DNA sequence as the model input and Deep5hmC-His using only histone modification as the model input are compared to the default multimodal Deep5hmC-Seq+His using both DNA sequence and histone modification as the model input. 5hmC peaks from the EB stage “Forebrain Organoid” and two histone marks: H3K4me1 and H3K4me3 ChIP-seq data in all brain regions from Roadmap Epigenomics are used as the training set. ( A ) AUROC reported for three compared methods. ( B ) AUPRC reported three compared methods.

Figure Legend Snippet: Comparison of unimodal and multimodal Deep5hmC for predicting binary 5hmC modification sites. When using histone modification in the epigenetic modality, two unimodal models of Deep5hmC: Deep5hmC-Seq using only DNA sequence as the model input and Deep5hmC-His using only histone modification as the model input are compared to the default multimodal Deep5hmC-Seq+His using both DNA sequence and histone modification as the model input. 5hmC peaks from the EB stage “Forebrain Organoid” and two histone marks: H3K4me1 and H3K4me3 ChIP-seq data in all brain regions from Roadmap Epigenomics are used as the training set. ( A ) AUROC reported for three compared methods. ( B ) AUPRC reported three compared methods.

Techniques Used: Comparison, Modification, Sequencing, ChIP-sequencing

other:

Article Title: Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.
Article Snippet: Without loss of generality, we utilize 5hmC peaks from four developmental stages in “Forebrain Organoid” and ChIP-seq data from two histone marks: H3K4me1 and H3K4me3 collected from all brain regions in Roadmap Epigenomics (Supplementary Table S2).

Article Title: Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.
Article Snippet: Specifically, we choose 5hmC peaks from four developmental stages in “Forebrain Organoid” and ChIP-seq data from all brain regions of seven histone marks in Roadmap Epigenomics (Supplement Table S1) and evaluate the predictive performance for each histone mark in terms of AUROC and AUPRC.

Article Title: Identification of causal genes and mechanisms by which genetic variation mediates juvenile idiopathic arthritis susceptibility using functional genomics and CRISPR-Cas9
Article Snippet: The regulatory roles of individual SNPs identified in the most recent JIA GWAS [ ] were investigated through annotation that incorporated publicly available ChIP-seq data from the Roadmap Epigenomics Consortium [ ], EpiMap predicted immune cell related enhancers [ ], as well as in-house generated ATAC-seq peaks and PCHi-C data from the 3 JIA CD4+ T cell samples.

Article Title: Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.
Article Snippet: In cases where tissue-matched ChIP-seq data is unavailable at Roadmap Epigenomics, we retrieve it from ENCODE portal (Sloan et al., 2016) (https://www.encodeproject.org/).

Article Title: Different genome-wide DNA methylation patterns in CD4+ T lymphocytes and CD14+ monocytes characterize relapse and remission of multiple sclerosis: Focus on GNAS.
Article Snippet: Background: Relapsing-remitting multiple sclerosis (RRMS) is a most common form of multiple sclerosis in which periods of neurological worsening are followed by periods of clinical remission.. RRMS relapses are caused by an acute autoimmune inflammatory process, which can occur in any area of the central nervous system.. Although development of exacerbation cannot yet be accurately predicted, various external factors are known to affect its risk.

Article Title: Program of the 94 th Annual Meeting of the American Association of Biological Anthropologists.
Article Snippet: Book i PROGRAM OF THE 94TH ANNUAL MEETING OF THE AMERICAN ASSOCIATION OF BIOLOGICAL ANTHROPOLOGISTS

Functional Assay:

Article Title: Targeting aryl hydrocarbon receptor functionally restores tolerogenic dendritic cells derived from patients with multiple sclerosis
Article Snippet: .. Bars represent log-transformed binomial q values of the GO term enrichment for the HD mDCs - MS mDCs contrast. (B) Chromatin functional state enrichment analysis of the differentially hypomethylated probes in HD-MS mDC contrast, based on CD14+ primary cells ChromHMM public data from Roadmap Epigenomics Project. ..

Image Search Results

a Re-expression of WT and mutant EPOP in mESCs. Representative of two biological replicates . b Co-IP assay of re-expressed EPOP. The re-expressed 3 × FLAG-EPOP-HA protein was used as the bait, and the bound endogenous SUZ12 was detected. Representative of two replicates. c Schematic of the EpiLC differentiation. Some parts of the figure were created in BioRender. Liu, X. (2026) ( https://biorender.com/gvraoqv ). d Heatmaps of ChIP-seq replicates. Using the FDR < 0.05 threshold, the gain-of-signal MTF2 (purple) and H3K27me3 (green) peaks from individual replicates (WT-1, WT-2, D5-1, and D5-2) are shown in the heatmaps. The centers of the consensus binding sites are aligned. The number of the consensus binding sites is labeled. e Metaplots of ChIP-seq replicates. The mean ChIP-seq signal of the differential MTF2 (upper panel) and H3K27me3 (lower panel) peaks shown in ( d ) is plotted. The individual replicates are color-coded. f Genome browser tracks of selected gene loci. Tracks were generated by SparK ( https://github.com/harbourlab/SparK ). Genomic coordinates are provided. The difference between the replicates, calculated as the standard deviation, is indicated as the shaded areas surrounding the tracks. g Scatter plots of MTF2 and H3K27me3. MTF2 (left panel) and H3K27me3 (right panel) reads were normalized by the total reads. The mean read concentration corresponding to log2 (normalized ChIP-seq reads with input reads subtracted) was calculated by DiffBind.

Journal: Nature Communications

Article Title: EPOP restricts PRC2.1 targeting to chromatin by directly modulating enzyme complex dimerization

doi: 10.1038/s41467-025-68280-5

Figure Lengend Snippet: a Re-expression of WT and mutant EPOP in mESCs. Representative of two biological replicates . b Co-IP assay of re-expressed EPOP. The re-expressed 3 × FLAG-EPOP-HA protein was used as the bait, and the bound endogenous SUZ12 was detected. Representative of two replicates. c Schematic of the EpiLC differentiation. Some parts of the figure were created in BioRender. Liu, X. (2026) ( https://biorender.com/gvraoqv ). d Heatmaps of ChIP-seq replicates. Using the FDR < 0.05 threshold, the gain-of-signal MTF2 (purple) and H3K27me3 (green) peaks from individual replicates (WT-1, WT-2, D5-1, and D5-2) are shown in the heatmaps. The centers of the consensus binding sites are aligned. The number of the consensus binding sites is labeled. e Metaplots of ChIP-seq replicates. The mean ChIP-seq signal of the differential MTF2 (upper panel) and H3K27me3 (lower panel) peaks shown in ( d ) is plotted. The individual replicates are color-coded. f Genome browser tracks of selected gene loci. Tracks were generated by SparK ( https://github.com/harbourlab/SparK ). Genomic coordinates are provided. The difference between the replicates, calculated as the standard deviation, is indicated as the shaded areas surrounding the tracks. g Scatter plots of MTF2 and H3K27me3. MTF2 (left panel) and H3K27me3 (right panel) reads were normalized by the total reads. The mean read concentration corresponding to log2 (normalized ChIP-seq reads with input reads subtracted) was calculated by DiffBind.

Article Snippet: For the shRNA KD ChIP-seq data sequenced by Novogene, the raw sequencing reads were trimmed by Cutadapt and then mapped by Bowtie 2 with the “--very-sensitive” parameter.

Techniques: Expressing, Mutagenesis, Co-Immunoprecipitation Assay, ChIP-sequencing, Binding Assay, Labeling, Generated, Standard Deviation, Concentration Assay

a Heatmaps of ChIP-seq replicates. The differential MTF2 peaks between the EPOP WT and EPOP D5 EpiLCs were grouped into three categories based on the response to shRNA KD, using the FDR < 0.05 threshold. Differential peaks shared by the control and Elongin B KD are labeled as n1, differential peaks unique to the control KD are labeled as n2, and differential peaks unique to the Elongin B KD are labeled as n3. b Genome browser tracks of selected gene loci. Tracks were generated by SparK. Two gene loci associated with the shared differential MTF2 peaks between the control KD and the Elongin B KD are shown. c Venn diagram of the differential MTF2 peaks in the three categories.

Journal: Nature Communications

Article Title: EPOP restricts PRC2.1 targeting to chromatin by directly modulating enzyme complex dimerization

doi: 10.1038/s41467-025-68280-5

Figure Lengend Snippet: a Heatmaps of ChIP-seq replicates. The differential MTF2 peaks between the EPOP WT and EPOP D5 EpiLCs were grouped into three categories based on the response to shRNA KD, using the FDR < 0.05 threshold. Differential peaks shared by the control and Elongin B KD are labeled as n1, differential peaks unique to the control KD are labeled as n2, and differential peaks unique to the Elongin B KD are labeled as n3. b Genome browser tracks of selected gene loci. Tracks were generated by SparK. Two gene loci associated with the shared differential MTF2 peaks between the control KD and the Elongin B KD are shown. c Venn diagram of the differential MTF2 peaks in the three categories.

Article Snippet: For the shRNA KD ChIP-seq data sequenced by Novogene, the raw sequencing reads were trimmed by Cutadapt and then mapped by Bowtie 2 with the “--very-sensitive” parameter.

Techniques: ChIP-sequencing, shRNA, Control, Labeling, Generated

a Volcano plot of differential gene expression. RNA-seq results of the EPOP D5 EpiLCs in triplicates were compared to those of the EPOP WT EpiLCs. The number of upregulated and downregulated genes is indicated. Data passing the FDR < 0.05, FC > 1.5, and average TPM of WT or mutant > 0.5 thresholds were analyzed. b Correlation of RNA-seq and ChIP-seq. One ChIP-seq replicate is shown here, and the other replicate is shown in the supplemental materials. The differential gene expression was aligned with the differential MTF2 enrichment around the transcription start site (TSS). The corresponding H3K27me3 signals are also displayed. Compared to the WT counterpart, 130 genes in the EPOP D5 EpiLCs were downregulated and associated with enhanced MTF2 signals around the TSS. The other 318 downregulated genes were not associated with EPOP-regulated MTF2 targeting. 187 upregulated genes are shown as well. c Gene ontology analysis. The PRC2.1-repressed, EPOP-maintained genes were subjected to gene ontology analysis on the DAVID server. The top 5 overrepresented terms in molecular function are shown. The p -value is one-tail Fisher Exact probability value used for gene-enrichment analysis by the DAVID server. d Schematic model of developmental gene repression by PRC2. On the left, the transient intrinsic dimer of the PRC2 core complex is illustrated. In the middle, distinct oligomerization states of various PRC2.1 and PRC2.2 holocomplexes are highlighted. MTF2 mediates direct chromatin binding, and it also stabilizes the intrinsic dimer, promoting chromatin targeting of the dimeric PRC2.1, likely via an avidity effect. EPOP disrupts the dimeric architecture of PRC2.1, containing MTF2, restricts PRC2.1 targeting, and thereby maintains the limited expression of PRC2.1-repressed developmental regulators. On the right, the PRC2.1-dependent role of EPOP in early development is illustrated. Black solid curve: during the ESC differentiation, a set of key gene regulators is repressed by PRC2.1, with limited expression being maintained by the EPOP-mediated inhibition of PRC2.1 targeting, which is followed by upregulation of the same set of gene regulators, leading to cell fate 1, e.g., PGCLCs. Gray dotted curve: the absence of EPOP results in the over-repression of these gene regulators by PRC2.1, which may change stem cell differentiation trajectories and result in an alternative cell fate 2. Some parts of the figure were created in BioRender. Liu, X. (2026) ( https://biorender.com/l3ji249 ).

Journal: Nature Communications

Article Title: EPOP restricts PRC2.1 targeting to chromatin by directly modulating enzyme complex dimerization

doi: 10.1038/s41467-025-68280-5

Figure Lengend Snippet: a Volcano plot of differential gene expression. RNA-seq results of the EPOP D5 EpiLCs in triplicates were compared to those of the EPOP WT EpiLCs. The number of upregulated and downregulated genes is indicated. Data passing the FDR < 0.05, FC > 1.5, and average TPM of WT or mutant > 0.5 thresholds were analyzed. b Correlation of RNA-seq and ChIP-seq. One ChIP-seq replicate is shown here, and the other replicate is shown in the supplemental materials. The differential gene expression was aligned with the differential MTF2 enrichment around the transcription start site (TSS). The corresponding H3K27me3 signals are also displayed. Compared to the WT counterpart, 130 genes in the EPOP D5 EpiLCs were downregulated and associated with enhanced MTF2 signals around the TSS. The other 318 downregulated genes were not associated with EPOP-regulated MTF2 targeting. 187 upregulated genes are shown as well. c Gene ontology analysis. The PRC2.1-repressed, EPOP-maintained genes were subjected to gene ontology analysis on the DAVID server. The top 5 overrepresented terms in molecular function are shown. The p -value is one-tail Fisher Exact probability value used for gene-enrichment analysis by the DAVID server. d Schematic model of developmental gene repression by PRC2. On the left, the transient intrinsic dimer of the PRC2 core complex is illustrated. In the middle, distinct oligomerization states of various PRC2.1 and PRC2.2 holocomplexes are highlighted. MTF2 mediates direct chromatin binding, and it also stabilizes the intrinsic dimer, promoting chromatin targeting of the dimeric PRC2.1, likely via an avidity effect. EPOP disrupts the dimeric architecture of PRC2.1, containing MTF2, restricts PRC2.1 targeting, and thereby maintains the limited expression of PRC2.1-repressed developmental regulators. On the right, the PRC2.1-dependent role of EPOP in early development is illustrated. Black solid curve: during the ESC differentiation, a set of key gene regulators is repressed by PRC2.1, with limited expression being maintained by the EPOP-mediated inhibition of PRC2.1 targeting, which is followed by upregulation of the same set of gene regulators, leading to cell fate 1, e.g., PGCLCs. Gray dotted curve: the absence of EPOP results in the over-repression of these gene regulators by PRC2.1, which may change stem cell differentiation trajectories and result in an alternative cell fate 2. Some parts of the figure were created in BioRender. Liu, X. (2026) ( https://biorender.com/l3ji249 ).

Article Snippet: For the shRNA KD ChIP-seq data sequenced by Novogene, the raw sequencing reads were trimmed by Cutadapt and then mapped by Bowtie 2 with the “--very-sensitive” parameter.

Techniques: Gene Expression, RNA Sequencing, Mutagenesis, ChIP-sequencing, Binding Assay, Expressing, Inhibition, Cell Differentiation

HMGN proteins localize to transcriptionally active regions of the genome . A , genome browser tracks of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq signal at the promoter of Sox2 and the super-enhancer domain downstream of Sox2 in WT mESCs. B , Pearson’s correlation hierarchical clustering heatmap of genome-wide signal of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq datasets in WT mESCs. C , bar graph of the number of expressed genes and non-expressed genes in the mouse embryonic stem cell (mESC) genome bound and not bound by HMGN1 and HMGN2. Active genes are defined as genes with a RPKM value ≥22 as defined by the EMBL Expression Atlas. D , UpSet plot of HMGN1 ChIP-Seq peaks in WT mESCs displaying intersection of sets of peaks at H3K27ac, H3K4me3, transcription start sites (TSSs), H2A.Z, RAD21, CTCF, and other sites. E , bar graph of the number of HMGN1 peaks that overlap with H3K4me3, H3K27ac, CTCF, H2A.Z, TSSs, RAD21, and other peaks in WT mESCs. F , average signal plot of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq signal at a union list of all HMGN1 and HMGN2 peaks (Z-score normalized). G , clustered heatmaps of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq signal at active enhancers, active promoters, and insulator sites, ordered by HMGN2 signal (Z-score normalized). H , average signal plots of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq signal in WT mESCs at active enhancers, active promoters, and insulator sites (Z-score normalized). ChIP-Seq, chromatin immunoprecipitation followed by sequencing; HMGN, High Mobility Nucleosome-binding protein; mESC, mouse embryonic stem cell; RPKM, reads per kilobase of transcript per million mapped reads.

Journal: The Journal of Biological Chemistry

Article Title: HMGN1 and HMGN2 are recruited to acetylated and histone variant H2A.Z-containing nucleosomes to regulate chromatin state and transcription

doi: 10.1016/j.jbc.2025.110997

Figure Lengend Snippet: HMGN proteins localize to transcriptionally active regions of the genome . A , genome browser tracks of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq signal at the promoter of Sox2 and the super-enhancer domain downstream of Sox2 in WT mESCs. B , Pearson’s correlation hierarchical clustering heatmap of genome-wide signal of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq datasets in WT mESCs. C , bar graph of the number of expressed genes and non-expressed genes in the mouse embryonic stem cell (mESC) genome bound and not bound by HMGN1 and HMGN2. Active genes are defined as genes with a RPKM value ≥22 as defined by the EMBL Expression Atlas. D , UpSet plot of HMGN1 ChIP-Seq peaks in WT mESCs displaying intersection of sets of peaks at H3K27ac, H3K4me3, transcription start sites (TSSs), H2A.Z, RAD21, CTCF, and other sites. E , bar graph of the number of HMGN1 peaks that overlap with H3K4me3, H3K27ac, CTCF, H2A.Z, TSSs, RAD21, and other peaks in WT mESCs. F , average signal plot of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq signal at a union list of all HMGN1 and HMGN2 peaks (Z-score normalized). G , clustered heatmaps of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq signal at active enhancers, active promoters, and insulator sites, ordered by HMGN2 signal (Z-score normalized). H , average signal plots of HMGN1, HMGN2, H3K27ac, H3K4me3, H2A.Z, RAD21, and CTCF ChIP-Seq signal in WT mESCs at active enhancers, active promoters, and insulator sites (Z-score normalized). ChIP-Seq, chromatin immunoprecipitation followed by sequencing; HMGN, High Mobility Nucleosome-binding protein; mESC, mouse embryonic stem cell; RPKM, reads per kilobase of transcript per million mapped reads.

Article Snippet: Previously collected and reported ChIP-Seq data used in this study are summarized in and can also be found in the National Center for Biotechnology Information Gene Expression Omnibus repository.

Techniques: ChIP-sequencing, Genome Wide, Expressing, Chromatin Immunoprecipitation, Sequencing, Binding Assay

Cohesin and CTCF localization on chromatin is not dependent on HMGN1 or HMGN2 . A , genome browser tracks of RAD21 and CTCF ChIP-Seq signal near the promoter of Zbp1 (differentially expressed gene in Hmgn1 −/− Hmgn2 −/− mESCs) in WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs. B , MA plot showing differential enrichment of RAD21 signal between WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs at conserved binding sites. C , MA plot showing differential enrichment of CTCF signal between WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs at conserved binding sites. D , average signal plots of RAD21 and CTCF ChIP-Seq signal at a union list of all HMGN1 and HMGN2 peaks in WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs (Z-score normalized). E , average signal plots of RAD21 and CTCF ChIP-Seq signal in WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs at CTCF sites, cohesin sites, active enhancers, and transcription start sites (TSSs). F , ChIP-Seq signal of RAD21 and CTCF in WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs shown at the promoters of upregulated and downregulated differently expressed genes in either Hmgn1 −/− mESCs, Hmgn2 −/− mESCs, or Hmgn1 −/− Hmgn2 −/− mESCs. ChIP-Seq, chromatin immunoprecipitation followed by sequencing; HMGN, High Mobility Nucleosome-binding protein; mESC, mouse embryonic stem cell.

Journal: The Journal of Biological Chemistry

Article Title: HMGN1 and HMGN2 are recruited to acetylated and histone variant H2A.Z-containing nucleosomes to regulate chromatin state and transcription

doi: 10.1016/j.jbc.2025.110997

Figure Lengend Snippet: Cohesin and CTCF localization on chromatin is not dependent on HMGN1 or HMGN2 . A , genome browser tracks of RAD21 and CTCF ChIP-Seq signal near the promoter of Zbp1 (differentially expressed gene in Hmgn1 −/− Hmgn2 −/− mESCs) in WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs. B , MA plot showing differential enrichment of RAD21 signal between WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs at conserved binding sites. C , MA plot showing differential enrichment of CTCF signal between WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs at conserved binding sites. D , average signal plots of RAD21 and CTCF ChIP-Seq signal at a union list of all HMGN1 and HMGN2 peaks in WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs (Z-score normalized). E , average signal plots of RAD21 and CTCF ChIP-Seq signal in WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs at CTCF sites, cohesin sites, active enhancers, and transcription start sites (TSSs). F , ChIP-Seq signal of RAD21 and CTCF in WT mESCs and Hmgn1 −/− Hmgn2 −/− mESCs shown at the promoters of upregulated and downregulated differently expressed genes in either Hmgn1 −/− mESCs, Hmgn2 −/− mESCs, or Hmgn1 −/− Hmgn2 −/− mESCs. ChIP-Seq, chromatin immunoprecipitation followed by sequencing; HMGN, High Mobility Nucleosome-binding protein; mESC, mouse embryonic stem cell.

Techniques: ChIP-sequencing, Binding Assay, Chromatin Immunoprecipitation, Sequencing

Chrom-Sig results on GM12878 CTCF ChIP-seq and CUT&RUN datasets. (a) Browser views of CTCF binding motifs with orientation (blue triangles) and coverage tracks generated by piling up the original (before Chrom-Sig) and Chrom-Sig ‘pass’ or ‘fail’ reads, accompanied by the peaks called by SICER. (b) Venn diagram of the peaks called on the original and Chrom-Sig ‘pass’ reads pile-up reads using false discovery rate (FDR) of 0.1 and 5000 pseudo-reads, with boxplots of maximum peak intensity for each peak. (c) Number of peaks overlapping CTCF motifs, and top MEME result on the original GM12878 CTCF CUT&RUN data before Chrom-Sig. (d) Similar to panel c for Chrom-Sig ‘pass’ results.

Journal: Bioinformatics

Article Title: Chrom-Sig: de-noising 1D genomic profiles by signal processing methods

doi: 10.1093/bioinformatics/btaf645

Figure Lengend Snippet: Chrom-Sig results on GM12878 CTCF ChIP-seq and CUT&RUN datasets. (a) Browser views of CTCF binding motifs with orientation (blue triangles) and coverage tracks generated by piling up the original (before Chrom-Sig) and Chrom-Sig ‘pass’ or ‘fail’ reads, accompanied by the peaks called by SICER. (b) Venn diagram of the peaks called on the original and Chrom-Sig ‘pass’ reads pile-up reads using false discovery rate (FDR) of 0.1 and 5000 pseudo-reads, with boxplots of maximum peak intensity for each peak. (c) Number of peaks overlapping CTCF motifs, and top MEME result on the original GM12878 CTCF CUT&RUN data before Chrom-Sig. (d) Similar to panel c for Chrom-Sig ‘pass’ results.

Article Snippet: For example, the GM12878 CTCF ChIP-seq data were originally noisy with a large portion of reads in non-binding sites, but Chrom-Sig with FDR of 0.1 retained only the reads with strong binding ( ).

Techniques: ChIP-sequencing, Binding Assay, Generated

Review

chip seq data (Epigenomics ag)

Structured Review

Images

1) Product Images from "Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model"

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