DRIPc-seq Service for High-Resolution, Strand-Specific R-Loop Analysis

R-loops are specialised three-stranded nucleic acid structures formed by an RNA–DNA hybrid and a displaced single-stranded DNA. They are present in about 5% of the mammalian genome, particularly near promoters and transcription termination sites. While R-loops regulate transcription and chromatin state, their abnormal accumulation can compromise genome stability and contribute to disease.

CD Genomics provides DRIPc-seq (DNA–RNA hybrid immunoprecipitation with cDNA conversion and sequencing) services to generate genome-wide, strand-specific R-loop maps. Our optimised workflow delivers reproducible, high-resolution results that identify the precise RNA strands involved in R-loop formation.

Key Service Features

  • Strand-specific R-loop mapping
  • Genome-wide coverage with reproducible outputs
  • Rigorous quality control standards
  • Publication-ready datasets and visualisation
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how DRIPc-seq resolves R-loop mapping challenges by providing strand-specific, accurate RNA–DNA hybrid profiling
  • Accurate detection of R-loops at genome-wide scale
  • Strand-specific profiling of RNA origins
  • Reliable insights into transcription and genome stability
What Is Why DRIPc-seq How It Works Applications Project Why CD Genomics Deliverables Sample Requirements FAQs Case Study Demo Inquiry

What Is DRIPc-seq?

DRIPc-seq is an advanced method for R-loop mapping and R-loop profiling. It builds on the conventional DRIP-seq workflow by adding a cDNA conversion step, enabling the strand-specific identification of RNA species that form R-loops.

Using the S9.6 antibody, DRIPc-seq captures RNA–DNA hybrids with high affinity while excluding double-stranded DNA. The RNA strand within the hybrid is then converted into cDNA and sequenced, revealing whether mRNA, lncRNA, or circRNA drives R-loop formation.

Compared with related technologies such as DRIP-seq, ssDRIP, R-CHIP, and R-loop CUT&Tag, DRIPc-seq uniquely provides strand resolution. This allows researchers to move beyond simple localisation of R-loops and instead define their molecular origin.

Why Choose DRIPc-seq for R-Loop Profiling?

Traditional R-loop profiling methods identify hybrid regions but cannot determine which RNA strand is involved. This limitation makes it difficult to understand transcriptional regulation or the role of noncoding RNAs in genome dynamics. DRIPc-seq overcomes these challenges with strand-specific, reproducible results.

Key Advantages of DRIPc-seq

For biochemistry labs, CRO projects, and academic institutions, these advantages translate into more precise insights into transcriptional regulation, genome instability, and disease-associated R-loops.

How DRIPc-seq Works

The DRIPc-seq workflow combines selective enrichment, strand-specific cDNA conversion, and high-throughput sequencing to generate accurate R-loop profiles. Each step is optimised for sensitivity and reproducibility.

Workflow Steps

  • Extract genomic DNA and fragment it enzymatically.
  • Enrich RNA–DNA hybrids using the S9.6 antibody.
  • Release the RNA strand and synthesise cDNA with dUTP incorporation.
  • Treat with UNG to ensure strand specificity.
  • Construct sequencing libraries.
  • Perform high-throughput sequencing.
  • Conduct bioinformatic analysis for R-loop mapping and profiling.

DRIPc-seq workflow diagram showing sample input, nucleic acid extraction, antibody enrichment, RNA isolation, reverse transcription, adaptor ligation, library amplification, and quality control Rigorous quality control measures are included at each stage. Negative and positive controls confirm specificity, and sequencing depth is optimised to balance sensitivity with cost efficiency.

Applications of DRIPc-seq

DRIPc-seq supports a wide range of research questions by providing strand-specific insights into R-loop biology. Its high sensitivity and reproducibility make it valuable across both basic and applied studies.

Transcriptional regulation: Define how coding and noncoding RNAs contribute to gene expression control.

Genome stability: Investigate replication stress and DNA damage linked to R-loop accumulation.

Chromatin biology: Explore how R-loops influence chromatin organisation and epigenetic modifications.

Disease research: Characterise R-loop dynamics in cancer, autoimmune disorders, and neurodegenerative diseases.

Project Experience

CD Genomics has successfully completed thousands of R-loop sequencing and analysis projects, supporting clients worldwide in fields ranging from basic transcription research to translational medicine.

Category Examples
Animals Human, mouse, rat, pig, cattle, rhesus monkey, chicken, sheep, honeybee, shellfish, fish, and other tissues or cultured cells.
Plants Arabidopsis, rice, soybean, maize, tomato, strawberry, rapeseed, grape, apple, hemp, cotton, wheat, peach, tea, tobacco, Asteraceae species, eggplant, magnolia, and more.
Microorganisms Aspergillus flavus, E. coli, methanogens, actinomycetes, yeast, brown planthopper, whitefly, microalgae, Toxoplasma gondii, bacteria, and fungi.
Plant Tissues Leaves, seedlings, buds, stems, panicles, callus tissue, fruits, and roots.
Animal Tissues Blood, embryos, blastocysts, kidney, liver, bladder, cervix, thyroid, pancreas, spleen, thymus, stomach, ovary, mammary gland, skin, muscle, cartilage, colon, nervous tissue, brain, vascular tissue, mucosa, and sponge-like tissue.

Why Partner with CD Genomics?

CD Genomics has extensive experience in R-loop mapping and sequencing-based profiling technologies, including DRIP-seq, ssDRIP, R-CHIP, and R-loop CUT&Tag. Our DRIPc-seq service is supported by a dedicated team of molecular biologists and bioinformaticians who ensure each project meets the highest scientific standards.

Comprehensive solutions: From sample preparation through sequencing and bioinformatics analysis.

Rigorous quality control: Positive and negative controls included at each stage for reliable data.

Publication-ready results: Visualisation and annotation tailored for manuscripts and grant submissions.

Flexible support: Customisable workflows to accommodate different cell types, tissues, or experimental designs.

Data Outputs and Deliverables

Our DRIPc-seq service provides a complete data package designed to support both exploratory and hypothesis-driven projects. All results are carefully validated and delivered in publication-ready formats.

Deliverables

Sample Requirements

Sample Type Recommended Quantity Purpose Notes
Cells ≥ 2 × 10⁸ cells R-loop profiling via DRIPc-seq Ensures sufficient hybrid capture and reproducibility
Tissue ≥ 100 mg R-loop profiling via DRIPc-seq Adequate material for enzymatic fragmentation and IP
Genomic DNA ≥ 30 µg Direct input into library prep Must be high-quality—OD260/280 around 1.8–2.0, DNA-free
Other Formats Upon consultation Customized experimental designs CD Genomics can adapt protocols based on sample type, as needed

Frequently Asked Questions

Case Study: Dynamic and Conserved R-Loop Structures in Mammals

Reference: Sanz LA, Hartono SR, Lim YW, et al. Prevalent, dynamic, and conserved R-loop structures associate with specific epigenomic signatures in mammals. Molecular Cell. 2016;63(1):167–178. doi:10.1016/j.molcel.2016.05.032.

R-loops, formed by RNA–DNA hybrids and displaced single-stranded DNA, were historically considered transcriptional by-products. Increasing evidence links them to transcription regulation, chromatin remodeling, and genome instability. The study aimed to profile R-loops at high resolution using DRIPc-seq across human and mouse cells, to determine their prevalence, conservation, and association with chromatin states.

Technique: DRIPc-seq (DNA–RNA immunoprecipitation with cDNA conversion and sequencing) was performed alongside DRIP-seq for comparison.

Samples: Human Ntera2 (embryonal carcinoma) cells and mouse embryonic fibroblasts and stem cells.

Controls: RNase H digestion confirmed that detected signals were genuine RNA–DNA hybrids.

Analyses: Genome-wide profiling of DRIPc-seq peaks, correlation with gene expression (RNA-seq), and integration with epigenomic data (ENCODE histone modifications, DNase hypersensitivity, FAIRE-seq).

Prevalence: R-loops occupied ~5% of the human genome (~70,000 peaks) with a median size of 1.5 kb.

Dynamics: R-loops formed co-transcriptionally and were rapidly resolved after transcription inhibition, supporting dynamic turnover.

Conservation: Strong conservation was observed across human and mouse genomes, particularly at CpG island promoters and transcription terminators.

Chromatin states: Promoter R-loops correlated with open chromatin, CpG islands, H3K4 methylation, and DNA hypomethylation.

Terminal R-loops associated with enhancer- and insulator-like signatures involving CTCF and cohesin.

DRIPc-seq R-loop conservation across species with chromatin correlation. Conserved R-loop formation across human and mouse genomes detected by DRIPc-seq.

DRIPc-seq revealed that R-loops are abundant, dynamic, and conserved genomic features. They preferentially form at promoters and terminators of poly(A)-dependent genes, influencing transcription termination and chromatin structure. Rather than being accidental by-products, R-loops represent a programmed component of mammalian chromatin with regulatory potential.

Demo

R-loop peak annotation across genomic featuresR-loop peaks were classified into promoters, exons, introns, and intergenic regions, providing an overview of genomic distribution.

Pie chart showing R-loop peak distribution by genomic featurePie chart showing the proportion of R-loop peaks located in promoters, exons, introns, terminators, and intergenic regions.

Observed versus expected enrichment of R-loop peaksBar chart comparing the observed fraction of R-loop peaks (red) with the expected fraction based on genomic length (blue).

Metagene profile of R-loop signal around TSS and TESAverage R-loop peak density plotted around transcription start sites (TSS) and transcription end sites (TES), showing enrichment at gene boundaries.

  • Annotation of R-loop peaks across genomic features.
  • Distribution of R-loop peaks across genomic features.
  • Enrichment of R-loop peaks in different genomic features.
  • Metagene distribution of R-loop peaks across gene bodies.

References:

  1. He Z, Li M, Pan X, Peng Y, Shi Y, Han Q, Shi M, She L, Borovskii G, Chen X, Gu X, Cheng X, Zhang W. R-loops act as regulatory switches modulating transcription of COLD-responsive genes in rice. New Phytol. 2024 Jan;241(1):267-282. doi: 10.1111/nph.19315. Epub 2023 Oct 17. PMID: 37849024.
  2. Sun B, Sherrin M, Roy R. Unscheduled epigenetic modifications cause genome instability and sterility through aberrant R-loops following starvation. Nucleic Acids Res. 2023 Jan 11;51(1):84-98. doi: 10.1093/nar/gkac1155. PMID: 36504323; PMCID: PMC9841415.


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  • For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.
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