4acC DNA Modification Profiling (4acC DIP-seq) Service
Need to determine where the emerging DNA modification N4-acetyldeoxycytosine is enriched across a genome? CD Genomics' 4acC DNA modification profiling service combines 4acC antibody enrichment, input-aware sequencing, regional peak analysis, and epigenomic integration to compare 4acC landscapes across species, tissues, genotypes, developmental states, or experimental treatments.
Key Project Advantages:
- DNA-Specific Study Design: Genomic DNA preparation and DNA-focused enrichment distinguish DNA 4acC from the similarly named RNA ac4C signal.
- Input-Aware Regional Profiling: A matched input library supplies the genomic background needed to interpret peaks and locus tracks.
- Comparative Analysis: Replicate-aware peak and differential-enrichment analysis identifies shared and condition-sensitive 4acC regions.
- Epigenomic Context: Integration with expression, chromatin accessibility, histone modifications, 5mC, and genetic features supports testable regulatory hypotheses.
What Does 4acC-IP-seq Measure?
4acC-IP-seq is an antibody-enrichment sequencing method that profiles genomic regions containing DNA N4-acetyldeoxycytosine (4acC). Fragmented, denatured genomic DNA is enriched with an anti-4acC antibody and compared with an input library, producing region-level maps for peak annotation, differential enrichment, and integration with transcriptional and epigenomic data.
Quick answer: 4acC-IP-seq answers, "Which genomic regions are enriched for DNA 4acC?" It does not identify the exact modified cytosine within every fragment or measure the absolute 4acC fraction at each base.
DNA 4acC Is Not RNA ac4C: DNA 4acC refers to N4-acetyldeoxycytosine in genomic DNA, whereas RNA ac4C refers to N4-acetylcytidine in RNA. The DNA assay therefore requires genomic DNA preparation, removal of residual RNA, and DNA-focused enrichment and analysis. Researchers interested in transcript-level RNA acetylation should instead consider RNA acRIP-seq.
A 4acC-IP-seq peak is an enriched DNA region supported by IP-versus-input sequencing. It is not automatically a precise base call, an absolute modification percentage, or evidence that one specific enzyme created the signal.
DNA 4acC and RNA ac4C require different analytes, workflows, and interpretation.
Why Use an Enrichment Map? A 4acC-recognizing antibody enriches modified DNA fragments from the full genomic background. Sequencing then concentrates analytical attention on regions with stronger 4acC-associated recovery, helping researchers screen promoters, gene bodies, intergenic regions, and other features for candidates that differ across biological conditions. When the project asks only whether 4acC is present or how much is present globally, Epigenetic LC-MS/MS Services may provide a more direct complementary readout.
4acC-IP-seq Workflow and Control Strategy
The workflow preserves the relationship between the enriched 4acC fraction and its genomic background. Study contrasts, input controls, biological replication, DNA purity, reference-genome quality, and desired integrations are reviewed before processing so the resulting peaks answer the intended biological question.
Input-aware 4acC-IP-seq converts enriched genomic DNA into annotated regional profiles and comparative results.
- Study Design and Sample Review: Confirm species, reference genome, sample type, comparison groups, biological replicates, and complementary datasets, aligning wet-lab design with downstream contrasts.
- Genomic DNA Extraction and QC: Extract genomic DNA from cells or tissues, or review submitted DNA for quantity, integrity, purity, and residual-RNA risk.
- Fragmentation, End Preparation, and Denaturation: Fragment and prepare genomic DNA for library construction, then denature it before immunoprecipitation.
- 4acC Immunoprecipitation with Matched Input: Retain one fraction as unenriched input and capture 4acC-containing DNA fragments with a 4acC-recognizing antibody and magnetic beads.
- Library Amplification, QC, and Sequencing: Convert recovered IP and input DNA into sequencing-ready libraries, review library quality, and perform paired-end sequencing.
- Bioinformatics and Scientific Review: Filter, align, normalize, and evaluate reads across replicates before calling, annotating, comparing, and integrating enriched regions.
Recommended Controls and Validation Logic:
- Input Control: Retains unenriched genomic representation for background-aware signal tracks and peak detection.
- Biological Replicates: Support reproducibility assessment and reduce the risk of interpreting a library-specific event as a biological pattern.
- DNA/RNA Separation: RNase treatment and DNA-focused QC help prevent RNA ac4C from contributing to the DNA assay.
- Optional Orthogonal Validation: Hydroxylamine treatment, targeted enrichment assays, dot blot, or LC-MS/MS may be considered when independent support for specificity or abundance is required.
Sample Requirements and Project Planning
The following values provide an initial planning baseline. Final acceptance depends on DNA quality, species, tissue composition, reference genome, group structure, and the amount reserved for input and validation.
| Sample Type | Recommended Starting Amount | Planning Value for the Study |
|---|---|---|
| Cultured Cells | More than 2 × 10^6 cells | Initial biomass target for DNA extraction, IP/input allocation, and biological replication. |
| Fresh or Frozen Animal Tissue | 200 mg | Initial collection target; tissue-specific extraction and purity risks require review. |
| Fresh or Frozen Plant Tissue | 500 mg | Accounts for the greater biomass often needed for plant genomic DNA preparation. |
| Purified Genomic DNA | 10 µg | Supports direct entry into DNA QC and enrichment when material meets project requirements. |
A typical planning configuration is paired-end 150-bp sequencing with approximately 6 Gb per library. Genome size, repeat content, reference quality, expected signal, replicate design, and desired comparative analyses may justify a different depth.
Before collection, provide the species, sample matrix, available amount, extraction status, biological groups, replicate count, treatment or genotype, reference genome, and expected comparison. Unusual tissues, non-model organisms, limited samples, or previously extracted DNA should undergo feasibility review before the complete experiment is collected.
Bioinformatics from Enrichment Reads to 4acC Regions
Bioinformatics converts IP and input reads into an auditable set of 4acC-enriched genomic regions. The analysis retains library, control, and replicate context so researchers can inspect the evidence behind each peak rather than receiving only a final gene list.
Core Processing and Enrichment Analysis:
- Raw-Read Quality Review: Summarize base quality, adapter content, read yield, and sequence composition before trimming.
- Reference Alignment and Filtering: Align clean reads to the approved reference and review mapping, duplicate, and usable-read statistics.
- Normalized Signal Tracks: Generate IP and input browser tracks for locus inspection and group comparison.
- Peak Calling and Reproducibility: Identify enriched regions and evaluate replicate agreement to support consensus peak sets.
- Genomic Annotation: Assign peaks to promoters, untranslated regions, exons, introns, gene bodies, transposable elements, and intergenic regions.
- Differential Enrichment: Prioritize regions with increased or decreased 4acC-associated signal across matched groups.
Functional and Integrative Analysis:
- Peak distribution across chromosomes and genomic features.
- Metagene profiles around transcription start sites, gene bodies, and transcription end sites.
- Associated-gene lists with GO and pathway enrichment.
- Sequence-context and motif analysis as a hypothesis-generating layer.
- Shared and condition-specific region analysis using overlap statistics, heatmaps, and clustering.
- Integration with ATAC-Seq to evaluate chromatin-accessibility context.
- Integration with ChIP-Seq to compare 4acC with histone-mark profiles.
- Joint analysis with RNA-seq, 5mC data, QTLs, transcription-factor predictions, or population variation.
Integrated overlap or correlation prioritizes hypotheses; it does not by itself prove that 4acC causes a transcriptional or chromatin change. Perturbation and targeted validation remain necessary for causal conclusions.
Results and Deliverables
The project package links raw data, quality evidence, regional signals, annotations, and biological interpretation. This makes it possible to trace a candidate gene or pathway back to its IP/input signal and analysis context.
Standard Deliverables:
- Study-design, sample, group, and control summary.
- Raw sequencing reads and clean-read files.
- Read-quality and alignment statistics for IP and input libraries.
- Alignment files and normalized genome-browser signal tracks.
- 4acC-enriched region tables with genomic coordinates and annotations.
- Replicate-correlation and region-reproducibility summaries.
- Genomic-feature, chromosome, metagene, and peak-length views.
- Differential-enrichment tables and visualizations for approved comparisons.
- Associated-gene, GO, pathway, motif, and multi-omics results when included.
- Final report with methods, results, interpretation notes, and project-specific limitations.
Representative Result Views:
- IP/Input Browser Tracks: Inspect localized enrichment and group-dependent signals at candidate loci.
- Metagene Profiles: Summarize average enrichment around TSSs, gene bodies, or other landmarks.
- Peak Annotation: Describe enriched-region distribution across genomic features.
- Differential Heatmaps: Prioritize reproducible condition-sensitive regions for validation.
- Motif and Functional Enrichment: Organize sequence and pathway hypotheses without presenting them as direct mechanistic proof.
Illustrative example. Synthetic result formats show how regional 4acC enrichment can be inspected, annotated, compared, and integrated.
Applications in Plant and Comparative Epigenetics
4acC profiling is most informative when the project starts with a defined biological contrast and asks how regional enrichment relates to transcription, chromatin state, development, environmental response, or genetic variation.
Compare roots, leaves, floral tissues, seeds, meristems, or developmental stages to identify tissue- or stage-associated 4acC regions. RNA-seq integration can test whether changing enrichment coincides with developmental gene programs.
Profile plants exposed to salt, drought, temperature, nutrient, light, or hormone treatments. Matched time points and controls can reveal stress-responsive 4acC regions for transcriptional and chromatin follow-up.
Overlay enriched regions with crop QTLs, trait-associated variants, promoters, and predicted regulatory elements in rice, maize, soybean, or other reference-supported species.
Combine 4acC-IP-seq with RNA-seq and ATAC-seq to examine whether promoter or gene-body enrichment is associated with expression and accessible chromatin.
Compare 4acC with WGBS or other 5mC data and active or repressive histone-mark profiles to identify co-localized, distinct, or condition-dependent patterns.
Compare orthologous genes, conserved features, or species-specific enrichment patterns across monocots and dicots while accounting for reference and annotation differences.
Evaluate feasibility in animal or human genomic DNA with orthogonal confirmation. Current genome-wide functional evidence is more developed in plants, so these projects should remain discovery-focused rather than diagnostic.
Case Study: Mapping 4acC-Associated Euchromatin in Arabidopsis
Choose 4acC-IP-seq or a Complementary DNA Modification Method
Method selection should follow the research question: regional discovery, global abundance, another DNA modification, or exact-site validation. These assays provide different answers and are often more useful in combination than as substitutes.
| Method | Primary Question | Main Readout | Best For | Not For |
|---|---|---|---|---|
| 4acC-IP-seq | Where are DNA 4acC-enriched regions, and how do they differ across groups? | IP/input tracks, enriched regions, annotations, and differential enrichment | Genome-wide discovery and epigenomic integration | Single-base calls, exact stoichiometry, or writer identification |
| Epigenetic LC-MS/MS Services | Is 4acC present, and what is its global abundance? | Global chemical detection or quantification | Orthogonal confirmation and abundance measurement | Genomic coordinates or regional maps |
| 5caC DIP-Seq | Where is 5-carboxylcytosine enriched? | 5caC-enriched genomic regions | 5caC-focused DNA modification studies | DNA 4acC profiling |
| DNA 6mA Sequencing | Where is N6-methyldeoxyadenosine enriched or detected? | 6mA-associated genomic signal | 6mA-focused epigenetic studies | DNA 4acC profiling |
| RNA acRIP-seq | Which RNA transcripts or regions are enriched for ac4C? | RNA ac4C enrichment | Epitranscriptomic RNA acetylation studies | Genomic DNA 4acC mapping |
Best for: Discovery-stage projects with sufficient genomic DNA, a usable reference genome, a defined biological comparison, input controls, and biological replicates.
Not for: Projects that require a confirmed modified nucleotide at single-base resolution, absolute per-site stoichiometry, or direct identification of a 4acC writer, eraser, or reader. Global chemical confirmation and targeted mechanistic assays should be added when those are the central questions.
Build a Method-Matched Study DesignFrequently Asked Questions (FAQ)
References
- Wang S, Xie H, Mao F, et al. "N4-acetyldeoxycytosine DNA modification marks euchromatin regions in Arabidopsis thaliana." Genome Biology. 2022;23:5.
- Chen Z, Xie H, Li Q, et al. "Genome-wide profiling and functional characterization of N4-acetyldeoxycytosine reveals conserved epigenetic roles in rice and Arabidopsis." BMC Genomics. 2026;27:297.
- Yan J, Ji J, Sun C, Xu W. "Multifactorial and multiplexed epigenomic profiling of five DNA modifications in plants with mDIP-seq." Science China Life Sciences. 2026;69:304-306.
- Zhou J, Wang X, Wei Z, Meng J, Huang D. "4acCPred: Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences." Molecular Therapy - Nucleic Acids. 2022;30:337-345.
- Li X, Wang Y, Zhang S, Zhang P, Huang S. "Nanopore Identification of N-Acetylation by Hydroxylamine Deacetylation (NINAHD)." ACS Sensors. 2024;9:1359-1371.
Disclaimer
For Research Use Only. This service is not intended for diagnostic procedures, patient management, or treatment decisions. Project feasibility, sample acceptance, controls, sequencing strategy, and analysis scope are finalized according to the submitted material and research objective.