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dsRNA RIP-Seq Service for Sensitive Double-Stranded RNA Profiling

CD Genomics offers dsRNA RIP-Seq (double-stranded RNA immunoprecipitation sequencing) to sensitively capture and profile structured RNAs from endogenous and exogenous sources. Our dsRNA RIP-Seq workflow enriches double-stranded RNA and delivers strand-specific, molecular barcodes-based quantification for confident results. Compared with standard RIP-Seq, dsRIP-Seq pinpoints low-abundance duplexes linked to innate immunity, viral replication, RNA processing, and IVT mRNA quality.

We solve these problems:

  • Low sensitivity for rare, immunogenic dsRNA species
  • Poor resolution of repeat-derived and mitochondrial dsRNA
  • Limited visibility of dsRNA contaminants in IVT mRNA

dsRNA RIP-Seq: sensitive double-stranded RNA sequencing

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dsRNA RIP-Seq wplatform
  • Molecular barcodes enable accurate quantification
  • Strand-specific mapping of sense/antisense origins
  • · Endogenous: LINE/SINE/ERV and mitochondrial dsRNA
  • Exogenous: viral dsRNA and IVT by-products
  • Full RIP-Seq workflow with publication-ready analytics
Why dsRNA RIP-Seq Advantages Workflow Data Analysis Applications Deliverables Requirements Why CD Genomics FAQ Case Study Inquiry

Why Study dsRNA with RIP-Seq?

Double-stranded RNA (dsRNA) is more than a viral signature. It is now recognised as a regulatory signal and immune activator in mammalian cells. Endogenous dsRNAs arise from transposable elements, antisense transcription, and mitochondrial transcripts, while exogenous dsRNAs are generated during viral replication or as by-products of in vitro transcription.

The challenge: Standard RNA-Seq under-represents duplexed RNA molecules. Many structured RNAs escape detection because conventional sequencing methods are optimised for single-stranded RNA. This limitation makes it difficult to study dsRNA-driven pathways in immunology, oncology, virology, and RNA therapeutics.

The solution: dsRNA RIP-Seq specifically enriches double-stranded RNAs using dsRNA antibodies, followed by high-throughput sequencing. This approach ensures sensitive detection of low-abundance dsRNA species, enabling researchers to uncover their contribution to innate immunity, antiviral signalling, and cellular stress responses.

With dsRIP-Seq, researchers can confidently address questions such as:

Advantages of dsRNA RIP-Seq

dsRNA RIP-Seq delivers unique benefits over conventional RNA analysis methods by combining immunoprecipitation with next-generation sequencing.

Key Advantages

High specificity – dsRNA antibodies selectively capture duplex RNA, avoiding rRNA and most ssRNA.

High sensitivity – Detects low-abundance dsRNA that standard RNA-Seq underestimates.

Molecular barcodes – Reduce amplification bias and ensure reliable quantification.

Strand-specific libraries – Differentiate sense and antisense strands, enabling precise mapping of dsRNA origins.

Comprehensive bioinformatics – Integrates dsRNA annotation, repeat element analysis, and immune pathway enrichment.

What This Means for Your Research

Oncology and epigenetics – Characterise dsRNA accumulation after DNA methyltransferase inhibitors or splicing modulators.

Virology – Identify replication intermediates and host–virus dsRNA signatures.

RNA therapeutics – Detect dsRNA by-products in IVT mRNA production to de-risk immunogenicity.

Immunology – Profile dsRNAs that activate RIG-I, MDA5, PKR, and OAS/RNase L pathways.

How dsRNA RIP-Seq Works

Our dsRNA RIP-Seq workflow combines antibody-based enrichment with high-throughput sequencing, ensuring sensitive detection and robust analysis of double-stranded RNAs.

Sample preparation and QC

  • Cells, tissues, or RNA extracts are assessed for quality and integrity.
  • RNA integrity is verified by Bioanalyzer or equivalent assays.

dsRNA immunoprecipitation

  • Anti-dsRNA antibodies (e.g., J2) capture duplex RNA molecules >40 bp.
  • Magnetic or agarose beads isolate antibody–dsRNA complexes.
  • Negative controls (isotype antibody) ensure specificity.

RNA purification

  • Bound dsRNAs are eluted under low-pH or competitive conditions.
  • Quality checks confirm recovery and purity.

Library preparation

  • Incorporation of molecular barcodes eliminates PCR bias.
  • Strand-specific adapters preserve directional information.
  • rRNA depletion is included when needed.

High-throughput sequencing

  • Paired-end sequencing provides depth and accuracy for dsRNA analysis.

Bioinformatics analysis

  • Raw reads undergo filtering and mapping to reference genomes.
  • Annotation includes repeat elements (LINEs, SINEs, LTRs, ERVs).
  • Outputs include differential dsRNA detection, pathway enrichment, and IGV-ready genome tracks.

dsRNA RIP-Seq workflow

Data Analysis Overview

The following analyses are included with each dsRNA RIP-Seq project. Customised bioinformatics is available upon request.

Analysis Item Description
Raw data processing Filtering and quality assessment; removal of low-quality reads.
dsRNA identification & annotation Genome-wide mapping and classification of enriched dsRNAs.
Differential dsRNA detection Identification of differentially enriched dsRNAs between groups.
GO functional analysis Functional enrichment of dsRNA-associated genes (biological process, cellular component, molecular function).
KEGG pathway analysis Pathway enrichment of dsRNA-associated genes.
Venn diagram Visualisation of shared and unique dsRNAs across sample groups.
Differential visualisation Clustering heatmaps, scatter plots, and volcano plots.
Length distribution Violin plots showing dsRNA length distribution in dsRIP vs. control samples.
Genome browser tracks IGV-ready files for direct visualisation of dsRNA enrichment regions.
Customised analysis Project-specific bioinformatics pipelines tailored to client needs.

Applications of dsRNA RIP-Seq

dsRNA RIP-Seq extends beyond structural RNA discovery. By specifically enriching double-stranded RNAs, it provides insight into how these molecules regulate cellular processes and trigger immune pathways.

Gene Regulation Studies

  • Identify antisense RNA–dsRNA hybrids that influence transcriptional control.
  • Investigate RNA processing errors, such as intron retention or alternative splicing, that generate dsRNA.

Cancer Biology and Epigenetics

  • Detect dsRNA accumulation following treatment with DNA methyltransferase inhibitors or splicing modulators.
  • Correlate repeat-derived dsRNAs with type I interferon activation and tumour immune responses.

Virology and Host–Virus Interactions

  • Profile viral dsRNA replication intermediates.
  • Understand how host dsRNA-binding proteins recognise viral infection.

RNA Therapeutics and Vaccines

  • Characterise dsRNA by-products in in vitro transcribed (IVT) mRNAs.
  • Evaluate how dsRNA contaminants contribute to unwanted immune stimulation.

Agricultural Biotechnology

  • Assess dsRNA molecules used in RNAi-based pest control strategies.
  • Improve safety and efficiency of dsRNA applications in crops.

Deliverables & Demo

GO enrichment bar plot and KEGG pathway bubble plot of differentially enriched dsRNA-associated genes

Venn diagram showing unique and shared dsRNAs between sample groups

Heatmap, scatter plot, and volcano plot visualizing differential dsRNA enrichment between samples

Violin plot comparing transcript length distributions of invariant and dsRIP-enriched dsRNAs

With every dsRIP-Seq project, CD Genomics provides:

  • Genome-wide dsRNA enrichment profiles with peak annotations
  • Repeat element and mitochondrial dsRNA classification
  • Gene Ontology (GO) and KEGG pathway enrichment analysis
  • High-resolution visualisation files (genome browser tracks, volcano/heatmaps)
  • Publication-ready figures and summary reports
  • Raw and processed sequencing data (FASTQ, BAM, annotation tables)

Sample Requirements

To ensure high-quality results, please follow these input requirements for dsRNA RIP-Seq projects.

Sample Type Minimum Input Quality Requirements Preservation & Transport
Cells ≥ 2 × 10^7 cells Healthy, log-phase cells preferred. No mycoplasma contamination. Pellet in RNase-free tubes, snap-freeze in liquid nitrogen, ship on dry ice.
Tissues ≥ 100 mg Fresh or frozen tissue with intact RNA. Cut to small pieces, preserve in RNA stabilisation reagent or snap-freeze, store at -80 °C, ship on dry ice.
Whole Blood ≥ 5 mL (EDTA anticoagulant tubes) Avoid heparin anticoagulants (PCR inhibitors). Store at -80 °C, ship on dry ice.
Total RNA ≥ 2 µg OD260/280 ≥ 1.8; OD260/230 ≥ 1.5; clear rRNA bands; no visible degradation or smear. Store at -80 °C, avoid repeated freeze–thaw cycles, ship on dry ice.

Notes:

Why Choose CD Genomics?

Proven expertise – Over a decade of
experience in RNA immunoprecipitation and dsRNA sequencing.

High reproducibility – Validated
antibody-based enrichment and commercial-grade RIP kits ensure consistent results.

Comprehensive QC – Strict quality control checkpoints from sample receipt to final analysis.

Advanced bioinformatics – Repeat-aware annotation, pathway
enrichment, and publication-ready figures.

One-stop service – End-to-end workflow covering sample preparation, sequencing, and data interpretation.

Frequently Asked Questions

Case Study: Optimizing dsRNA Sequences for RNAi-based Pest Control

Source: Doga Cedden, Gözde Güney, Michael Rostás & Gregor Bücher (2025). Optimizing dsRNA sequences for RNAi in pest control and research with the dsRIP web platform. BMC Biology 23:114.

RNA interference (RNAi) is widely used for functional genomics and is an emerging eco-friendly alternative to chemical pesticides. Its success depends on the delivery of double-stranded RNA (dsRNA), which is processed into small interfering RNAs (siRNAs) that silence essential target genes. While design rules for siRNA are well studied in humans, their direct application to insects is limited. The study by Cedden et al. addressed this gap by systematically testing insect-specific siRNA features to improve dsRNA design for pest control.

Researchers tested 31 siRNAs inserted into a non-targeting dsRNA backbone and delivered them into Tribolium castaneum larvae. They analyzed larval survival outcomes and mapped RISC-bound siRNAs through small RNA sequencing. Predictive models including thermodynamic asymmetry, GC content, and sequence motifs were evaluated. In parallel, the team designed and compared full-length dsRNAs optimized with their new algorithm (dsRIP) across multiple essential insect genes and pest species.

  • Key Features Identified: Insecticidal efficacy correlated with siRNA thermodynamic asymmetry, absence of secondary structures, adenine at the 10th antisense position, and high GC content (9–14 nt).
  • Cross-Species Validation: Optimized dsRNAs showed higher lethality in T. castaneum, Psylliodes chrysocephala, and Leptinotarsa decemlineata.
  • Mechanistic Insight: RISC-bound sRNA-seq revealed optimized dsRNAs produced significantly more antisense siRNAs, explaining higher knockdown efficiency.
  • Practical Impact: dsRIP-designed dsRNAs outperformed commercial products such as Ledprona at lower concentrations, offering higher mortality and feeding inhibition.

Kaplan-Meier survival curves comparing dsRIP-optimized dsRNA with commercial and traditional designs in leaf beetle pest species Survival analysis of Psylliodes chrysocephala and Leptinotarsa decemlineata fed with dsRIP-optimized dsRNAs shows significantly higher insecticidal efficacy compared to previous or commercial dsRNA designs.

Cedden et al. demonstrated that dsRNA design rules derived from human systems are not fully transferable to insects. By experimentally identifying insect-specific sequence features and validating them across species, the authors established dsRIP, a web platform integrating target gene selection, dsRNA optimization, and off-target minimization. This resource improves both research applications and practical pest management, advancing the deployment of RNAi as a sustainable, species-specific pest control tool.

References:

  1. Gao Y, Chen S, Halene S, Tebaldi T. Transcriptome-wide quantification of double-stranded RNAs in live mouse tissues by dsRIP-Seq. STAR Protoc. 2021 Mar 18;2(1):100366. doi: 10.1016/j.xpro.2021.100366. PMID: 33778776; PMCID: PMC7982789.
  2. Jiang, N., Yang, H., Lei, Y. et al. Characterization of dsRNA binding proteins through solubility analysis identifies ZNF385A as a dsRNA homeostasis regulator. Nat Commun 16, 3433 (2025).
  3. Nguyen MU, Potgieter S, Huang W, Pfeffer J, Woo S, Zhao C, Lawlor M, Yang R, Halstead A, Dent S, Sáenz JB, Zheng H, Yuan ZF, Sidoli S, Ellison CE, Verzi M. KAT2 paralogs prevent dsRNA accumulation and interferon signaling to maintain intestinal stem cells. bioRxiv [Preprint]. 2023 Sep 5:2023.09.04.556156.


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