Strand-specific ssDRIP-Seq that turns complex R-loop biology into decision-ready evidence. We deliver validated, genome-wide maps and clear comparative insights—so you can locate, quantify, and interpret R-loops with confidence.
ssDRIP-Seq (strand-specific DNA:RNA ImmunoPrecipitation sequencing) is a genome-wide method for mapping R-loops—three-stranded nucleic acid structures formed when nascent RNA hybridizes to the DNA template strand, displacing the non-template strand. By enriching native RNA:DNA hybrids with the S9.6 antibody and preserving read orientation through strand-aware sequencing, ssDRIP-Seq localizes R-loops with template/non-template resolution and distinguishes true hybrid signals via RNase H–based specificity controls. The result is a quantitative, strand-resolved landscape of R-loops that can be directly integrated with transcription, chromatin, and replication features to interpret regulatory mechanisms and genome stability.
Strand-Resolved Mapping — Directional Library Preparation
Preserves strand orientation, enabling unambiguous assignment of R-loops to template or non-template strands with >95% strand specificity.
Hybrid-Dependent Validation — RNase H Controls
Paired negative controls confirm true RNA:DNA hybrids, typically showing >80% depletion of signal at positive loci.
Optimized Fragmentation — Enzyme-Based Digest
Generates ~300–500 bp fragments while preserving hybrid integrity, improving resolution and reproducibility compared with sonication.
Quantitative Reproducibility — Spike-In Standards
Optional exogenous spike-ins normalize across batches, delivering CV <15% for replicate comparisons in differential R-loop analysis.
High-Resolution Sequencing — Deep Coverage
Standard depth of 30–60 million paired-end reads for mammalian genomes captures both strong and subtle R-loop signals with confidence.
Rigorous Quality Control — Transparent Reporting
Comprehensive QC metrics (FRiP, replicate correlation, depletion efficiency) provided; biological replicates often achieve r ≥0.9.
Use when you need to: see where gene control is happening around promoters/enhancers/terminators.
Typical questions: "Which regions drive or block my gene?" "Is directionality an issue?"
Popular options: Strand-specific, Multi-omics add-on.
Use when you need to: check if a treatment triggers replication stress or fragile sites.
Typical questions: "Does this compound create conflicts at replication forks?" "Where are the hotspots?"
Popular options: Time-course / dose series.
Use when you need to: understand how a compound or edit shifts genome-wide R-loop patterns.
Typical questions: "What's the mechanism?" "Which loci respond most?"
Popular options: Parallel groups with batch control; Targeted capture for key loci.
Use when you need to: diagnose read-through/backtracking in termination zones or mega-genes.
Typical questions: "Why do transcripts fail to stop?" "Is gene length a factor?"
Popular options: Strand-specific; refined termination annotations.
Use when you need to: link patient/engineered variants to localized R-loop changes.
Typical questions: "Do mutations shift the landscape?" "Which pathways are implicated?"
Popular options: Low-input workflow; variant/phenotype context.
Use when you need to: follow R-loop dynamics across stages or lineage commitment.
Typical questions: "Where are the turning points?" "Which modules change over time?"
Popular options: Multi-timepoint design; optional RNA-seq/ATAC-seq.
Use when you need to: map confidently in high-repeat or GC-skewed regions.
Typical questions: "Can we get reliable calls in tricky regions?"
Popular options: Custom alignment/filters; region-focused capture.
Use when you need to: zoom in on predefined pathways or candidate mega-genes at high depth.
Typical questions: "What happens in this pathway under treatment?" "Which sites should we validate?"
Popular options: Probe-based capture + ssDRIP.
Project scoping: Objectives, species/genome build, comparisons, and depth per sample.
Sample QC: Concentration, purity (A260/280, A260/230), integrity of HMW DNA; acceptance criteria verified before proceeding.
Native fragmentation: Optimized restriction enzyme mix to preserve RNA:DNA hybrids and yield suitable fragment sizes.
Immunoprecipitation: S9.6 IP under hybrid-preserving buffers; in-parallel RNase H–treated negative control.
Library construction (stranded) & sequencing: Directional paired-end libraries; depth scaled to genome size and study aims.
Bioinformatics & statistics: QC metrics, strand-aware peak calling, differential analysis, motif/feature association, pathway enrichment.
Delivery & review: Data package (FASTQ/BAM/BigWig/peak BED), figures/tables, and consultative results walk-through.
Sequencing Platforms & Modes — Illumina
NovaSeq/NextSeq paired-end runs (2×75 bp or 2×150 bp). Depth guidelines:
Run Configuration — Throughput & Consistency
High-throughput flow cells with balanced multiplexing for lane-to-lane consistency; coverage evenness is evaluated across libraries to ensure robust peak detection and strand-resolved signal.
Quality Control (QC) — Acceptance Targets
Illumina NovaSeq 6000
Illumina NextSeq 2000
| Sample Type | Amount (Recommended) | Quality / Integrity | Buffer & Container | Storage & Shipping | Notes |
| Cell pellet | ≥5–10 million cells | High-integrity nuclei/DNA; minimal degradation | RNase-free tube; PBS (no chelators) | Frozen on dry ice | Avoid RNase contamination; provide cell type and culture conditions. |
| Tissue | ≥50–100 mg (species-dependent) | HMW DNA recoverable; no crosslinking | RNase-free cryovial | Snap-frozen; dry ice | Provide tissue origin, collection method, and any treatments. |
| Purified genomic DNA (preferred for many designs) | ≥3–5 µg total; ≥100 ng/µL | A260/280 ~1.8–2.0; A260/230 ≥2.0; HMW (>50 kb prior to digest) | TE or low-EDTA buffer; RNase-free | Cold packs or dry ice | No phenol/carryover; avoid heparin/EDTA excess and detergents. |
| Microbial/yeast DNA | ≥1–2 µg total | Purity as above; confirm genome size/strain | TE; RNase-free | Cold packs or dry ice | Provide reference genome/assembly for alignment. |
| Optional spike-ins | By consultation | Defined hybrid standards or exogenous DNA | Provided kit or supplied by client | As specified | Enables cross-batch normalization and calibration. |
If your material differs from the above, we will tailor acceptance criteria during project scoping.
RNase H Specificity Validation (Bar Chart)
Enrichment levels at representative R-loop–positive loci, showing strong signals in untreated samples and near-complete depletion after RNase H treatment, confirming signal specificity.
Genome Browser Tracks (Strand-Specific BigWig)
Strand-specific R-loop coverage tracks across a genomic region, displaying distinct enrichment patterns on the plus and minus strands aligned with annotated gene features.
Peak Calling & Signal Enrichment Profile (Metagene Plot)
Average R-loop signal distribution across transcriptional features, with enrichment observed from transcription start sites (TSS) into the gene body and tapering near termination sites (TTS).
Differential R-loop Analysis (Volcano Plot)
Differential R-loop profiling between two conditions (e.g., wild type vs. mutant), highlighting significantly enriched or depleted regions.
| Method | ssDRIP-Seq | DRIP-Seq (classic) | DRIPc-Seq | RNase H1–based capture (e.g., R-ChIP/MapR) |
| What it captures | DNA:RNA hybrids with DNA context | DNA:RNA hybrids, no strand | RNA within hybrids | Hybrids via dRNase H1 binding |
| Strand specificity | Yes (template vs non-template) | No | N/A (RNA reads) | Often orientation-aware/targeted |
| Best for | Genome-wide strand-resolved mapping; promoter/terminator analysis; condition/genotype contrasts | Broad localization when strand is not required | Linking R-loops to transcription output | Focused interrogation of regulatory elements with high specificity |
| Quantitative comparisons | Strong (differential analysis with RNase H controls, replicates) | Moderate (relative) | Good (expression-coupled changes) | Good in targeted designs |
| Input tolerance | Moderate; native high-quality gDNA/cells | Moderate; native DNA | RNA component captured; needs robust RNA | Depends on expression of fusion/assay setup |
| Interpretability | High—strand orientation + DNA features | Medium—no strand disambiguation | High for RNA perspective; pair with DNA tracks | High at targeted loci; limited genome-wide scope |
| Controls & validation | Built-in RNase H negative control recommended | RNase H recommended | RNase H recommended | Binding/overexpression controls required |
| Typical deliverables | FASTQ, BAM/BAI, strand-specific BigWig, peak BED, differential tables, QC & summary | FASTQ, BAM/BAI, BigWig (no strand), peak BED, QC | FASTQ (RNA), expression-linked hybrid signals, QC | Targeted peak sets, browser tracks, QC |
| Key limitations | Requires careful native prep; standard Illumina depth | Lacks strand resolution | Loses direct DNA-strand context | Potential binding/overexpression bias; protocol complexity |
What scientific question is ssDRIP-Seq best at answering?
Strand-resolved, genome-wide mapping of R-loops with template vs. non-template orientation for mechanistic insights and condition/genotype contrasts.
How does ssDRIP-Seq differ from DRIP-Seq and DRIPc-Seq?
ssDRIP-Seq adds strand orientation in DNA context; DRIP-Seq lacks strand resolution; DRIPc-Seq sequences the RNA component and should be integrated with DNA tracks.
How is specificity verified?
Matched RNase H–treated material is processed in parallel; genuine hybrid signals show strong depletion at positive loci.
What are the recommended sequencing parameters?
Illumina paired-end (2×75 or 2×150); typical depth guidelines: ~30–60 M PE reads for large eukaryotes, ~5–15 M for small genomes.
What are the key QC acceptance targets?
Base quality Q30 ≥ 85%, alignment rate ≥ 80%, insert size ~300–500 bp, informative FRiP, high replicate correlation (Pearson/Spearman ≥ 0.9).
What sample types are accepted?
Cell pellets, tissues, or purified high-molecular-weight genomic DNA; RNase-free handling and acceptable purity (A260/280, A260/230) are required.
How much input do I need?
Low-to-moderate input is supported; we advise gDNA with good integrity and purity for reliable hybrid preservation (exact amounts confirmed at scoping).
What deliverables will I receive?
FASTQ, BAM/BAI, strand-specific BigWig (±), peak BED (with scores/FDR/strand), QC report, differential analysis tables, IGV session, and an executive summary.
Can you run differential R-loop analysis across conditions?
Yes—strand-aware peak sets with effect sizes and multiple-testing control, plus pathway/gene-set enrichment on associated genes.
Can you integrate with my existing omics data?
Yes—co-analysis with RNA-seq, ATAC-seq, ChIP-seq, and replication timing to link hybrids with expression, accessibility, and chromatin marks.
How do you minimize false positives?
Native fragmentation preserves hybrids, RNase H controls confirm dependency, and replicate/IDR criteria stabilize peak calls.
Is ssDRIP-Seq suitable for low-input or precious samples?
Feasible with careful optimization; for ultra-low input we may recommend alternative hybrid-targeting assays after scoping.
What genomes/species are supported?
Mammalian, plant, yeast, and microbial genomes; depth and parameters are right-sized to genome size and complexity.
What exactly is an R-loop and why map it?
An R-loop is an RNA:DNA hybrid plus a displaced DNA strand; mapping reveals regulation, replication conflicts, and genome stability features.
Why is strand specificity important?
It disambiguates template vs. non-template signals, resolving antisense activity and promoter/terminator dynamics.
Do I need RNase H controls?
Strongly recommended to prove hybrid dependency and improve interpretability in reviews and publications.
How many replicates should I plan?
Biological replicates are advised to ensure statistical power and reproducibility; we tailor recommendations during scoping.
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