What is the difference between DAP-seq and ChIP-seq?

DAP-seq (DNA Affinity Purification sequencing) is an in vitro method that uses purified TF protein incubated with fragmented genomic DNA to identify binding sites, whereas ChIP-seq uses an antibody to pull down TF-bound chromatin from living cells. DAP-seq does not require TF-specific antibodies, works across any species with a sequenced genome, and avoids cellular context effects. However, DAP-seq lacks the native chromatin context that ChIP-seq captures.

Can I use existing RNA-seq or DAP-seq data for the integrated analysis?

Yes. If you already have RNA-seq data (FASTQ, BAM, or count matrices) and/or DAP-seq data (FASTQ, BAM, or BED peak files), we can start the integrated analysis from any of these entry points. Raw FASTQ files are recommended for optimal pipeline consistency.

What sequencing depth is recommended for DAP-seq?

We typically recommend 20-40 million paired-end reads per DAP-seq sample. The optimal depth depends on the TF binding repertoire size. An input genomic DNA control and negative control TF are recommended for background modeling.

How do you handle peak-to-gene assignment for distal regulatory elements?

We use a tiered assignment strategy: proximity-based assignment for promoter (2 kb) and genic-proximal (10 kb) peaks, supplemented by GREAT analysis for distal peaks, with optional Hi-C contact data or expression correlation integration for the most likely target gene identification.

Can integrated analysis be extended to include other omics layers?

Yes. The framework supports integration with translatomics data (Ribo-seq, polysome profiling, RNC-seq), small RNA-seq, epitranscriptomics data, proteomics, or metabolomics for multi-layer systems biology studies.

RNA-Seq & DAP-Seq Integrated Analysis Service

Uncover transcription factor regulatory networks by integrating transcriptome profiling with genome-wide TF binding mapping.

Understanding how transcription factors (TFs) orchestrate gene expression programs requires more than expression data alone — it requires direct evidence of where and when TFs bind the genome. DAP-Seq (DNA Affinity Purification sequencing) maps TF binding sites genome-wide in vitro, while RNA-seq quantifies the resulting transcriptomic output. Integrated analysis of these two data types enables researchers to distinguish direct TF targets from indirect effects, reconstruct regulatory networks, and prioritize functional binding events.

Our RNA-Seq & DAP-Seq Integrated Analysis Service provides a complete workflow — from RNA-seq library preparation and sequencing to DAP-seq data processing (customer-provided or partner-facilitated) through advanced integrative bioinformatics. We deliver high-confidence TF target gene lists, genome browser visualization of binding profiles, and biological context through pathway and enrichment analysis — empowering you to move from binding sites to mechanistic understanding.

End-to-end integration — RNA-seq transcriptome profiling coupled with DAP-seq TF binding analysis in a unified pipeline
High-confidence target identification — peak-to-gene assignment with multiple algorithms and statistical rigor to minimize false positives
Comprehensive regulatory analysis — motif discovery, ceRNA network integration, and pathway enrichment for biological interpretation
Flexible data options — RNA-seq performed by us; DAP-seq data may be customer-provided or coordinated through partner genomics services
Multi-layer integration — optionally incorporate translatomics data (Ribo-seq, polysome profiling) for post-transcriptional regulatory insight

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Integrated RNA-seq and DAP-seq analysis workflow: from TF binding and transcriptome data to gene regulatory network

What Integrated RNA-Seq & DAP-Seq Analysis Reveals

Transcription factors regulate gene expression by binding to specific DNA sequences in regulatory regions — yet identifying the genome-wide targets of a given TF and understanding how those binding events translate into transcriptional output remains a central challenge in regulatory genomics. DAP-Seq (DNA Affinity Purification sequencing) addresses the binding side of this equation by expressing the TF of interest as an in vitro protein, incubating it with fragmented genomic DNA, and sequencing the pulled-down DNA fragments to map genome-wide binding sites. RNA-seq addresses the expression side by quantifying transcript abundance under matched conditions — typically comparing TF perturbation (knockdown, knockout, or overexpression) against a control. The power of integrated analysis lies in connecting these two data types: determining which of the genes near DAP-seq binding peaks actually change expression when the TF is perturbed, thereby distinguishing direct targets from indirect downstream effects.

Our integrated analysis pipeline systematically processes both data modalities through a coordinated bioinformatics workflow. For RNA-seq, we provide comprehensive transcriptome quantification and differential expression analysis using our standard RNA Sequencing Service or Total RNA-Seq Service, supporting multiple library types (polyA-enriched, ribosomal-depleted, strand-specific) depending on your experimental requirements. For DAP-seq, we process data generated by the customer or through partner genomics services, performing quality control, read alignment, peak calling, and downstream integrative analysis — following the DAP-seq framework established in landmark studies that have profiled hundreds of TFs across plant and mammalian systems^[2]. The resulting output — a high-confidence set of direct TF target genes with associated regulatory binding sites, functional annotations, and network context — provides a quantitative foundation for mechanistic hypotheses about TF function.

To extend the analysis beyond transcriptional regulation, our Translatomics Sequencing portfolio — including Ribo-seq (Ribosome Footprinting) and Polysome Profiling — can be integrated for a multi-layer view of how TF binding affects not only transcript abundance but also translation efficiency, providing deeper mechanistic insight into post-transcriptional regulatory programs.

RNA-Seq Only vs. DAP-Seq Only vs. Integrated Analysis

Each individual approach captures only one facet of transcriptional regulation. RNA-seq alone cannot distinguish direct TF targets from indirect downstream effects. DAP-seq alone identifies where a TF binds but cannot determine which binding events are functional — i.e., which actually drive expression changes. Integrated analysis resolves both limitations simultaneously.

Feature	RNA-Seq Only	DAP-Seq Only	Integrated RNA-Seq + DAP-Seq (Our Service)
What it measures	Transcript abundance (gene expression levels)	Genome-wide TF binding sites (peak locations and signal intensity)	Both — binding events + corresponding expression changes in matched conditions
Direct vs. indirect targets	Cannot distinguish — differentially expressed genes include both direct TF targets and secondary effects	Cannot determine which binding events are functional (affect transcription)	Yes — peaks near differentially expressed genes indicate direct regulatory targets
Regulatory direction	Identifies up/down-regulated genes but not the regulatory logic	No expression information — cannot determine whether binding activates or represses	Binding + direction — determines whether TF binding correlates with activation or repression of each target
Network construction	Co-expression networks only (correlative, no causal direction)	Binding site map only (no target gene validation)	Regulatory network — causal edges from TF binding to target gene expression changes
Motif discovery context	Promoter motif analysis possible but lacks binding validation	De novo motif discovery from peak sequences — identifies preferred binding motifs	Motif + function — motifs linked to actual target gene regulation, enabling validation of motif variants
False positive rate	High for direct target claims (indirect effects confound)	Moderate — in vitro binding may include non-functional sites not occupied in vivo	Lower — requiring both binding evidence and expression change eliminates many false positives

Technical Advantages

End-to-End Multi-Omics Pipeline

From RNA-seq library construction and sequencing through DAP-seq peak calling and integrative analysis — a single coordinated pipeline eliminates data format compatibility issues, ensures consistent genome builds and annotation versions, and provides unified quality control across both data modalities. All analysis steps use version-controlled workflows with documented parameters for full reproducibility.

Multi-Algorithm Consensus Filtering

DAP-seq peaks are called using multiple algorithms (MACS2, GEM, PICS) with consensus-based filtering to retain only high-confidence binding sites. For peak-to-gene assignment, we integrate proximity-based, Hi-C contact-based, and expression correlation approaches to minimize both false-positive and false-negative target gene identification — a critical advantage over single-method pipelines.

Biologically Contextualized Output

We go beyond delivering lists of peaks and differentially expressed genes to provide fully contextualized results: transcription factor motif enrichment at bound regions, Gene Ontology and KEGG pathway enrichment of target genes, integration with public regulatory datasets (ENCODE, PlantCARE, JASPAR), and visualization-ready genome browser tracks. Optional ceRNA network and RBP interaction analysis extends the regulatory model to the post-transcriptional layer.

Our bioinformatics team works with you to design the optimal integration strategy — whether you need standard peak-to-gene correlation or advanced multi-layer models incorporating chromatin state, evolutionary conservation, or comparative genomics across species. For projects focused specifically on the translatome layer, complementary Enhanced Ribosome Profiling or RNC-seq data can be incorporated to assess the impact of TF binding on translation efficiency independently of transcript abundance.

Integrated RNA-Seq & DAP-Seq Analysis Workflow

Our six-phase workflow progresses from experimental design through biological interpretation, with integrated analysis serving as the core differentiator. The pipeline is modular — you may already have RNA-seq or DAP-seq data and need only the integrative bioinformatics phase.

Phase 1 — Experimental Design Consultation – Define TF of interest, perturbation strategy (KD/KO/OE vs. control), biological replicate requirements (3–5 per condition), sequencing depth (30–50 M reads/sample for RNA-seq; 20–40 M reads for DAP-seq), and integration goals
Phase 2 — RNA-Seq Library Preparation & Sequencing – PolyA-enriched or ribosomal-depleted library construction, strand-specific if required, paired-end 150 bp sequencing on Illumina platform (NovaSeq 6000 or equivalent)
Phase 3 — DAP-Seq Data Generation – TF protein expression and purification, genomic DNA fragmentation, in vitro binding and pull-down, library construction, and paired-end sequencing (coordinated through partner genomics services or customer-provided)
Phase 4 — Individual Data Processing & QC – RNA-seq: read alignment (STAR), quantification (Salmon), differential expression (DESeq2). DAP-seq: alignment (Bowtie2/BWA), peak calling (MACS2, GEM), consensus peak set generation, QC metrics (FRiP, peak number, reproducibility)
Phase 5 — Integrated Bioinformatics Analysis – Peak-to-gene assignment (proximity-based + alternative methods), correlation of binding signal with expression change, direct target identification, regulatory direction assignment, motif discovery at target gene peaks
Phase 6 — Biological Interpretation & Report – GO/KEGG enrichment, regulatory network visualization, genome browser tracks, comparative analysis with public datasets, written report with methods and results interpretation

Bioinformatics and Data Analysis

Our integrated bioinformatics pipeline combines best-in-class tools for each data modality with custom integration scripts. Standard analysis is included with every project; advanced and optional modules are available based on your research questions.

Analysis Type	Content Description
RNA-Seq Data Processing (Standard)
1. Read QC & Trimming	FastQC quality assessment, adapter trimming (Trimmomatic/cutadapt), and read filtering; rRNA removal efficiency assessment
2. Transcript Quantification	Splice-aware alignment (STAR two-pass), gene-level and transcript-level quantification (Salmon/Sailfish); raw count matrices and normalized expression (TPM/FPKM)
3. Differential Expression Analysis	DESeq2 or edgeR for pairwise and multi-group comparisons; adjusted p-value and log2 fold-change thresholds; MA plots, volcano plots, heatmaps of differentially expressed genes
DAP-Seq Data Processing (Standard)
4. Read Alignment & Peak Calling	Alignment to reference genome (Bowtie2/BWA), duplicate marking, peak calling with MACS2 (q < 0.05), GEM, and PICS; consensus peak set by intersection of ≥2 callers; peak annotation (promoter, genic, intergenic, distal)
5. Quality Metrics	Fraction of reads in peaks (FRiP), peak count and width distribution, genomic feature distribution, replicate reproducibility (IDR analysis if ≥2 replicates), cross-correlation metrics (NSC, RSC)
Integrated Analysis (Standard)
6. Peak-to-Gene Assignment	Proximity-based assignment (nearest gene, promoter window ±2 kb, genic ±10 kb), supplemented by alternative methods (GREAT, ChIPpeakAnno) for regulatory elements in gene-desert regions
7. Binding–Expression Correlation	Overlap analysis: DAP-seq peak-associated genes vs. differentially expressed genes; Fisher's exact test for enrichment significance; regulatory direction classification (activation vs. repression) based on expression change sign
8. Direct Target Identification	Intersection of (a) genes within peak-associated regions AND (b) differentially expressed genes in TF perturbation condition; confidence tiers based on peak strength, expression significance, and replicate consistency
Advanced Analysis (Optional)
9. Motif Discovery & Enrichment	De novo motif discovery (MEME-ChIP, HOMER, STREME) from target gene-associated peaks; comparison with known TF motifs (JASPAR, Cis-BP); position weight matrix (PWM) generation for novel TF motifs
10. Functional Enrichment	GO biological process, molecular function, and cellular component enrichment; KEGG and Reactome pathway analysis; transcription factor binding site enrichment in target gene promoters (Enrichr, ChIP-X Enrichment Analysis)
11. Regulatory Network Visualization	TF–target gene regulatory network construction (Cytoscape-compatible format); integrated heatmaps showing binding signal + expression change across conditions; genome browser tracks (IGV, UCSC) for visual inspection of binding peaks and gene loci
12. Multi-Layer Integration	Optional incorporation of translatomics data (Ribo-seq, Polysome Profiling, RNC-seq) to partition regulatory effects into transcriptional (RNA abundance) vs. translational (ribosome loading) components; ceRNA network integration for post-transcriptional regulatory context

Analytical Strategy

The central challenge in integrated RNA-seq and DAP-seq analysis is rigorously connecting binding events to transcriptional outcomes while controlling for the distinct noise characteristics of each assay. Our strategy employs a tiered filtering approach that progressively refines the binding signal based on functional evidence, delivering maximal biological confidence for target gene identification.

Tiered Integration Strategy for TF Target Identification

Our pipeline processes RNA-seq and DAP-seq data independently through initial QC and primary analysis, then joins them through a series of increasingly stringent integration tiers:

Tier 1 — Binding Evidence – Consensus peaks from multi-caller DAP-seq analysis, filtered by read support and IDR reproducibility. Peaks annotated to nearest gene and all genes within ±50 kb regulatory windows. Output: all potential TF binding regions.
Tier 2 — Expression Consequence – Differentially expressed genes from RNA-seq (perturbation vs. control), filtered for statistical significance (adjusted p < 0.05, |log₂FC| ≥ 1). Output: genes whose expression changes upon TF perturbation.
Tier 3 — Direct Target Integration – Intersection of Tier 1 and Tier 2: genes that both have a nearby DAP-seq peak AND change expression upon TF perturbation. Statistical enrichment assessed by Fisher's exact test or hypergeometric distribution to confirm that the overlap exceeds chance expectation.
Tier 4 — Regulatory Direction – Classification of each direct target by regulatory mode: activation (peak near gene, expression increases when TF is present) or repression (peak near gene, expression decreases). Promoter vs. distal binding preference analysis for each regulatory mode.
Tier 5 — Biological Context – Motif enrichment at direct-target-associated peaks to confirm binding specificity; GO/KEGG enrichment of direct targets to identify regulated pathways; network visualization to reveal regulatory hubs, feed-forward loops, and target gene cascades.

This tiered design ensures that each direct target call is supported by both binding evidence and functional consequence, dramatically reducing the false-positive rate compared to either assay alone. The pipeline is implemented as reproducible R/Python workflows with full parameter documentation.

Applications & Mechanisms

Integrated RNA-seq and DAP-seq analysis is broadly applicable across any biological system where transcription factor function and gene regulatory networks are under investigation. The approach is particularly powerful for de novo characterization of unstudied TFs, comparative analysis across conditions or species, and multi-omics dissection of complex regulatory programs.

Transcription Factor Target Identification

Define the complete set of direct target genes for any TF across any sequenced genome. Essential for characterizing novel TFs, validating predicted targets from ChIP-seq or literature, and resolving whether a TF acts primarily as an activator, repressor, or dual-function regulator. Particularly valuable for TF families with degenerate DNA-binding preferences where motif prediction alone is insufficient.

Gene Regulatory Network Reconstruction

Build causal regulatory networks linking TFs to their direct target genes, including cross-regulatory interactions between TFs (TF → TF target edges). Enables identification of regulatory hubs, feed-forward loop motifs, and downstream effector cascades. Network models can be integrated with expression data across developmental time points, tissues, or stress conditions for dynamic regulatory inference.

Developmental & Disease Biology

Dissect TF-driven regulatory programs in development (e.g., cell fate specification, organogenesis) and disease (e.g., oncogenic TF addiction in cancer, TF dysregulation in metabolic disorders). Integrate with translatomics data — such as Disome-seq for ribosome stalling analysis or Long-read RNC-seq for isoform-resolved translation efficiency — to reveal post-transcriptional regulatory layers modulated by TF activity.

Comparative & Evolutionary Regulatory Genomics

Compare TF binding landscapes and target gene repertoires across species, varieties, or ecotypes to understand how cis-regulatory divergence drives phenotypic variation. DAP-seq is uniquely suited for cross-species comparisons because it uses in vitro protein binding — eliminating species-specific antibody limitations of ChIP-seq — making it ideal for evolutionary studies of transcription factor binding site turnover and conservation.

Multi-Omics Regulatory Dissection

Combine integrated RNA-seq + DAP-seq analysis with additional omics layers for comprehensive regulatory dissection. For example: incorporate Polysome Profiling data to determine whether TF-regulated genes show concordant changes at the translation level; add Small RNA Sequencing data to assess TF regulation of miRNA expression; or combine with Epitranscriptomics profiling to explore whether TF targets are enriched for specific RNA modifications that modulate transcript stability or translation.

Deliverables

RNA-seq data — Raw FASTQ files, alignment files (BAM), gene-level quantification matrices, normalized expression data (TPM/FPKM)
DAP-seq data — Raw FASTQ files, alignment files (BAM), peak calls (BED/narrowPeak format), consensus peak set, peak annotation tracks
Differential expression results — Full gene-level results tables, filtered significant gene lists, diagnostic plots (MA, volcano, PCA, heatmaps)
Integrated analysis report — Direct target gene lists (all tiers), peak-to-gene assignment tables, binding-expression correlation statistics, regulatory direction classification
Functional annotation — GO/KEGG enrichment results, motif discovery output (MEME/HOMER), regulatory network files (Cytoscape-compatible .sif/.xgmml)
Visualization-ready tracks — Genome browser (IGV/UCSC) session files for integrated viewing of DAP-seq peaks and RNA-seq coverage across target gene loci
Methods & results report — Complete documentation of all analysis parameters, software versions, statistical methods, and biological interpretation

Data and Sample Requirements

Requirement	RNA-Seq	DAP-Seq
Input material	Total RNA ≥ 200 ng (RIN ≥ 7)	Purified TF protein + genomic DNA ≥ 1 μg
Sequencing depth	30–50 M paired-end reads per sample	20–40 M paired-end reads per sample
Replicates	3–5 biological replicates per condition	2–3 biological replicates per TF
Reference genome	Fully sequenced genome required (model species preferred; non-model supported with transcriptome assembly)
Controls	Matched control samples (wild-type, empty vector, or non-targeting sgRNA)	Input genomic DNA control (no TF), negative control TF (GFP or empty protein tag)

Important Notes:

For RNA-seq, total RNA quality is critical: RIN ≥ 7 for mammalian samples; DV200 ≥ 40% for FFPE or fragmented samples
For DAP-seq, we recommend customers provide purified TF protein or an expression construct (tagged with Halo, GST, or similar affinity tag); our partners can support TF expression and purification if needed
If you have existing RNA-seq or DAP-seq data and need only the integrated bioinformatics analysis, we accept raw FASTQ, aligned BAM, or pre-called peak files as starting points
Supported species include human, mouse, rat, zebrafish, Arabidopsis, rice, maize, and other sequenced model organisms; consult us for emerging model or non-model species

Case Study: Comprehensive Cistrome Mapping of 529 Transcription Factors in Arabidopsis via DAP-Seq

Despite the availability of fully sequenced plant genomes, the genome-wide binding sites of most transcription factors remain uncharacterized — limiting our understanding of how transcriptional programs govern development, metabolism, and stress responses. In a landmark study, O'Malley et al. applied DAP-seq to systematically map the cistrome (complete set of TF binding sites) of 529 transcription factors in Arabidopsis thaliana, generating the most comprehensive TF binding landscape for any multicellular organism at the time of publication^[2].

Figure 1. DAP-seq peak signal across Arabidopsis transcription factor families.
Heatmap representation of DAP-seq signal intensity (log2 enrichment over input control) across 529 TFs clustered by family, showing the diversity of binding profiles — from broadly binding TFs with thousands of peaks to sequence-specific TFs with narrow target repertoires.

Figure 2. Methylation sensitivity landscape of Arabidopsis TFs (Epicistrome).
DAP-seq with methylated vs. unmethylated genomic DNA revealed that >75% of Arabidopsis TFs are methylation-sensitive. This figure illustrates the epicistrome concept — where DNA methylation status at binding sites determines whether a TF can bind, providing an additional layer of regulatory specificity beyond DNA sequence alone.

Background

ChIP-seq requires TF-specific antibodies that are unavailable for the vast majority of Arabidopsis TFs, limiting prior regulatory studies to a few well-characterized factors. DAP-seq overcomes this limitation by using in-vitro-expressed TFs with affinity purification, enabling high-throughput binding site discovery for any TF without needing antibodies.

Methodology

Full-length coding sequences of 529 Arabidopsis TFs spanning 25 families were cloned and expressed in vitro with HaloTag affinity purification. DAP-seq was performed using fragmented Arabidopsis genomic DNA, retaining native 5-methylcytosine marks. Parallel RNA-seq data from public Arabidopsis transcriptome compendia were integrated to correlate TF binding with gene expression across developmental stages and stress conditions.

Results

The study resolved motifs and genome-wide binding peaks for 529 TFs, revealing that >75% (248/327 surveyed) of Arabidopsis TFs are methylation-sensitive — a finding that fundamentally changed understanding of how DNA methylation shapes TF binding landscapes (the "epicistrome"). The resource enabled systematic analysis of binding site architecture, TF family-specific DNA recognition preferences, and the impact of epigenetic marks on cis-regulatory element function. This study serves as the foundational validation of the DAP-seq approach and demonstrates its scalability for comprehensive cistrome mapping in any organism with a sequenced genome.

FAQs – Frequently Asked Questions

- Q: What is the difference between DAP-seq and ChIP-seq?
  - DAP-seq (DNA Affinity Purification sequencing) is an in vitro method that uses purified TF protein incubated with fragmented genomic DNA to identify binding sites, whereas ChIP-seq uses an antibody to pull down TF-bound chromatin from living cells. DAP-seq does not require TF-specific antibodies, works across any species with a sequenced genome, and avoids cellular context effects — making it ideal for TFs where antibodies are unavailable or where in vivo chromatin state might obscure binding preferences. However, DAP-seq lacks the native chromatin context (nucleosome positioning, histone modifications, cooperativity) that ChIP-seq captures. The two methods are complementary: DAP-seq reveals intrinsic DNA-binding preferences, while ChIP-seq captures binding in the native cellular environment.
- Q: Can I use existing RNA-seq or DAP-seq data for the integrated analysis?
  - Yes. If you already have RNA-seq data (FASTQ, BAM, or count matrices) and/or DAP-seq data (FASTQ, BAM, or BED peak files), we can start the integrated analysis from any of these entry points. We recommend providing raw FASTQ files for optimal pipeline consistency, but downstream analysis is fully supported from pre-processed data. Please contact us with your data format so we can design the most efficient analysis workflow.
- Q: What sequencing depth is recommended for DAP-seq?
  - We typically recommend 20–40 million paired-end reads per DAP-seq sample. The optimal depth depends on the size of the TF binding repertoire: TFs with thousands of binding sites (e.g., broadly binding TFs like MYB or bZIP families) benefit from deeper sequencing to saturate detection, while TFs with fewer specific sites may achieve saturation at 20 million reads. We also recommend an input genomic DNA control (no TF) sequenced to the same depth for background modeling, and a negative control TF (e.g., GFP) to assess non-specific DNA binding.
- Q: How do you handle peak-to-gene assignment for distal regulatory elements?
  - We use a tiered peak-to-gene assignment strategy. Primary assignment uses proximity-based methods: peaks within 2 kb of a transcription start site are assigned to that promoter; peaks within 10 kb of a gene body are assigned as genic-proximal. For distal peaks (>10 kb from any gene), we supplement with GREAT (Genomic Regions Enrichment of Annotations Tool) and optionally integrate chromatin contact data (Hi-C, if available) or expression correlation to identify the most likely target gene. Each assignment tier is tracked in the output so you can distinguish high-confidence promoter-proximal targets from exploratory distal assignments.
- Q: Can integrated analysis be extended to include other omics layers?
  - Absolutely. The integrated analysis framework is modular and extensible. You can combine RNA-seq + DAP-seq with translatomics data (Ribo-seq, polysome profiling, RNC-seq) to assess how TF binding affects translation efficiency independently of transcript abundance. Small RNA-seq data can reveal TF regulation of miRNA expression. Epitranscriptomics data (m6A-MeRIP-seq, m5C-seq) can identify whether TF targets are enriched for specific RNA modifications. We also support integration with proteomics or metabolomics data for multi-layer systems biology studies. Please discuss your specific integration needs during the project design phase.
- Q: What species are supported for DAP-seq analysis?
  - DAP-seq can be performed for any species with a sequenced reference genome. The method has been extensively validated in plants (Arabidopsis, maize, rice, soybean, wheat) and is increasingly applied in mammalian systems (human, mouse) and non-model organisms. For species without a fully assembled genome, we can use a related species genome or de novo transcriptome assembly as reference, though peak-to-gene assignment may be less precise in diverged regulatory regions.

References:

Bartlett A, O'Malley RC, Huang SC, et al. Mapping genome-wide transcription-factor binding sites using DAP-seq. Nat Protoc. 2017;12(8):1659-1672. DOI: 10.1038/nprot.2017.055
O'Malley RC, Huang SC, Song L, et al. Cistrome and Epicistrome Features Shape the Regulatory DNA Landscape. Cell. 2016;165(5):1280-1292. DOI: 10.1016/j.cell.2016.04.038