Sample Submission Guidelines
What is ChIP-Seq?
ChIP-Seq, or Chromatin Immunoprecipitation Sequencing, is a powerful molecular biology technique that combines chromatin immunoprecipitation (ChIP) with high-throughput sequencing. It enables genome-wide identification of protein-DNA binding sites. By using specific antibodies to enrich DNA fragments bound to target proteins, followed by next-generation sequencing, researchers can map where proteins interact with the genome.
This method is widely used to study transcription factors, histone modifications, and other chromatin-associated proteins. It helps scientists uncover key biological processes such as gene regulation, epigenetic mechanisms, cell differentiation, and disease development. Compared to traditional ChIP-qPCR, ChIP-Seq offers higher throughput, greater sensitivity, and finer resolution, allowing discovery of both known and novel regulatory elements across the genome.
Overview of ChIP-seq experiments. (Hojo, Hironori, and Shinsuke Ohba., 2023)
Advantages of ChIP-Seq and How It Differs from ATAC-Seq
- High specificity for precise protein-DNA binding site identification
ChIP-Seq uses antibodies to selectively capture DNA fragments bound by target proteins. This enables accurate mapping of transcription factors, histone modifications, and other regulatory proteins, revealing key regulatory elements and gene expression mechanisms. - Genome-wide coverage to decode broad regulatory networks
By integrating with high-throughput sequencing, ChIP-Seq systematically analyzes protein-DNA interactions across the entire genome. This helps researchers gain a comprehensive understanding of chromatin regulation and gene expression control. - Versatile protein and sample compatibility
ChIP-Seq works with a wide range of protein targets, including transcription factors and histone marks, and supports various sample types such as cells, tissues, and different species. This flexibility suits diverse research needs. - Quantitative analysis of dynamic protein-DNA interactions
Beyond identifying binding sites, ChIP-Seq enables comparison of binding strength across different conditions or treatments, uncovering dynamic regulatory changes and complex biological processes.
| Feature | ChIP-Seq | ATAC-Seq |
|---|---|---|
| Research Focus | Maps specific protein-DNA binding sites (e.g., transcription factors, histone marks) | Profiles open chromatin regions, reflecting chromatin accessibility |
| Antibody Dependence | Yes, requires high-quality, specific antibodies | No, antibody-free |
| Specificity | High, precisely locates protein-DNA interactions | Lower, provides broad overview of accessible chromatin without protein specificity |
| Applicability | Ideal for studying specific regulatory factors and their networks | Better suited for global chromatin accessibility profiling and initial regulatory region screening |
| Data Interpretation | Clear binding sites simplify linking to target genes and regulatory functions | Requires additional integration with transcription factor data for functional inference |
Summary:
- ChIP-Seq is preferred when the goal is to study binding patterns of a particular transcription factor or histone modification due to its specificity and direct binding site identification.
- ATAC-Seq is a faster, simpler approach to survey genome-wide chromatin accessibility and identify potential regulatory regions.
ChIP-Seq Service Types and Specifications
| Service Type | Recommended Data Volume | Sequencing Platform | Notes |
|---|---|---|---|
| Histone Modification ChIP-Seq | 8 GB per sample | Illumina NovaSeq/HiSeq | For group comparisons, at least 2 biological replicates per group are recommended, including both ChIP and Input samples. |
| Transcription Factor ChIP-Seq | 6 GB per sample | Illumina NovaSeq/HiSeq | Same requirements for biological replicates and controls as above. |
ChIP-Seq Service Workflow
Requirement discussion
Plan confirmation
Sample registration
Quality check
Optional DNA extraction
Chromatin fragmentation
Immunoprecipitation (ChIP)
DNA purification
Library construction & QC
Method selection by protein type
Platforms: NovaSeq/HiSeq PE150, DNBSEQ
Insert size: 150–300 bp
Data:
Transcription factors: ≥ 20M reads/sample
Histone modifications: ≥ 50M reads/sample
Raw data (FASTQ)
QC & alignment results
Peak calling & annotations
Final comprehensive report
Explore detailed bioinformatics solutions ↓ChIP-Seq Bioinformatics Analysis
| Analysis Type | Category | Notes |
|---|---|---|
| Raw data processing and quality check | A | Included |
| Reference genome annotation and statistics | A | Included |
| Alignment to reference genome | A | Included |
| Peak calling | A | Included |
| GO function annotation for peak-related genes | A | Included |
| KEGG pathway annotation for peak-related genes | A | Included |
| Differential peak analysis | A | Requires >1 sample |
| GO enrichment analysis of differential peaks | A | Requires >1 sample |
| KEGG enrichment analysis of differential peaks | A | Requires >1 sample |
| Motif analysis (binding sequence preference) | A | Included |
Applications of ChIP-Seq
ChIP-Seq stands as a fundamental technique in epigenetics and gene regulation research. It is widely used across various fields to uncover gene expression control mechanisms and chromatin function:
- Mapping Transcription Factor Binding Sites
ChIP-Seq captures DNA regions bound by specific transcription factors. This helps identify their target genes and reveals gene regulatory networks. It’s commonly applied in studies on cell fate determination, developmental regulation, and disease models. - Profiling Histone Modification Landscapes
Using antibodies against specific histone marks (e.g., H3K27ac, H3K4me3), ChIP-Seq charts the genome-wide distribution of chromatin modifications. This assists in pinpointing regulatory elements like promoters and enhancers. - Investigating Epigenetic Regulation Mechanisms
ChIP-Seq tracks dynamic changes in epigenetic marks across cell states, developmental stages, or disease conditions. It reveals patterns of gene silencing and activation, providing insights into cancer, neurodegeneration, immune disorders, and more. - Drug Target Discovery and Functional Validation
The technique evaluates how small molecules or targeted drugs affect the binding of transcription factors or epigenetic regulators to DNA. This supports target screening and validation in drug development. - Functional Genomics in Plants and Animals
ChIP-Seq enables functional analysis of regulatory proteins in non-model organisms. Combined with transcriptomic data, it aids studies on complex traits, stress resistance, and other biological processes.
ChIP-Seq Sample Requirements
| Sample Type | Recommended Starting Amount | Minimum Starting Amount | Additional Requirements |
|---|---|---|---|
| ChIP DNA | ≥ 10 ng | 5 ng | Concentration ≥ 1 ng/µl; OD 260/280 ratio between 1.8 and 2.0; RNase treated; no degradation or contamination |
| Cell Samples | ≥ 2 × 10⁷ cells | 1 × 10⁵ cells | Crosslinked with 1% formaldehyde; washed 3 times with PBS; pellets collected by centrifugation; snap-frozen in liquid nitrogen; stored at -80°C |
| Tissue Samples | ≥ 500 mg | - | Immediately snap-frozen in liquid nitrogen after collection; avoid repeated freeze-thaw cycles; transport on dry ice |
Why CD Genomics Is Your Trusted ChIP-Seq Partner
- Decades of Expertise Across Species
Our team has years of hands-on experience with ChIP-Seq across a wide range of sample types—cells, tissues, plants, and animals. You get scientifically sound protocols tailored to your study needs. - High-Quality, Publication-Ready Data
We apply rigorous quality control checkpoints throughout the workflow—from sample prep to final analysis—to ensure your data meets peer-reviewed publishing standards. - Flexible Sample Handling
Whether you're working with limited material or complex experimental groups, our protocols adapt to your sample type and research design. - End-to-End Bioinformatics Support
Receive a well-structured report including both raw data and in-depth analysis. From peak detection to pathway annotation, we help you make sense of the data quickly and confidently.
Reference
- Nakato, Ryuichiro, and Toyonori Sakata. "Methods for ChIP-seq analysis: a practical workflow and advanced applications." Methods 187 (2021): 44-53. https://doi.org/10.1016/j.ymeth.2020.03.005
- Jiang, Shan, and Ali Mortazavi. "Integrating ChIP-seq with other functional genomics data." Briefings in functional genomics 17.2 (2018): 104-115. https://doi.org/10.1093/bfgp/ely002
- Steinhauser, Sebastian, et al. "A comprehensive comparison of tools for differential ChIP-seq analysis." Briefings in bioinformatics (2016): bbv110. https://doi.org/10.1093/bib/bbv110
- Ma, Shaoqian, and Yongyou Zhang. "Profiling chromatin regulatory landscape: insights into the development of ChIP-seq and ATAC-seq." Molecular biomedicine 1.1 (2020): 9. https://doi.org/10.1186/s43556-020-00009-w
- Muhammad, Isiaka Ibrahim, et al. "RNA-seq and ChIP-seq as complementary approaches for comprehension of plant transcriptional regulatory mechanism." International journal of molecular sciences 21.1 (2019): 167. https://doi.org/10.3390/ijms21010167
- Hojo, Hironori, and Shinsuke Ohba. "Runt-related transcription factors and gene regulatory mechanisms in skeletal development and diseases." Current Osteoporosis Reports 21.5 (2023): 485-492. https://doi.org/10.1007/s11914-023-00808-4
Partial results are shown below:
Peak Distribution
KEGG Pathway Enrichment
Motif analysis
1. What is an Input sample, and why is it important?
An Input sample is total DNA from sonicated chromatin that hasn’t undergone immunoprecipitation. It serves as a control to check fragmentation quality and helps filter out background noise, ensuring accurate peak calling.
2. How does an Input sample relate to the IP (immunoprecipitated) sample?
Input and IP samples are processed in parallel but sequenced separately. Their data is later integrated to accurately identify true protein-DNA binding sites.
3. How much sequencing data is recommended per sample?
We recommend at least 20 million clean reads per sample to achieve sufficient depth for reliable binding site detection.
4. Is PCR amplification needed for library prep, and does it affect the data?
Yes, PCR is typically required to amplify DNA for sequencing. However, if the input DNA is sufficient, fewer cycles can be used to minimise bias. Overall, PCR has minimal impact on results.
5. Does DNA fragment size affect sequencing quality?
Absolutely. Ideal fragment size is 200–300 bp, with total range between 100–500 bp. Consistent fragment size improves sequencing efficiency and data quality.
6. Is a negative control necessary for ChIP-Seq?
Yes, the Input sample usually serves as a negative control. Additional controls can be included based on budget and study goals.
7. What factors affect ChIP-Seq data quality?
Key factors include antibody specificity, chromatin shearing consistency, sample prep, sequencing depth, and data quality control.
8. What’s the difference between sonication and enzyme digestion for chromatin fragmentation?
Sonication uses sound energy and is ideal for histone-related studies. Enzyme digestion is gentler and offers better reproducibility, especially for low-abundance transcription factors.
9. What causes false positives in ChIP-Seq?
Sources include poor chromatin quality, PCR bias, repetitive regions, or sequencing errors. Using Input controls and motif analysis can help reduce these artifacts.
10. Which species are suitable for ChIP-Seq?
ChIP-Seq is best suited for diploid organisms with chromosome-level genome assemblies and well-annotated references (including GTF files). For other species, contact us to assess feasibility.
Customer Publication Highlight
Identification of an RNA Polymerase II-Associated Protein Subcomplex and Epigenetic Regulation of Cellular Characteristics
Journal: Nature Communications
Impact Factor: ~12.1
Published: 14 September 2023
DOI: 10.1038/s41467-023-41297-4
Background
Undifferentiated cell populations exhibit unique molecular mechanisms that maintain their regulatory functions. Epigenetic modifications, particularly histone methylation, play a critical role in modulating these processes. This study identifies a novel RNA polymerase II-associated protein subcomplex involving KMT2A, PHF5A, PHF14, HMG20A, and RAI1, which epigenetically regulates key cellular properties.
Project Objective
The study aimed to:
- Characterize protein-protein interactions (PPIs) of PHF5A in stem-like cells using proteomics and genomics.
- Identify epigenetic regulators influencing cellular maintenance via small-molecule screening.
- Validate functional mechanisms through ChIP-seq and RNA-seq analysis.
CD Genomics’ Services
This study utilized methodologies aligned with CD Genomics’ expertise:
- ChIP-Seq Profiling
- Genome-wide mapping of PHF5A, PHF14, and KMT2A binding sites.
- Identified 171 co-occupied gene targets (e.g., PAK3, FLT4, LINGO2).
- Peak calling (MACS3) and motif analysis (HOMER).
- RNA-Seq Analysis
- Transcriptomic profiling of KMT2A-inhibited cells (MM-102 treatment).
- Differential expression analysis (DESeq2) revealing 768 upregulated and 1317 downregulated genes.
- Bioinformatics Integration
- Pathway enrichment (Wnt signaling, chromatin remodeling).
- Genomic distribution analysis (48.7% gene body, 38.1% intergenic regions).
Key Findings
- PHF5A-PHF14-HMG20A-RAI1 Subcomplex
- LC-MS/MS and Co-IP confirmed physical interactions among these proteins.
- ChIP-seq revealed co-occupancy at regulatory regions of genes associated with cellular maintenance (SOX2, NANOG).
- KMT2A as an Epigenetic Regulator
- Inhibition (via OICR-9429/MM-102) reduced H3K4me3 levels and impaired self-renewal.
- RNA-seq showed transcriptional downregulation of pluripotency pathways.
- Functional Validation
- In vitro: KMT2A inhibition decreased cellular proliferation (P < 0.001).
- In vivo: Reduced growth in xenograft models (50 mg/kg MM-102, P < 0.01).
Figures Referenced
Fig. 3: PHF14 occupies common DNA binding sites with PHF5A in PCSCs.
Fig. 5: KMT2A epigenetically regulates PC cells and physically associates with RNA Pol II-associated PHF5A-PHF14-HMG20A-RAI1 protein subcomplex in PCSCs.
For similar epigenetic or transcriptional studies, explore CD Genomics’ ChIP-seq and RNA-seq services.
Reference
- Mouti, M.A., Deng, S., Pook, M. et al. KMT2A associates with PHF5A-PHF14-HMG20A-RAI1 subcomplex in pancreatic cancer stem cells and epigenetically regulates their characteristics. Nat Commun 14, 5685 (2023). https://doi.org/10.1038/s41467-023-41297-4
Here are some publications that have been successfully published using our services or other related services:
Downregulated PITX1 Modulated by MiR-19a-3p Promotes Cell Malignancy and Predicts a Poor Prognosis of Gastric Cancer by Affecting Transcriptionally Activated PDCD5
Journal: Cellular Physiology and Biochemistry
Year: 2018
IL-4 drives exhaustion of CD8+ CART cells
Journal: Nature Communications
Year: 2024
High-Fat Diets Fed during Pregnancy Cause Changes to Pancreatic Tissue DNA Methylation and Protein Expression in the Offspring: A Multi-Omics Approach
Journal: International Journal of Molecular Sciences
Year: 2024
KMT2A associates with PHF5A-PHF14-HMG20A-RAI1 subcomplex in pancreatic cancer stem cells and epigenetically regulates their characteristics
Journal: Nature communications
Year: 2023
Cancer-associated DNA hypermethylation of Polycomb targets requires DNMT3A dual recognition of histone H2AK119 ubiquitination and the nucleosome acidic patch
Journal: Science Advances
Year: 2024
Genomic imprinting-like monoallelic paternal expression determines sex of channel catfish
Journal: Science Advances
Year: 2022
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