What is R-loop sequencing used for?

R-loop sequencing detects RNA–DNA hybrids to study transcription regulation, genome stability, and epigenetic mechanisms.

Which assay should I choose?

DRIP-seq is best for genome-wide mapping, DRIPc-seq for RNA strand origin, ssDRIP for displaced DNA, R-CHIP for protein-linked studies, and CUT&Tag for low-input samples.

Can R-loop assays be performed on limited or clinical samples?

Yes, CUT&Tag and optimised protocols support as few as 0.5–5×10^5 cells, suitable for rare or clinical materials.

What sample types are supported?

We accept mammalian and plant cells, tissues, blood, organoids, and microorganisms including yeast, fungi, and bacteria.

How are results delivered?

Clients receive FASTQ, BAM, BED, and BigWig files plus an interactive HTML report with publication-ready figures.

Do you provide integrative analysis with other omics?

Yes, we routinely integrate R-loop profiles with RNA-seq, ChIP-seq, ATAC-seq, and RNA modification datasets.

How do you ensure accuracy and reduce false positives?

RNase H–treated controls, replicate concordance, and strand bias analysis are applied to confirm specificity.

R-Loop Sequencing and Analysis Services for Genome-Wide Hybrid Profiling

CD Genomics offers comprehensive R-loop sequencing, R-loop analysis, and r-loop assay services to uncover RNA–DNA hybrid structures with accuracy and depth. R-loops play essential roles in transcription, chromatin regulation, and genome stability, but uncontrolled accumulation is linked to genome instability, cancer, and neurological disorders. Traditional methods struggle to distinguish true R-loops from non-specific RNA binding, creating a barrier for precise research.

Our advanced assays solve these challenges by combining antibody- and RNaseH1-based capture with next-generation sequencing, delivering reliable, strand-specific results for a wide range of samples.

Key advantages:

Accurate detection of RNA–DNA hybrids with reduced background noise
Strand-specific identification of RNA and displaced DNA within R-loops
High-resolution, low-input compatible workflows (CUT&Tag, ssDRIP)
Multi-omics integration with transcriptome and epigenome analysis

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R-loop analysis service illustration showing DNA-RNA hybrid and sequencing methods

Genome-wide R-loop sequencing (DRIP-seq / ssDRIP)
Strand-specific R-loop analysis (DRIPc-seq)
Low-input r-loop assays (CUT&Tag / R-CHIP)
Functional insights via multi-omics integration

Why Study R-Loops?

R-loops are unique three-stranded nucleic acid structures composed of a DNA:RNA hybrid and a displaced single-stranded DNA. They are formed naturally during transcription, DNA replication, and DNA repair. While essential for normal gene regulation, uncontrolled R-loop accumulation disrupts genome stability and has been linked to cancer, neurodegenerative disorders, and autoimmune diseases.

Studying R-loops through R-loop sequencing and analysis provides critical insights into how transcription interfaces with chromatin structure and epigenetic regulation. Genome-wide R-loop mapping enables researchers to:

Identify regulatory hotspots at transcription start and termination sites
Investigate how RNA modifications (e.g., m6A) influence hybrid formation and resolution
Explore connections between R-loop dysregulation, DNA damage, and disease models
Characterise lncRNA- and protein-associated R-loops in functional assays

For researchers in molecular biology, epigenetics, and genome stability, R-loop assays provide a powerful tool to address mechanistic questions and support high-impact discoveries.

Our R-Loop Assay Portfolio

CD Genomics provides multiple R-loop sequencing assays designed for different scientific questions. Each method captures RNA–DNA hybrids from a unique perspective, offering complementary insights.

Assay	Principle	Key Advantages	Best Suited For
DRIP-seq	S9.6 antibody immunoprecipitation of DNA:RNA hybrids followed by DNA library construction	- Genome-wide coverage - High specificity with RNase H control	Constructing global R-loop distribution maps across the genome
DRIPc-seq	S9.6 antibody immunoprecipitation; RNA strand converted to cDNA for sequencing	- Strand-specific information - RNA origin identified (mRNA, lncRNA, circRNA)	Studies on transcriptional regulation and RNA strand directionality
ssDRIP	S9.6 antibody enrichment; ligation-based library of displaced ssDNA	- Detects displaced DNA strand - RNase H validation ensures non-genomic DNA origin	Analysing termination sites, enhancer activity, and displaced DNA features
R-CHIP	Catalytically inactive RNase H1 used as a hybrid recognition protein, combined with ChIP-seq	- High resolution - Protein-guided specificity	Protein–R-loop interaction studies; functional validation
R-loop CUT&Tag	In situ recognition with S9.6 antibody or RNaseH probe, coupled with Tn5 transposase tagmentation	- Requires low input (~50k cells) - High resolution, rapid workflow - No crosslinking or sonication	Rare samples, clinical biopsies, or low-input projects

How to choose the right assay:

Select DRIP-seq for broad, genome-wide mapping.
Use DRIPc-seq when strand-specific RNA origin is critical.
Choose ssDRIP for displaced DNA strand studies.
Apply R-CHIP if you need protein–R-loop interaction insights.
Opt for R-loop CUT&Tag when working with low-input or clinical samples.

Workflow Overview

Our R-loop sequencing service follows a streamlined workflow to ensure accuracy and reproducibility.

Sample Preparation – DNA, RNA, or nuclei extraction; quality control checks

R-loop Enrichment – Capture using S9.6 antibody or RNase H1 probe

Library Construction – DNA, cDNA, or strand-specific protocols depending on assay type

Next-Generation Sequencing – High-throughput Illumina sequencing

Bioinformatics Analysis – Alignment, peak calling, annotation, integrative analysis

R-loop profiling workflow diagram with enrichment methods and Illumina sequencing analysis

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.

This experience ensures that each project is supported by tried-and-tested workflows and tailored analysis strategies.

Sample Requirements

Assay	Recommended Input
DRIP-seq / ssDRIP	≥12–15 µg high-quality genomic DNA fragmented with restriction enzymes
DRIPc-seq	RNA equivalent of ~5×10^6 cells for strand-specific cDNA library
R-CHIP	1–5×10^6 cells expressing inactive RNaseH1; suitable for cultured cells and tissues
R-loop CUT&Tag	0.5–5×10^5 cells; compatible with rare samples and clinical biopsies

Why Choose CD Genomics?

Choosing the right partner is critical for R-loop research. CD Genomics combines technical expertise with proven project experience to deliver reliable, publication-ready results.

Specialised expertise

– Decades of experience in R-loop sequencing, R-loop analysis, and r-loop assays across human, mouse, and plant systems.

Comprehensive assay portfolio

– From DRIP-seq to CUT&Tag, covering genome-wide, strand-specific, and low-input applications.

Stringent quality control

– RNase H validation, replicate concordance, and strand bias checks ensure accuracy and reproducibility.

Multi-omics integration

– Combine R-loop data with RNA-seq, ChIP-seq, ATAC-seq, or RNA modification profiling for deeper insights.

Publication-ready deliverables

– Clean raw data, processed files, interactive reports, and customised figures tailored for journals.

Global client support

– Experienced scientists provide consultation before, during, and after project completion.

With CD Genomics, you gain a partner that understands both the technical complexity of R-loop assays and the practical needs of your research program.

Bioinformatics & Data Analysis

Our R-loop sequencing service includes both standard data processing and advanced integrative analysis. This ensures that every client receives reliable baseline outputs, with the option to extend into deeper biological interpretation.

Basic Analysis

Raw data quality control, including read quality scores, duplication levels, and GC content checks
Adapter trimming and filtering of low-quality reads
Alignment of reads to the reference genome with strand specificity retained
Peak calling to identify DNA:RNA hybrid regions
Generation of basic genome browser tracks (BigWig, BED files) for direct visualisation
Delivery of raw FASTQ, BAM alignment files, and peak annotation results

Advanced Analysis

Strand-specific signal separation for RNA origin (DRIPc-seq) and displaced DNA (ssDRIP)
Quantitative comparison of R-loop profiles across conditions (e.g., knockout vs control, treated vs untreated)
Feature annotation linking R-loops to promoters, enhancers, gene bodies, repeats, and termination sites
Meta-gene profiling and enrichment analysis at transcription start and termination regions
Cross-omics integration with RNA-seq, ChIP-seq, ATAC-seq, or RNA modification datasets (m6A, m5C, etc.)
Statistical summaries, motif discovery, and pathway analysis to connect R-loop changes with biological function
Customised interactive HTML report with publication-ready figures and data interpretation

Demo Results

Genome browser tracks and correlation plots of R-loop profiling with DRIP-seq and DRIPc-seq

To help clients visualise project outcomes, we provide clear, publication-ready result displays. Typical examples include:

Genome browser tracks showing R-loop peaks with RNase H controls
Heatmaps illustrating R-loop enrichment across genes or conditions
Meta-gene profiles highlighting signal distribution at transcription start and termination sites
Comparative plots revealing differences between wild-type and perturbed samples

FAQs

- What is R-loop sequencing used for?
  - R-loop sequencing detects RNA–DNA hybrids to study transcription regulation, genome stability, and epigenetic mechanisms.
- Which assay should I choose?
  - DRIP-seq is best for genome-wide mapping, DRIPc-seq for RNA strand origin, ssDRIP for displaced DNA, R-CHIP for protein-linked studies, and CUT&Tag for low-input samples.
- Can R-loop assays be performed on limited or clinical samples?
  - Yes, CUT&Tag and optimised protocols support as few as 0.5–5×10^5 cells, suitable for rare or clinical materials.
- What sample types are supported?
  - We accept mammalian and plant cells, tissues, blood, organoids, and microorganisms including yeast, fungi, and bacteria.
- How are results delivered?
  - Clients receive FASTQ, BAM, BED, and BigWig files plus an interactive HTML report with publication-ready figures.
- Do you provide integrative analysis with other omics?
  - Yes, we routinely integrate R-loop profiles with RNA-seq, ChIP-seq, ATAC-seq, and RNA modification datasets.
- How do you ensure accuracy and reduce false positives?
  - RNase H–treated controls, replicate concordance, and strand bias analysis are applied to confirm specificity.

References:

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. pii: 6887602. doi: 10.1093/nar/gkac1155.
Sanz LA, Chédin F. High-resolution, strand-specific R-loop mapping via S9.6-based DNA-RNA immunoprecipitation and high-throughput sequencing. Nat Protoc. 2019 Jun;14(6):1734-1755. pii: 10.1038/s41596-019-0159-1. doi: 10.1038/s41596-019-0159-1.