Journal: bioRxiv

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

doi: 10.1101/2025.08.12.670000

Figure Lengend Snippet: Paired End Example for GM12878 Chrom-Sig results for all paired-end datasets from GM12878 cell-line, visualized in the genome browser. CTCF Motif: CTCF binding sites with orientation. Original: Bedgraph file generated directly from input BAM/bed file. SICER peaks: Bed file result of running SICER algorithm on the original bedgraph file. Chrom-Sig FDR 0.1 pass: pass bedgraph generated from original bedgraph by Chrom-Sig (percentage refers to how many reads were retained by Chrom-Sig result from original bedgraph). SICER peaks (below Chrom-Sig FDR 0.1 pass): Bed file from SICER algorithm run on pass-pileup bed generated by Chrom-Sig. ChromHMM: Chromatin states.

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: Binding Assay, Generated

Journal: bioRxiv

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

doi: 10.1101/2025.08.12.670000

Figure Lengend Snippet: Single End Example Chrom-Sig results for all single-end datasets (all single-end data is from GM12878 cell-line), visualized in the genome browser. CTCF Motif: CTCF binding sites with orientation. Original: Bedgraph file generated directly from input BAM/bed file. SICER peaks: Bed file result of running SICER algorithm on the original bedgraph file. Chrom-Sig FDR 0.1 pass: pass bedgraph generated from original bedgraph by Chrom-Sig (percentage refers to how many reads were retained by Chrom-Sig result from original bedgraph). SICER peaks (below Chrom-Sig FDR 0.1 pass): Bed file from SICER algorithm run on pass-pileup bed generated by Chrom-Sig. ChromHMM: Chromatin states.

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: Binding Assay, Generated

Journal: bioRxiv

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

doi: 10.1101/2025.08.12.670000

Figure Lengend Snippet: CTCF Motif Analyses a) Top enriched motifs, E-value, and matching motifs from MEME-Chip for GM12878 CUT&RUN CTCF 4DNFI2G71DR4 before and after Chrom-Sig. b) Comparison of CTCF motif precision between original data and Chrom-Sig with FDR 0.1 and 5000 pseudo-reads for GM12878 ChIP-seq CTCF ENCFF355CYX, GM12878 CUT&RUN CTCF 4DNFI2G71DR4 and 4DNFI9U71IB4.

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: Comparison, ChIP-sequencing

Journal: bioRxiv

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

doi: 10.1101/2025.08.12.670000

Figure Lengend Snippet: ChromHMM State Annotation Distribution Comparison of the distribution of chromHMM states between original data and Chrom-Sig with FDR 0.1 and 5000 pseudo-reads for K562 RNAPII ChIP-seq ENCFF480AJZ and ENCFF785OCU and GM12878 ATAC-seq ENCFF646NWY. The proportion of enhancer and promotor states increases when Chrom-Sig is applied to the data. Between K562 RNAPII ChIP-seq replicates there is an average of 12.3% higher distribution of enhancers and promotors (ENCFF480AJZ: 77% original vs 87.3% Chrom-Sig and ENCFF785OCU: 76.9% original vs 85.5% Chrom-Sig). In ATAC-seq data, the percentage of transcription and heterochromatin states drops from 28.9% to 12.6% after Chrom-Sig.

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: Comparison, ChIP-sequencing

Journal: bioRxiv

Article Title: MAPS: model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments

doi: 10.1101/411835

Figure Lengend Snippet: a. Schematic of the data analysis procedure. MAPS includes of two major components: pre-processing and interaction calling. In pre-processing, valid intra-chromosomal reads are obtained from raw sequencing file after mapping, reads pairing and duplicates removal, and then split into short-range and long-range reads depending on whether the pair of reads is separated by 1Kb or not. The long-range reads are further assigned into the “AND”, “XOR” or “NOT” set. MAPS only considers the “AND” and “XOR” set of bin pairs for normalization and identification of the statistically significant long-range chromatin interactions. Short-range reads are used to estimate and correct for biases introduced by the ChIP procedure. b. Comparison of sensitivity of MAPS and hichipper. The Y-axis is the sensitivity, defined as the percentage of detectable HiCCUPS loops of deeply sequenced in situ Hi-C data ( Table S3 ) recovered by MAPS-or hichipper-identified interactions. c. Assessment of reliability of MAPS and hichipper. The proportion of convergent, tandem and divergent CTCF motif pairs among testable MAPS- and hichipper-identified interactions. Only interactions with both ends containing either single CTCF motif or multiple CTCF motifs in the same direction are considered. The dotted vertical line indicates the expected convergent proportion from randomly chosen CTCF motif pairs (25%). d. Cis -regulatory elements are enriched in the target bins of MAPS-identified “XOR” interactions. As only interactions from “XOR” are considered, CTCF enrichment analysis is not applicable for mESC CTCF PLAC-seq data, and H3K4me3 enrichment analysis is not applicable for mESC H3K4me3 PLAC-seq data (denoted as N.A. in the heatmap). For each ChIP-seq/ATAC-seq data, we calculated the proportion of target bins and control bins containing ChIP- seq/ATAC-seq peaks, defined as %target and %control, respectively. We further defined the enrichment score as the ratio between %target and %control. e. Genome-browser shows MAPS-identified interactions anchored at Mtnr1a promoter from mESC H3K4me3 PLAC-seq data. Anchor regions around Mtnr1a promoter are highlighted by yellow box (chr8:45,065,000-45,075,000, two 5Kb bins). The MAPS-identified interactions overlapping this anchor region are marked by magenta arcs. The black arrow points to the interaction verified in the previous publication and the other end of the interaction is marked by magenta box. Additional interacting regions identified by MAPS are marked by grey boxes. No interaction is identified by hichipper anchored at this region from mESC H3K4me3 PLAC-seq data.

Article Snippet: This enrichment holds for H3K27ac, H3K4me1, H3K4me3 and CTCF ChIP-seq peaks (all Chi-square test p-values < 2.2e-16).

Techniques: Sequencing, Comparison, In Situ, Hi-C, ChIP-sequencing, Control

Journal: bioRxiv

Article Title: MAPS: model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments

doi: 10.1101/411835

Figure Lengend Snippet: a. Enrichment of CREs around the target bins of XOR set of MAPS-specific interactions. Enrichment of H3K27ac (ChIP-seq peaks), H3K4me1 (ChIP-seq peaks), ATAC-seq peaks, H3K4me3 (ChIP-seq peaks) and CTCF (ChIP-seq peaks) in a window of 500Kb around the target bins for all 3 datasets. Due to the definition of XOR set of interactions, H3K4me3 and CTCF enrichment level is not analyzed for mESC H3K4me3 and mESC CTCF PLAC-seq data, respectively. b. Frequency of MAPS-identified interactions and control bin pairs versus their rankings in the SPRITE contact matrix. A bin pair with higher normalized SPRITE interaction frequency tends to rank top, among all bin pairs with the same genomic distance (only bin pairs in “AND” and “XOR” sets from the SPRITE contact matrix are considered). c. MAPS-identified interactions from mESC H3K4me3 PLAC-seq data anchored at Med13l promoter. Anchor region around target promoter is highlighted by yellow box. The MAPS-identified interactions overlapping this anchor region are marked by magenta arcs. The deleted enhancer region in Moorthy et al study is marked by magenta box.

Article Snippet: This enrichment holds for H3K27ac, H3K4me1, H3K4me3 and CTCF ChIP-seq peaks (all Chi-square test p-values < 2.2e-16).

Techniques: ChIP-sequencing, Control

Journal: Genome Biology

Article Title: DNA G-quadruplex structures act as functional elements in α- and β-globin enhancers

doi: 10.1186/s13059-025-03627-1

Figure Lengend Snippet: Genome editing of enhancer G4s reveal functional roles in maintenance of open chromatin. A – B Integrative Genomics Viewer (IGV) browser view of G4 structures using BG4 and SG4 G4-structure-specific antibodies by CUT&Tag across the α- and β-globin loci. Significant ( q < 0.01) 3D interactions as determined by G4 ViCAR (GEO accession: GSE250219 ) are displayed above as red arcs. STARR-seq data from ENCODE indicates sequences with enhancer activity in reporter assays ( www.encodeproject.org ENCSR858MPS). Gray vertical shading indicates enhancer regions with G4 signal in HS-40 and HS2 enhancers. A schematic below indicates the position of the G4 motif relative to transcription factor binding sites. C – D Circular dichroism spectra of G4-forming sequences for α-globin HS-40 (left) and β-globin HS2 (right) enhancers and their corresponding mutated sequences in the presence of KCl or LiCl. E IGV browser view of G4 signal for BG4 and SG4 CUT&Tag at the α-globin HS-40 enhancer in wild type (WT), G4 mutated (αMut) and G4 restored (αRes) cells. F Bar plots of BG4 counts per million (cpm) normalized read counts at the α-globin HS-40 enhancer and non-targeted promoter G4 sites, KRAS and SDF4 in wild type (WT), G4 mutated (αMut) and G4 restored (αRes) cells ( n = 7) error bars represent the standard deviation (SD), *** P -value < 0.001, n.s is not significant. G IGV browser view of G4 signal for BG4 and SG4 CUT&Tag at the β-globin HS2 enhancer in wild type (WT) and G4 mutated (βMut) cells. H Bar plots of BG4 cpm normalized read counts at β-globin HS2 and non-targeted promoter G4 sites RBBP4 and SDF4 in wild type (WT) and G4 mutated (βMut) cells ( n = 6) error bars present the SD, *** P -value < 0.001, n.s is not significant. I IGV browser view of CUTAC signal at the α-globin HS-40 enhancer, comparing wild type vs G4 mutated (αMut), top; or G4 restored cells (αRes), bottom. Yellow bar highlights the position of the HS-40 G4 sequence motif. Scale bar indicates a 120-bp CUTAC region affected by HS-40 G4 loss. J IGV browser view of CUTAC signal at the β-globin HS2 enhancer comparing wild type (WT) vs G4 mutated (βMut) cells. Yellow bar highlights the position of the HS2 G4 sequence motif. See also Additional file 1: Figs. S1–S4

Article Snippet: The following ENCODE datasets were used: ENCSR858MPS (K562 STARR-seq) [ , ] ENCSR000EGM (file: ENCFF322EGW K562 CTCF ChIP-seq) [ ], ENCSR000EWF (file: ENCFF577NSA, K562 YY1 ChIP-seq) [ ].

Techniques: Functional Assay, Activity Assay, Binding Assay, Circular Dichroism, Standard Deviation, Sequencing

Journal: Nature

Article Title: Insulator dysfunction and oncogene activation in IDH mutant gliomas

doi: 10.1038/nature16490

Figure Lengend Snippet: Sequenced Libraries Characteristics.Pertinent statistics are listed for ChIP, genomic DNA, and bisulfite-converted sequencing libraries.

Article Snippet: GBM1w - CTCF , CTCF ChIP-seq , 38 base pairs , Paired end , Illumina NextSeq 500 , 19.3.

Techniques: Sequencing, CRISPR