ssDRIP-seq for Genome-Wide R-Loop Profiling

CD Genomics provides ssDRIP-seq services for genome-wide R-loop mapping with strand-level resolution. This advanced method supports researchers in studying transcriptional regulation, chromatin biology, and genome stability with high sensitivity, reproducibility, and minimal background noise.

We address your challenges:

  • Difficulty in distinguishing strand-specific R-loops
  • High background interference in conventional R-loop methods
  • Poor reproducibility in genome-wide R-loop profiling
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Comparison of traditional R-loop mapping vs ssDRIP-seq showing reduced background noise and strand-specific profiling
About R-Loops What Is Workflow Data Analysis Why CD Genomics Demo FAQs Case Study Inquiry

Why R-Loops Matter

R-loops are unique three-stranded nucleic acid structures consisting of an RNA:DNA hybrid and a displaced single-stranded DNA. They are not rare artefacts but widespread features of genomes across bacteria, yeast, plants, and mammals.

These structures typically form in GC-rich, highly transcribed regions, such as promoters or transcription start sites. They are now recognised as important regulators of cellular processes, including:

However, R-loops can be a double-edged sword. While regulatory R-loops contribute to normal gene expression and genome integrity, unscheduled R-loops can drive harmful outcomes, including transcription-replication conflicts, stalled forks, and DNA breaks. Such instability is linked to cancer development, neurological disorders, and impaired cell function.

For these reasons, accurate R-loop mapping and strand-specific profiling have become essential tools for understanding the balance between normal regulatory roles and pathological risks.

What Is ssDRIP-seq and Why It Matters

ssDRIP-seq is an advanced sequencing method for strand-specific R-loop profiling. Unlike earlier approaches, it captures not only the location of R-loops but also the precise strand information of the displaced DNA.

Traditional methods such as DRIP-seq and DRIPc-seq have provided valuable insights but are limited by background noise, random priming artefacts, and lack of strand resolution. ssDRIP-seq overcomes these obstacles by:

This makes ssDRIP-seq especially valuable for researchers investigating:

By offering cleaner, strand-specific, and genome-wide datasets, ssDRIP-seq provides a more complete picture of how R-loops shape gene regulation and genome function.

Workflow and Technical Details

The ssDRIP-seq workflow is designed to deliver strand-specific, high-resolution R-loop maps with minimal background interference. Each step is optimised for accuracy and reproducibility:

DNA extraction – Gentle lysis preserves natural R-loop structures without introducing artefacts.

Fragmentation – Restriction enzymes digest chromatin DNA into suitable fragments for sequencing.

Immunoprecipitation – The S9.6 antibody, which binds RNA:DNA hybrids with high affinity, selectively enriches R-loops.

Adaptor ligation – Single-stranded DNA ends are tagged with adaptors at both the 3′ and 5′ ends, ensuring strand information is preserved.

Library amplification – PCR amplification generates strand-specific sequencing libraries.

High-throughput sequencing – Libraries are sequenced at genome-wide scale to produce strand-specific R-loop profiles.

Quality control checkpoints are applied throughout the process, from sample preparation to sequencing and analysis, ensuring reproducible and publication-ready data.

For complementary approaches, see also: R-ChIP, and R-loop CUT&Tag .

DRIP-seq workflow diagram showing genomic DNA extraction, R-loop immunoprecipitation with S9.6 antibody, adaptor ligation, and library amplification

Bioinformatics & Data Analysis

Our ssDRIP-seq service includes professional bioinformatics support. Results are delivered in standard formats and are ready for direct use in publications or downstream studies.

Basic Analysis

  • Raw data quality control and filtering
  • Sequence alignment to the reference genome
  • Peak calling for enriched R-loop regions
  • Genome annotation of identified peaks

Advanced Analysis

  • Differential R-loop region analysis between experimental conditions
  • Functional enrichment analysis (GO and KEGG pathways)
  • Strand-specific motif discovery for sense and antisense peaks
  • Genome-wide distribution visualisation (chromosome maps, gene region charts, heatmaps)
  • Integrated reporting with publication-ready figures

Why Researchers Partner With Us

One-stop solution

– From sample preparation and library construction to sequencing and bioinformatics analysis, we manage the entire workflow.

Strict quality control

– Rigorous checkpoints at every stage ensure consistency, accuracy, and reproducibility.

Expert bioinformatics support

– Our dedicated team provides detailed reports, including genome-wide maps, peak annotation, motif analysis, and functional enrichment.

Proven track record

– We support leading academic groups, CRO clients, and industry researchers worldwide in uncovering new insights into transcriptional regulation, genome stability, and disease mechanisms.

Sample Requirements

Sample Type Minimum Input Transport Storage
Cells ≥ 2 × 10^7 Ship on dry ice in sealed tubes Store at −80 °C after liquid nitrogen freezing
Tissue ≥ 400 mg Ship on dry ice in sealed tubes Store at −80 °C after liquid nitrogen freezing
Genomic DNA ≥ 10 µg Ship with ice packs Store at −20 °C (short-term) or −80 °C (long-term), avoid freeze–thaw cycles
IP-enriched DNA > 50 ng Ship with ice packs Store at −20 °C (short-term) or −80 °C (long-term), avoid freeze–thaw cycles

All samples should be sealed in 1.5 mL tubes or cryovials to prevent contamination and degradation.

Result Display (Demo Outputs)

To ensure clarity and usability, CD Genomics provides both raw sequencing files and processed datasets with comprehensive visualisation outputs. These results are formatted for direct integration into publications or presentations.

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.

Typical deliverables include:

  • Genome browser tracks – Visualisation of R-loop peaks on sense and antisense strands across the genome
  • Pie charts – Distribution of R-loops across genomic regions (e.g., promoters, exons, introns, intergenic regions)
  • Chromosome maps – Global view of R-loop enrichment patterns across all chromosomes
  • Motif logos – Sequence motifs enriched in strand-specific peaks
  • Venn diagrams – Overlaps of differential R-loop regions between experimental groups
  • Functional enrichment plots – GO and KEGG pathway associations of R-loop regions

FAQs

Case Study: Mapping 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. Mol Cell. 2016;63(1):167–178. doi:10.1016/j.molcel.2016.05.032.

R-loops are RNA:DNA hybrid structures that can influence transcription, chromatin state, and genome stability. Despite their importance, their prevalence, conservation across species, and relationship to chromatin signatures were poorly understood. This study aimed to generate a high-resolution, strand-specific atlas of R-loops in mammalian genomes and link them to functional chromatin states.

The researchers used DRIPc-seq, a strand-specific derivative of DRIP-seq, in human and mouse cells. The method combined S9.6 antibody-based immunoprecipitation with RNA recovery and cDNA conversion, allowing mapping of R-loops with both high resolution and strand specificity. Comparative analyses were performed across multiple cell types and species, including human embryonic carcinoma cells (Ntera2), K562 cells, fibroblasts, and mouse embryonic stem cells. Chromatin accessibility and histone modification datasets (ENCODE) were integrated to correlate R-loop locations with epigenomic states.

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

Conservation: Comparative analysis revealed conserved R-loop formation at promoters and terminators across human and mouse orthologous loci (see Figure 1A

Figure 1. Cluster Analysis of sGB-R-Loops and asTSS-R-Loops during Development.)

Dynamics: Inhibiting transcription initiation (via DRB) showed rapid disappearance of promoter R-loops (half-life ~10 min) and slower turnover of terminator R-loops, supporting their co-transcriptional and dynamic nature.

Chromatin state: R-loops correlated with open chromatin signatures (DNase hypersensitivity, FAIRE signals) and were enriched for active histone marks (H3K4me3, H3K27ac) at promoters, while terminal R-loops overlapped with enhancer- and insulator-like states (see Figure 2 and Figure 3).

Figure 2. Uncoupling between R-Loop Dynamics and RNA Expression Changes.

Figure 3. Regulatory Network and Potential Roles of R-Loops in Regulating Gene Expression.

This study demonstrated that R-loops are:

  • Prevalent and conserved across mammalian species
  • Dynamic structures formed co-transcriptionally and resolved within minutes
  • Associated with distinct chromatin signatures, acting as regulators of promoter activity and transcription termination

These findings position R-loops as integral regulators of gene expression and chromatin organisation, not just accidental by-products.

References:

  1. Xu W, Liu X, Li J, Sun C, Chen L, Zhou J, Li K, Li Q, Meng A, Sun Q. ULI-ssDRIP-seq revealed R-loop dynamics during vertebrate early embryogenesis. Cell Insight. 2024 Jun 1;3(4):100179. doi: 10.1016/j.cellin.2024.100179. PMID: 38974143; PMCID: PMC11225018.
  2. Sun C, Wang Z, Li Q, Sun Q, Xu W. mDRIP-seq is a high-throughput method for quantitative profiling of R-loop landscape. Sci Bull (Beijing). 2025 Jan 15;70(1):38-41. doi: 10.1016/j.scib.2024.05.023. Epub 2024 May 19. PMID: 38821747.
  3. Xu W, Li K, Li Q, Li S, Zhou J, Sun Q. Quantitative, Convenient, and Efficient Genome-Wide R-Loop Profiling by ssDRIP-Seq in Multiple Organisms. Methods Mol Biol. 2022;2528:445-464. doi: 10.1007/978-1-0716-2477-7_29. PMID: 35704209.


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