R-Loops in Transcription Regulation
Uncover initiation, pausing, and termination dynamics with strand-specific precision.
DRIPc-Seq Analysis Service
Accelerate R-loop discovery with Creative Proteomics' DRIPc-Seq—a strand-specific, IP-safe workflow that converts complex hybrid signals into actionable biological insights. By combining antibody enrichment and RNase H validation with Illumina paired-end sequencing and expert analytics, we help you confidently answer questions in transcription regulation, enhancer function, splicing, and genome stability.
- Strand-resolved R-loop maps (RNA captured directly for native orientation)
- RNase H–validated specificity to minimize false positives
- IP-safe, reagent-agnostic libraries (no patented add-ons)
- Illumina paired-end data + rigorous QC dashboards
- Multi-omics integration with RNA-seq/ChIP-seq/ATAC-seq
What Is DRIPc-Seq?
DNA:RNA Immunoprecipitation followed by sequencing of the RNA (DRIPc-Seq) is a high-resolution technique designed to map R-loops, the three-stranded nucleic acid structures formed during transcription. Unlike DRIP-seq, which sequences DNA fragments bound to DNA:RNA hybrids, DRIPc-seq captures and sequences the RNA component directly, enabling strand-specific mapping of R-loops across the genome. This provides a more comprehensive understanding of transcriptional regulation and genome instability events linked to R-loop formation.
Typical Scientific Questions DRIPc-Seq Can Answer
- Where are R-loop hotspots across the genome (promoters, enhancers, termination regions, long genes)?
- How do R-loops relate to transcription initiation/pausing/termination and enhancer activity?
- How do genetic perturbations or treatments reshape R-loop architecture?
- Do R-loops co-localize with replication stress or instability-prone regions?
- What is the relationship between R-loops, RNA processing, splicing junctions, and chromatin states?
Advantages of Our DRIPc-Seq Service
High-Specificity Enrichment — RNase H Validation
Integration of RNase H–treated controls reduces background, ensuring >80% signal reduction at genuine R-loop regions.
Strand-Resolved Profiling — Direct RNA Sequencing
Captures the RNA strand directly, achieving >95% correct orientation assignment and eliminating ambiguity common in DNA-only methods.
Deep Coverage — High-Throughput Illumina Platforms
Standard sequencing depth of 30–60 million paired-end reads per sample detects >90% of promoter-associated R-loops in human and mouse genomes.
Robust Data Quality — Optimized Library Complexity
Delivers >80% unique reads after deduplication and >90% genome mapping rate, ensuring reproducible and interpretable datasets.
Quantitative Comparability — Input Normalization
Input-based scaling keeps replicate variation below 10% coefficient of variation (CV), enabling reliable cross-condition comparisons.
Technical Services
Service Scope
Workflow and Instrumentation
Sample Requirement
Deliverables
FAQ
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Scope of DRIPc-Seq Services at Creative Proteomics
Enhancer and Noncoding Regulatory Mapping
Profile enhancer-associated R-loops and link them to promoter activity.
Genome Instability and Replication Stress Insights
Identify fragile sites and conflict-prone regions shaped by R-loops.
R-Loops and RNA Splicing Programs
Connect R-loop landscapes to exon–intron boundaries and alternative splicing choices.
Chromatin and Epigenomic Context
Integrate R-loop distribution with histone modifications, open chromatin, and boundary elements.
Structured DNA and Long Gene Analysis
Characterize R-loops within repeats, G-rich motifs, and transcriptionally demanding long genes.
Perturbation and Comparative Studies
Measure R-loop changes under genetic alterations, treatments, or developmental conditions.
Recommended Experimental Design & Controls
- Controls: Include Input and Mock-IP; add an RNase H–treated arm to confirm specificity (typically >80% signal loss at true hybrids).
- Replicates: Use ≥2 biological replicates per condition (prefer 3 for differential comparisons).
- Balanced Design: Randomize extraction/library/lanes; mirror controls and replicate counts across groups to avoid batch bias.
- Normalization Plan: Apply input-based scaling; for multi-batch/longitudinal work, add non-proprietary external standards.
- Targeted Validation (optional): Perform DRIP-qPCR at priority loci to confirm genome-wide calls.
Our DRIPc-Seq Service Workflow
1
Sample Intake & QC
Integrity check of RNA (RIN), DNA contamination assessment, and purity screening. Feasibility feedback provided before processing.
2
Immunoprecipitation
DNA:RNA hybrid capture with validated antibody. Controls include Input and Mock-IP; RNase H–treated sample verifies specificity.
3
RNA Release & Library Prep
RNA strands gently recovered from hybrids and prepared into strand-specific libraries, preserving transcription orientation without patented chemistries.
4
Sequencing
High-throughput Illumina paired-end sequencing; depth optimized to project goals (typically 30–60M reads/sample).
5
Primary Data Processing
Quality filtering, adapter trimming, and orientation-aware alignment to reference genome. Library complexity monitored by duplicate assessment.
6
Peak Calling & Quantification
R-loop enriched regions identified with integrated controls; RNase H validation removes false positives. Peak lists, coverage tracks, and matrices generated.
7
Comparative & Functional Analysis
Differential R-loop profiling across groups, genomic annotation, and pathway enrichment. Optional integration with RNA-seq, ChIP-seq, or ATAC-seq for deeper insights.
Technical Strengths and Platform Capabilities for DRIPc-Seq
Immunoprecipitation System
- High-specificity DNA:RNA hybrid capture using validated antibody-based enrichment.
- Integrated RNase H–treated control channel for signal specificity verification.
Library Preparation Platform
- Strand-specific RNA library construction optimized for transcriptional orientation.
- Non-patent-encumbered workflows ensuring flexible customization.
Sequencing Instruments
- Illumina NovaSeq and NextSeq platforms providing high-throughput paired-end sequencing.
- Configurable read lengths (PE75–PE150) with standard depth of 30–60 million reads per sample.
Performance Parameters
- ≥90% alignment rate to reference genome under standard conditions.
- 80% unique reads retained post-deduplication, supporting reproducibility.
Illumina NovaSeq 6000
Illumina NextSeq 2000
Sample Requirements for DRIPc-Seq Assay
| Sample Type | Input Amount (Min) | Quality Criteria | Storage & Shipping |
| Cell pellets | ≥ 1×10⁷ cells | High viability; minimal apoptosis | Snap-frozen; ship on dry ice |
| Tissue | ≥ 50 mg | Fresh-frozen; avoid repeated freeze–thaw | Ship on dry ice |
| Total RNA (purified) | ≥ 5 µg | RIN ≥ 7.0; A260/280 ≈ 1.8–2.1; DNase-treated optional | RNase-free tubes; cold chain |
| Other matrices | By consultation | Matrix-specific inhibitor assessment | Per agreed SOP |
What You Will Receive from Our DRIPc-Seq Projects
Raw Sequencing Data: FASTQ files with full sequencing QC reports.
Processed and Aligned Data: BAM alignment files, strand-specific coverage tracks (bigWig), and deduplicated data sets.
R-Loop Detection Outputs: High-confidence peak lists (BED/bedGraph), quantitative matrices, and control-integrated annotations.
Functional and Comparative Insights: Differential R-loop analysis, pathway and GO enrichment, and optional integration with RNA-seq, ChIP-seq, or ATAC-seq.
Visualization Resources: Genome browser tracks, heatmaps, metagene profiles, and publication-ready figures.
Comprehensive Report: Methods summary, QC dashboards, and clear interpretation to support presentations or manuscripts.
QC Metrics Plot
Quality assessment of sequencing data. Left: Q30 score distribution across sequencing cycles. Right: GC content distribution of reads.
Metagene Profile & Heatmap
Metagene analysis of R-loop distribution. Average profile (left) shows strong enrichment at transcription start sites (TSS) and termination sites (TES). Heatmap (right) displays gene-level patterns across 100 representative genes.
Genome Browser Tracks
Genome browser view showing R-loop enrichment. DRIPc-Seq IP track reveals strong hybrid signal compared with Input; RNase H–treated control eliminates the peak, confirming specificity.
Differential Analysis Volcano Plot
Volcano plot of differential R-loop enrichment. Significant upregulated regions (purple) and downregulated regions (light purple) are highlighted against background non-significant points.
DRIPc-Seq vs Other RBP–RNA Mapping Methods: Which Should You Choose?
| Selection Criteria | DRIPc-Seq | DRIP-seq | ssDRIP-seq | R-ChIP / MapR | qDRIP (quantitative DRIP) | DRIP-qPCR |
| Primary readout | RNA from DNA:RNA hybrids | DNA fragments from hybrids | DNA fragments (strand-aware) | RNase H1–based hybrid capture | DRIP-seq with quantitative normalization | Locus-specific qPCR after DRIP |
| Strand information | Direct, native orientation | Indirect / not native | Orientation inferred (strand-specific prep) | Orientation from RNase H1 binding profile | Same as DRIP-seq | None (targeted loci only) |
| Resolution & scale | Genome-wide; promoter/enhancer/TES directionality | Genome-wide; robust broad peaks | Genome-wide; improved directionality over DRIP-seq | Genome-wide; high specificity in cell lines | Genome-wide; cross-batch comparability | Targeted, a few to dozens of loci |
| Best for | Transcription-centric questions (initiation/pausing/termination), enhancer linkage, splicing-adjacent R-loops | Stable R-loop landscapes, broad domains, baseline surveys | Directional DNA-side mapping without RNA libraries | High-specificity mapping when RNase H1 can be expressed/delivered | Experiments needing rigorous between-condition normalization | Fast validation of candidate loci; orthogonal confirmation |
| Key controls | Input, Mock-IP, RNase H verification | Input, Mock-IP, RNase H recommended | Input, Mock-IP, RNase H recommended | Proper negative controls; expression controls for RNase H1 | Input-normalized; external non-proprietary standards optional | RNase H ± locus primers |
| Sample compatibility | Cells, fresh-frozen tissues, purified RNA | Cells, tissues | Cells, tissues (library demands higher) | Best in transfectable cell lines; limited in primary tissues | Same as DRIP-seq | DNA/RNA from cells or tissues (assay-dependent) |
| Typical outputs | Strand-specific bigWig, peak BED, RNA-oriented R-loop maps | Peak BED, coverage tracks | Direction-aware peak sets, coverage | RNase H1-anchored maps, high-confidence peaks | Quantitative peak tables comparable across runs | Fold-enrichment at selected loci |
| When not ideal | Projects needing only a few loci confirmed | Fine strand resolution not required | Hard samples with limited library quality | Primary tissues or non-transfectable samples | One-off screens without need for cross-project comparison | Discovery-scale, genome-wide mapping |
You May Want to Know
How is DRIPc-Seq different from DRIP-seq or ssDRIP-seq?
DRIPc-Seq sequences the RNA component (native orientation); DRIP-seq/ssDRIP-seq profile DNA fragments (orientation inferred); choose DRIPc-Seq for transcription-centric questions, ssDRIP-seq for DNA-structure emphasis.
Which controls are included?
Input and Mock-IP as standards; RNase H–treated control to confirm hybrid specificity.
What sequencing setup do you use?
Strand-specific paired-end Illumina; depth and read length tailored to study goals and genome complexity.
Can you integrate with other omics?
Yes—optional alignment with RNA-seq, ChIP-seq, and ATAC-seq for mechanism-level interpretation.
How do you ensure specificity and reduce false positives?
RNase H validation, matched controls, background modeling, and orientation-aware alignment.
What reference genomes/species are supported?
Common model organisms out of the box; custom references for non-model species on request.
Is low-input or partially degraded material acceptable?
Often feasible after pre-QC and protocol tuning; we advise on risk and optimization before proceeding.
How are replicates handled?
Biological replicates per condition are recommended for robust statistics and reliable differential calls.
What are the typical QC checkpoints?
Sample integrity/purity, library complexity, mapping/strandness metrics, control suppression (RNase H), and peak-to-background ratios.
What limitations should I know?
Repetitive/GC-extreme regions and antibody cross-reactivity are mitigated but not eliminated; RNase H control and careful interpretation address these risks.
Can you prioritize candidate loci for validation?
Yes—ranked hotspot lists and locus panels for DRIP-qPCR or orthogonal assays are available.
Is DRIPc-Seq the same as DRIP-seq?
No; DRIPc-Seq sequences RNA for native strand information, while DRIP-seq sequences DNA fragments.
Do I need RNase H treatment for R-loop mapping?
It is strongly recommended to verify hybrid specificity and suppress false positives.
Can DRIPc-Seq detect enhancer-linked R-loops?
Yes; enhancer occupancy and promoter linkage can be profiled with strand directionality
Which antibody is used to capture DNA:RNA hybrids?
A validated anti-hybrid antibody (e.g., S9.6) within a control-rich workflow to ensure specificity.