DRIPc-seq R-loop Profiling Service for Strand-Specific Mapping
Discover genome-wide R-loop dynamics with single-strand precision.
CD Genomics offers a strand-specific DRIPc-seq service to help researchers decode RNA:DNA hybrid structures and their impact on transcription, chromatin, and genome stability.
Our optimized platform combines high-affinity S9.6 immunoprecipitation, directional cDNA library prep, and expert bioinformatics to deliver accurate, reproducible maps of R-loop activity—critical for epigenetic studies, replication-transcription conflict analysis, and non-coding RNA research.
R-loops: Gatekeepers of Genome Stability and Transcriptional Regulation
R-loops are three-stranded nucleic acid structures that form when a newly transcribed RNA molecule hybridizes back to its DNA template strand, displacing the non-template strand in the process. Once considered incidental, they are now recognized as pervasive elements with important biological functions.
Why R-loops Deserve Attention
They shape transcriptional activity. R-loops are commonly found at gene promoters, transcription terminators, and enhancer regions, where they can influence RNA polymerase dynamics and regulatory factor binding.
They mark epigenetic transitions. These structures often coincide with chromatin remodeling events, CpG islands, and DNA methylation changes.
They connect to non-coding RNA biology. R-loops formed by lncRNAs, circRNAs, and unspliced pre-mRNAs play distinct roles in transcriptional control, insulation, and RNA-guided targeting.
What Makes Them Technically Difficult to Study
Strand ambiguity. Most conventional methods detect hybrids, but can't tell which RNA strand is involved—or where it originated from.
They're context-sensitive. R-loop formation is dynamic, condition-dependent, and often transient, making precise detection especially challenging.
They carry risk. When unresolved, R-loops can interfere with replication forks, trigger DNA damage, and contribute to genome instability.
Why Strand-Resolved Mapping Matters
To fully understand R-loop function, it's not enough to detect their presence—you need to know:
Which gene or transcript initiated the hybrid
In which direction transcription occurred
How hybrid formation relates to chromatin context, stress response, or cellular state
That's where DRIPc-seq comes in. This method provides strand-specific, transcript-informed R-loop maps that allow researchers to explore these structures with the resolution needed to uncover their true roles in genome regulation.
Schematic of an R-loop.
DRIPc-seq: The Gold Standard for Strand-Resolved R-loop Mapping
R-loops are not random byproducts of transcription—they are structured, functional intermediates that can act as regulatory elements or sources of genomic instability. Understanding where they form, under what conditions, and which RNA strands are involved requires a technology that goes beyond location—it demands directionality and resolution.
DRIPc-seq (DNA:RNA Immunoprecipitation with cDNA sequencing) addresses this need by providing genome-wide, strand-specific maps of R-loop structures. Unlike conventional DRIP-seq, which captures the presence of DNA:RNA hybrids without discriminating the RNA origin, DRIPc-seq sequences the RNA strand directly. This enables researchers to determine which gene or non-coding transcript contributed to R-loop formation and in which direction transcription occurred.
At CD Genomics, we've optimized the DRIPc-seq platform to preserve native R-loop structures while maximizing strand recovery fidelity. Through a precise workflow involving S9.6-mediated immunoprecipitation, enzymatic RNA release, directional library preparation, and high-depth sequencing, we generate high-confidence datasets that are both sensitive and reproducible—even in complex samples or under subtle experimental perturbations.
This level of detail is especially valuable when:
Distinguishing sense vs. antisense transcriptional R-loops at promoters or gene bodies
Investigating the interplay between R-loops and chromatin remodeling
Tracking dynamic changes in R-loop patterns under different cellular states or treatments
Uncovering the contributions of lncRNAs and other non-coding RNAs to epigenomic regulation
Whether you are studying transcriptional regulation, replication interference, or genome integrity, DRIPc-seq empowers you with the resolution and specificity to explore R-loop biology at the molecular level.
What Sets DRIPc-seq Apart: Technical Highlights
Not all R-loop mapping techniques offer the same level of specificity or resolution. DRIPc-seq stands out by directly sequencing the RNA component of DNA:RNA hybrids, enabling directional and transcript-aware profiling. This distinction is essential for researchers who need precise insights into transcriptional regulation, non-coding RNA behavior, or chromatin interaction dynamics.
Core Technical Strengths of DRIPc-seq
Strand specificity at the transcript level
By converting the RNA strand within R-loops into directional cDNA libraries, DRIPc-seq allows you to pinpoint exactly which transcript—and which transcriptional direction—produced the hybrid.
High affinity and specificity via S9.6 IP
The S9.6 monoclonal antibody offers sub-nanomolar affinity for RNA:DNA hybrids while avoiding interaction with dsDNA, ensuring selective and reproducible R-loop enrichment.
Preservation of native structures
The protocol avoids harsh enzymatic or chemical steps that could destabilize fragile hybrids, preserving the biological relevance of detected R-loops.
Reduced background, clear signal
Low-noise data output and well-defined peaks improve interpretability, especially in complex genomes or low-input samples.
Robust compatibility with different sample types
Whether you are working with mammalian cells, plant tissues, or isolated genomic DNA, DRIPc-seq adapts easily with minimal optimization.
Consistent results across replicates
Carefully controlled workflows and quality assurance steps (including internal spike-ins and negative controls) support data reproducibility across experiments and conditions.
Workflow
Step-by-Step: Our DRIPc-seq Experimental Workflow
Each DRIPc-seq project at CD Genomics is executed through a rigorously optimized, strand-preserving workflow. From sample processing to data delivery, every step is designed to ensure the integrity of R-loop structures and the reliability of sequencing results.
Bioinformatics
Bioinformatics: Making Your DRIPc-seq Data Truly Informative
When you invest in DRIPc-seq, you're not just looking for mapped reads—you need data that tells a story. At CD Genomics, our bioinformatics pipeline is designed to give researchers like you the context, confidence, and clarity to draw meaningful conclusions from R-loop data.
We approach DRIPc-seq analysis with one goal: to help you see where R-loops form, which transcripts are involved, and what those patterns might mean in your biological system.
Here's what our analysis includes:
Strand-aware alignment and rigorous QC
Every read is carefully processed and aligned with strand information retained—so you can tell exactly which RNA strand initiated the hybrid.
R-loop peak identification
Using a custom strategy tailored to DRIPc-seq data, we call hybrid-enriched regions with high specificity and minimal noise.
Transcript-level annotation
We map each R-loop to its source gene or transcript, including strand direction, so you can distinguish sense from antisense activity—even in overlapping transcription zones.
Genomic distribution analysis
You'll get detailed charts showing where R-loops accumulate: promoters, gene bodies, 3′ UTRs, intergenic regions, and more—helping you interpret regulatory context.
Motif analysis (optional)
For researchers exploring binding preferences or structural features, we can identify enriched sequence motifs at R-loop sites.
Seamless integration with other datasets
Outputs are compatible with your RNA-seq, ChIP-seq, or ATAC-seq data—so you can explore multi-layered regulation without additional formatting.
Deliverables
What You'll Receive: Data Driven Insights
DRIPc-seq from CD Genomics delivers more than sequencing — it provides a complete analytical package, ready for interpretation, visualization, and publication. You'll receive high-quality, strand-specific data tailored to epitranscriptomic research and R-loop biology.
Your Project Package Includes:
Raw Sequencing Data
Paired-end FASTQ files from Illumina sequencing, suitable for reanalysis or integration into your pipelines.
Aligned Read Files
Strand-resolved alignment files mapped to your reference genome, viewable in IGV or UCSC Genome Browser.
R-loop Peak Annotation Tables
BED and Excel tables with genomic coordinates, transcript strand direction, peak scores, and functional categories (e.g., promoter, exon, intergenic).
Genomic Feature Distribution Plots
Visualizations showing R-loop enrichment across gene elements (TSS, TES, gene bodies, etc.), along with motif and length enrichment analyses.
Ready-to-Use Genome Tracks
BigWig files for quick inspection and integration into genome browsers.
Project Summary Report
A publication-ready PDF report including QC metrics, read stats, mapping summaries, representative peak profiles, and interpretation notes.
Sample Requirements
Sample Requirements: Ensuring Optimal Results
Category
Requirements
Sample Types
Cultured cells, tissue, high-quality genomic DNA, or IP-enriched hybrid material. Other types, please inquire.
Recommended Input
Cells: ~2×10⁷ Tissue: ~100 mg gDNA: >30 μg
Storage Conditions
Cells/Tissues: Snap-freeze in liquid nitrogen, store at −80 °C DNA: −80 °C, avoid freeze–thaw cycles
Shipping Instructions
Use 1.5 mL RNase-free tubes, seal tightly, and ship on dry ice
Not sure if your sample is compatible? Contact us for personalized guidance before submission.
Application
How DRIPc-seq Supports Real Research Questions
As a researcher, you're not just looking to detect R-loops — you're trying to understand what they're doing in your system. That's where DRIPc-seq becomes valuable. By providing strand-specific, transcript-aware mapping of DNA:RNA hybrids, this method helps connect transcriptional events to genomic function in a way that traditional approaches often can't.
Researchers use DRIPc-seq to address questions like:
"Which transcripts are forming hybrids, and in what direction?"
With strand resolution, you can distinguish sense from antisense R-loops and dissect overlapping transcription events—particularly around promoters and bidirectional regions.
"Is a non-coding RNA forming R-loops that might affect chromatin structure?"
DRIPc-seq helps trace R-loops back to their RNA origin, making it possible to study how lncRNAs or circRNAs participate in chromatin regulation or transcriptional interference.
"Where are hybrids accumulating under stress?"
If you're working with cells under replication stress or topological tension, DRIPc-seq reveals where hybrids persist—often highlighting vulnerable or regulated loci.
"How does R-loop distribution change across conditions or timepoints?"
Whether comparing treatments, knockdowns, or developmental stages, you can monitor hybrid dynamics with clear spatial and directional resolution.
"How do R-loops relate to my other datasets?"
DRIPc-seq integrates naturally with RNA-seq, ChIP-seq, and epigenetic data, allowing you to build a more complete picture of transcriptional regulation.
Advantages
Why Scientists Trust CD Genomics for R-loop Profiling
Choosing the right partner for DRIPc-seq isn't just about sequencing capacity—it's about knowing your data will be biologically meaningful, technically sound, and tailored to your experimental goals.
At CD Genomics, we work closely with researchers who are asking complex questions about transcription, genome structure, and regulatory mechanisms. We've built our DRIPc-seq service to meet those demands—not just with precision lab work, but with scientific understanding.
Here's why many teams trust us with their R-loop projects:
We understand the biology behind the signal
Our team isn't just running protocols—we know what R-loops represent in your system. We design our workflow and analysis to preserve strand information, reduce background, and help you see real patterns, not artifacts.
We care about reproducibility and clarity
From careful sample handling to customized peak calling, we focus on consistency. Every dataset includes QC checkpoints, strand annotations, and formatted outputs so you can compare across conditions or revisit results months later.
We bridge wet lab and bioinformatics
Our strength is the integration of clean immunoprecipitation with thoughtful, biology-aware analysis. You don't just get files—you get structured results that support interpretation, integration, and publication.
We're built for collaboration
Need help selecting controls? Not sure how to scale your sample prep? Want to connect DRIPc-seq to other omics data? We'll walk you through it—not with generic answers, but with input grounded in real experimental logic.
