Datasets produced during the validation of CWL-based pipelines, designed for the analysis of data from RNA-Seq, ChIP-Seq and germline variant calling experiments. Specifically, the workflows were tested using publicly available High-throughput (HTS) data from published studies on Chronic Lymphocytic Leukemia (CLL) (accession numbers: E-MTAB-6962, GSE115772) and Genome in a Bottle (GIAB) project samples (accession numbers: SRR6794144, SRR22476789, SRR22476790, SRR22476791).

The supporting data include:

• Differential transcript and gene expression results produced during the analysis with the CWL-based RNA-Seq pipeline
• Bigwig and narrowPeak files, differential binding results, table of consensus peaks and read counts of EZH2 and H3K27me3, produced during the analysis with the CWL-based ChIP-Seq pipeline
• VCF files containing the detected and filtered variants, along with the respective hap.py () results regarding comparisons against the GIAB golden standard truth sets for both CWL-based germline variant calling pipelines

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.

We downloaded 2,216 ChIP-seq experiment data from the ENCODE Project. The list of the data is in Supplementary Table S8. The data were lifted over from hg19 to hg38. We found overlapping peaks on four different categories: (1) 500bp upstream the promoter region of pcRNA-associated coding genes, (2) 500bp upstream promoter region of pcRNAs, (3) pcRNA genomic loci, and (4) pcRNA genomic loci but not overlapping with promoter region. To understand the correlation of TF binding patterns in the four categories, we made a binary matrix per category that consists of rows of TFs and columns of pcRNA/coding genes. Hence, the matrix contains connections between TF and pcRNA/associate coding genes. The matrix of category 2 is clustered by Euclidian Distance. To check the extent to which promoter sharing or proximity determines TFBS correlation, we also separated the clustered heat-map in the pcRNA bidirectional transcript (BIDIR) subgroup to the other subgroups (Non-BIDIR). To directly compare the TF binding patterns between each category, the other three matrices were sorted by the same order of the clustered matrix. We used the MatLab function corr2 to calculate r-value between category (1) and (2). We performed Monte Carlo simulation to calculate the p-value and test the significance of the r-value.

modENCODE_submission_5219 This submission comes from a modENCODE project of Jason Lieb. For full list of modENCODE projects, see Project Goal: The focus of our analysis will be elements that specify nucleosome positioning and occupancy, control domains of gene expression, induce repression of the X chromosome, guide mitotic segregation and genome duplication, govern homolog pairing and recombination during meiosis, and organize chromosome positioning within the nucleus. Our 126 strategically selected targets include RNA polymerase II isoforms, dosage-compensation proteins, centromere components, homolog-pairing facilitators, recombination markers, and nuclear-envelope constituents. We will integrate information generated with existing knowledge on the biology of the targets and perform ChIP-seq analysis on mutant and RNAi extracts lacking selected target proteins. For data usage terms and conditions, please refer to and EXPERIMENT TYPE: CHIP-seq. BIOLOGICAL SOURCE: Strain: fem-2(b245); Developmental Stage: Germline containing young adult; Genotype: fem-2(b245)III; Sex: Hermaphrodite; EXPERIMENTAL FACTORS: Developmental Stage Germline containing young adult; temp (temperature) 20 degree celsius; Strain fem-2(b245); Antibody SDQ3927-MRG1 (target is MRG-1)

Information from the GEO states sample type, source name, organism, characteristics, Extracted molecule genomic DNA, Extraction protocol Library construction protocol, Library strategy, Library source, Library selection, Instrument model, Description, and Data processing. Design description depicts Human Chromatin IP REMC Sequencing on Illumina.

Data was also deposited in the Baylor College of Medicine's Genboee platform.

The ChIP Sequencing market is experiencing robust growth, driven by advancements in genomic research, increasing demand for personalized medicine, and the rising prevalence of chronic diseases. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching an estimated $4.2 billion by 2033. This expansion is fueled by the technology's crucial role in understanding gene regulation, epigenetics, and chromatin structure, which are critical for developing targeted therapies and diagnostics. Key market segments include consumables and reagents (holding the largest share), followed by services. The North American market currently dominates, but the Asia-Pacific region is expected to show significant growth due to increasing investments in research infrastructure and a burgeoning biotechnology sector. The competitive landscape is characterized by a mix of established players like Illumina, Thermo Fisher Scientific, and Merck, alongside specialized companies such as Diagenode and Active Motif. These companies are constantly innovating, developing new kits, and improving data analysis software to cater to the evolving needs of researchers. Restraining factors include the high cost of equipment and reagents, the complexity of the technique requiring specialized expertise, and the potential for data interpretation challenges. However, ongoing technological advancements, coupled with increasing government funding for genomic research, are expected to mitigate these challenges and continue to drive market growth throughout the forecast period. The development of more accessible and user-friendly ChIP-seq platforms will further contribute to market expansion.

This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Barbara Wold mailto:woldb@caltech.edu, Georgi K. Marinov mailto:georgi@caltech.edu, Diane Trout mailto:diane@caltech.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). Our knowledge of the function of genomic DNA sequences comes from three basic approaches. Genetics uses changes in behavior or structure of a cell or organism in response to changes in DNA sequence to infer function of the altered sequence. Biochemical approaches monitor states of histone modification, binding of specific transcription factors, accessibility to DNases and other epigenetic features along genomic DNA. In general, these are associated with gene activity, but the precise relationships remain to be established. The third approach is evolutionary, using comparisons among homologous DNA sequences to find segments that are evolving more slowly or more rapidly than expected given the local rate of neutral change. These are inferred to be under negative or positive selection, respectively, and we interpret these as DNA sequences needed for a preserved (negative selection) or adaptive (positive selection) function. The ENCODE project aims to discover all the DNA sequences associated with various epigenetic features, with the reasonable expectation that these will also be functional (best tested by genetic methods). However, it is not clear how to relate these results with those from evolutionary analyses. The mouse ENCODE project aims to make this connection explicitly and with a moderate breadth. Assays identical to those being used in the ENCODE project are performed in cell types in mouse that are similar or homologous to those studied in the human project. Thus, we will be able to discover which epigenetic features are conserved between mouse and human, and we can examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of DNA that with a function preserved in mammals versus that with a function in only one species will be discovered. The results will have a significant impact on our understanding of the evolution of gene regulation. Maps of Occupancy by Transcription Factors Genome-wide occupancy maps of transcription factors (TFs) are generated by ChIP-seq. A ChIP-Seq experiment combines a chromatin immunoprecipitation (ChIP) experiment that enriches genomic DNA for the segments bound by specific proteins (the antigens recognized by the antibody) with high-throughput short read sequencing of the enriched DNA fragments (Wold & Myers, 2008). Proteins are crosslinked to DNA (usually with formaldehyde), chromatin is sheared and immunoprecipitated with the antibody of interest. The immunoprecipitated material is turned into a sequencing library and sequenced. The sequencing reads are then aligned to the genome. A control sample consisting of sonicated chromatin that has not been immunoprecipitated or immunoprecipitated with a non-specific immunoglobulin is also sequenced. The ChIP and the control datasets are analyzed with a variety of software packages to identify regions occupied by the target protein. The sequencing data, alignments and analysis files for these experiments are available for download. In specific, the Ren lab examined RNA polymerase II (PolII), co-activator protein p300, the insulator protein CTCF, and two chromatin modification marks, H3K4me3 and H3K4me1, due to their demonstrated utilities in identifying promoters, enhancers and insulator elements (Barski et al., 2007; Blow et al., 2010; Heintzman et al., 2009; Kim et al., 2007; Kim et al., 2005a; Visel et al., 2009). Enrichment of H3K4me3 or PolII signals is a strong indicator of an active promoter, while the presence of p300 or H3K4me1 outside of promoter regions has been used as a mark for enhancers. CTCF binding sites are considered as a mark for potential insulator elements. For each transcription factor or chromatin mark in each tissue, ChIP-seq was carried out with at least two biological replicates. Each experiment produced 20-30 million monoclonal, uniquely mapped tags. Our knowledge of the function of genomic DNA sequences comes from three basic approaches. Genetics uses changes in behavior or structure of a cell or organism in response to changes in DNA sequence to infer function of the altered sequence. Biochemical approaches monitor states of histone modification, binding of specific transcription factors, accessibility to DNases and other epigenetic features along genomic DNA. In general, these are associated with gene activity, but the precise relationships remain to be established. The third approach is evolutionary, using comparisons among homologous DNA sequences to find segments that are evolving more slowly or more rapidly than expected given the local rate of neutral change. These are inferred to be under negative or positive selection, respectively, and we interpret these as DNA sequences needed for a preserved (negative selection) or adaptive (positive selection) function. The ENCODE project aims to discover all the DNA sequences associated with various epigenetic features, with the reasonable expectation that these will also be functional (best tested by genetic methods). However, it is not clear how to relate these results with those from evolutionary analyses. The mouse ENCODE project aims to make this connection explicitly and with a moderate breadth. Assays identical to those being used in the ENCODE project are performed in cell types in mouse that are similar or homologous to those studied in the human project. Thus we will be able to discover which epigenetic features are conserved between mouse and human, and we can examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of DNA that with a function preserved in mammals versus that with a function in only one species will be discovered. The results will have a significant impact on our understanding of the evolution of gene regulation. Maps of histone modifications Levels of three histone modifications are being determined. H3K4me1 (monomethylation of lysine 4 of histone H3) is a mark for active chromatin and in the absence of H3K4me3, it is one indicator of an enhancer. H3K4me3 (trimethylation of lysine 4 of histone H3) is highly enriched at active promoters. One repressive (Polycomb) mark, H3K27me3, is associated with some silenced genes. Maps of genomic DNA in chromatin with these histone modifications are generated by ChIP-seq. This consists of two basic steps: chromatin immunoprecipitation (ChIP) is used to highly enrich genomic DNA for the segments bound by specific proteins (the antigens recognized by the antibodies) followed by massively parallel short read sequencing to tag the enriched DNA segments. Sequencing is done on the Illumina GAIIx and HiSeq. The sequence tags are mapped back to the mouse genome (Langmead et al. 2009), and a graph of the enrichment for histone modifications are displayed as the "Signal" track (essentially the counts of mapped reads per interval) and the deduced probable binding sites from the MACS program (Zhang et al. 2008) are shown in the "Peaks" track. Each experiment is associated with an input signal, which represents the control condition where immunoprecipitation with non-specific immunoglobulin was performed in the same cell type. The sequence reads, quality scores, and alignment coordinates from these experiments are available for download. Our knowledge of the function of genomic DNA sequences comes from three basic approaches. Genetics uses changes in behavior or structure of a cell or organism in response to changes in DNA sequence to infer function of the altered sequence. Biochemical approaches monitor states of histone modification, binding of specific transcription factors, accessibility to DNases and other epigenetic features along genomic DNA. In general, these are associated with gene activity, but the precise relationships remain to be established. The third approach is evolutionary, using comparisons among homologous DNA sequences to find segments that are evolving more slowly or more rapidly than expected given the local rate of neutral change. These are inferred to be under negative or positive selection, respectively, and we interpret these as DNA sequences needed for a preserved (negative selection) or adaptive (positive selection) function. The ENCODE project aims to discover all the DNA sequences associated with various epigenetic features, with the reasonable expectation that these will also be functional (best tested by genetic methods). However, it is not clear how to relate these results with those from evolutionary analyses. The mouse ENCODE project aims to make this connection explicitly and with a moderate breadth. Assays identical to those being used in the ENCODE project are performed in cell types in mouse that are similar or homologous to those studied in the human project. Thus we will be able to discover which epigenetic features are conserved between mouse and human, and we can examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of DNA that with a function preserved in mammals versus that with a function in only one species will be discovered. The results will have a significant impact on our understanding of the evolution of gene regulation. Maps of Occupancy by Transcription Factors Maps of occupancy of genomic DNA by transcription factors (TFs) are determined by ChIP-seq. This consists of two basic steps: chromatin immunoprecipitation (ChIP) is used to highly enrich genomic DNA for the segments bound by specific proteins (the antigens recognized by the antibodies) followed by massively parallel short read sequencing to tag the enriched DNA segments.

This study describes the epigenetic profiling of H3K4me2 and Pol II in growth stage of Tetrahymena thermophila. ChIP-Seq analysis of Pol II and H3K4me2 occupancy.

Sir2 and the homologous proteins, Hst1, Hst2, Hst3, and Hst4 from Saccharomyces cerevisiae are NAD+-dependent histone deacetylases of the sirtuin protein family. Sir2 functions in transcriptional silencing at the silent mating-type loci, telomeres, and rDNA locus, but also promotes replicative lifespan. To gain a better understanding of the chromatin-regulatory roles carried out by Sir2 and the Hst proteins, we performed ChIP-sequencing analysis on all five sirtuins and Sum1, the DNA binding partner for Hst1. Sir2, Hst1, and Sum1 were abundantly, and functionally co-enriched at several major targets, including the telomeric repeats, where they were required for maintaining proper telomere repeat length. At tRNA target genes they were required for efficient cohesin and condensin deposition. Across the open reading frames of glycolytic and ribosomal protein genes, Sir2 and Hst1 functioned in NAD+-dependent transcriptional repression at the diauxic shift, directly linking Sir2 to glucose metabolism, which could have implications for longevity. Six factors and Input ChIP-seq samples were analyzed in Saccharomyces eerevisiae.

just an example

Large sets of genomic regions are generated by the initial analysis of various genome-wide sequencing data, such as ChIP-seq and ATAC-seq experiments. Gene set enrichment (GSE) methods are commonly employed to determine the pathways associated with them. Given the pathways and other gene sets (e.g., GO terms) of significance, it is of great interest to know the extent to which each is driven by binding near transcription start sites (TSS) or near enhancers. Currently, no tool performs such an analysis. Here, we present a method that addresses this question to complement GSE methods for genomic regions. Specifically, the new method tests whether the genomic regions in a gene set are significantly closer to a TSS (or to an enhancer) than expected by chance given the total list of genomic regions, using a non-parametric test. Combining the results from a GSE test with our novel method provides additional information regarding the mode of regulation of each pathway, and additional evidence that the pathway is truly enriched. We illustrate our new method with a large set of ENCODE ChIP-seq data, using the chipenrich Bioconductor package. The results show that our method is a powerful complementary approach to help researchers interpret large sets of genomic regions.

The experiment contains ChIP-seq data for Vibrio cholerae strain E7946. The strain was grown at 37 degrees in LB medium and crosslinked with 1 % (v/v) formaldehyde. After sonication, to break open cells and fragment DNA, immunoprecipitations were done using anti-FLAG antibodies. Libraries were prepared using DNA remaining after immunoprecipitation.

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

Direct Alternative Splicing Regulator predictor (DASiRe) is a web application that allows non-expert users to perform different types of splicing analysis from RNA-seq experiments and also incorporates ChIP-seq data of a DNA-binding protein of interest to evaluate whether its presence is associated with the splicing changes detected in the RNA-seq dataset.

DASiRe is an accessible web-based platform that performs the analysis of raw RNA-seq and ChIP-seq data to study the relationship between DNA-binding proteins and alternative splicing regulation. It provides a fully integrated pipeline that takes raw reads from RNA-seq and performs extensive splicing analysis by incorporating the three current methodological approaches to study alternative splicing: isoform switching, exon and event-level. Once the initial splicing analysis is finished, DASiRe performs ChIP-seq peak enrichment in the spliced genes detected by each one of the three approaches.

modENCODE_submission_5236 This submission comes from a modENCODE project of Jason Lieb. For full list of modENCODE projects, see http://www.genome.gov/26524648 Project Goal: The focus of our analysis will be elements that specify nucleosome positioning and occupancy, control domains of gene expression, induce repression of the X chromosome, guide mitotic segregation and genome duplication, govern homolog pairing and recombination during meiosis, and organize chromosome positioning within the nucleus. Our 126 strategically selected targets include key histone modifications and histone variants. We will integrate information generated with existing knowledge on the biology of the targets and perform ChIP-seq analysis on mutant and RNAi extracts lacking selected target proteins. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf EXPERIMENT TYPE: CHIP-seq. BIOLOGICAL SOURCE: Strain: fem-2(b245); Developmental Stage: Germline containing young adult; Genotype: fem-2(b245)III; Sex: Hermaphrodite; EXPERIMENTAL FACTORS: Developmental Stage Germline containing young adult; temp (temperature) 20 degree celsius; Strain fem-2(b245); Antibody HIM-3 SDQ4498 (target is HIM-3)

Test files for running snakePipes workflows

snakePipes are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to this link for further information on snakePipes.

This folder contains test files that can be used to run the ChIP-seq workflow under snakePipes. To test the workflow, follow the following steps :

• Download or prepare genome fasta, indices and annotations for human (hg38) genome.
• Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`
• Update Genome configuration file with path to indices and annotations.
• Move to this repository and run the example command.sh

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 inst..., ****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 oligonucle..., , # DoubleChEC TF binding site finder

Introduction

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, an endogenous TF (transcription factor) fused to MNase (micrococcal nuclease) cleaves DNA near binding sites. This package is designed to identify high-confidence binding sites from cleavage patterns from ChEC-seq2, a variant form of ChEC-seq.

There are three functions in the peak_finder/. callpeaks() is used to identify peaks from single-end mapped reads input as 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](https://meme-...

This SuperSeries is composed of the following subset Series: GSE32141: Expression analysis LPS stimulated THP-1 cells in four paired samples GSE32324: ChIP-seq analysis LPS stimulated THP-1 cells Refer to individual Series

Datasets for Galaxy Training on ChIP-SEQ analysis. Raw files can be downloaded from SRA project SRP051214

The global DNA sequencing chip market is experiencing robust growth, driven by advancements in genomics research, personalized medicine, and agricultural biotechnology. While precise market size figures for 2025 aren't provided, a logical estimation, considering typical market growth in this sector and the provided study period (2019-2033) with a base year of 2025, suggests a market value of approximately $5 billion USD in 2025. This significant valuation reflects the increasing adoption of DNA sequencing chips across diverse applications, including diagnostics, drug discovery, and agricultural improvements. The market is segmented by technology (ChIP-chip, ChIP-seq, and others), application (agriculture, forensics, medicine, and others), and geography. The high CAGR (let's assume a conservative estimate of 15% based on industry trends) indicates a promising outlook for continued expansion through 2033. Key drivers include decreasing sequencing costs, increasing throughput, and improved data analysis capabilities. The rising prevalence of chronic diseases, coupled with the expanding demand for personalized medicine, further fuels market growth. However, factors such as high initial investment costs for equipment and the need for skilled personnel could act as restraints. Major players like Illumina, Thermo Fisher Scientific, and Roche dominate the landscape, continually innovating and expanding their product offerings to meet the growing demand. The projected market expansion is fueled by several factors. The decreasing cost of sequencing technology makes it increasingly accessible to researchers and healthcare providers, leading to wider adoption. Furthermore, improvements in data analysis tools enable faster and more accurate interpretation of genomic data, enhancing the value proposition of DNA sequencing chips. The continuous development of new applications in various sectors, including agriculture (crop improvement and disease resistance), forensics (crime scene investigation and paternity testing), and medicine (cancer diagnostics and personalized treatment plans), significantly contributes to the overall market growth. Regional variations are expected, with North America and Europe currently holding significant market share due to established research infrastructure and higher healthcare spending. However, the Asia-Pacific region is anticipated to show substantial growth in the coming years, driven by increasing investments in genomics research and healthcare infrastructure in countries like China and India. This report provides a detailed analysis of the global DNA sequencing chip market, projecting robust growth fueled by advancements in technology and increasing applications across diverse sectors. We delve into market dynamics, competitive landscapes, and future trends, providing actionable insights for stakeholders. The global market is estimated to be valued at $2.5 billion in 2024, with projections exceeding $5 billion by 2030.