What is the difference between RAP-Seq and RAP-MS?

RAP-Seq and RAP-MS share the same core capture methodology. RAP-Seq sequences captured DNA (RAP-DNA) or RNA (RAP-RNA) for chromatin binding sites and RNA interaction partners, while RAP-MS identifies captured proteins by mass spectrometry.

What types of RNA can be studied by RAP-Seq?

RAP-Seq is most commonly applied to nuclear lncRNAs that interact with chromatin, such as Xist, MALAT1, NEAT1, HOTAIR, and NORAD. It can also be applied to enhancer RNAs, promoter-associated RNAs, and snRNAs.

What controls are essential for RAP-Seq?

Essential controls include scramble probe control, input chromatin/RNA, no-probe beads-only control, and RNase-treated control. For RAP-RNA, no-crosslink control is also recommended.

How many cells are needed for RAP-Seq?

A standard RAP-Seq experiment requires ≥2 × 10⁷ cells per capture, with even/odd probe pools plus controls requiring ≥6 × 10⁷ cells total. More cells may be needed for low-expressed lncRNAs.

Can RAP-Seq be combined with other methods?

Yes, RAP-Seq can be combined with RAP-MS for dual DNA/protein readout, RNA-seq for functional validation, RIC-seq for global RNA-RNA interactome mapping, and RADICL-seq for unbiased RNA-chromatin interaction profiling.

RAP-Seq Service | RNA Antisense Purification Sequencing for LncRNA-Chromatin Interactions

RAP-Seq Overview

RNA Antisense Purification followed by high-throughput sequencing (RAP-Seq) is a powerful method for mapping the molecular interactions of a target RNA — most commonly a lncRNA — at genome-wide scale. The method uses biotinylated antisense DNA probes (typically 120 nucleotides) tiled across the entire target RNA to capture it from crosslinked cellular lysate, along with its bound DNA, RNA, and protein partners. The enriched nucleic acids are then eluted and analyzed by high-throughput sequencing to identify interaction sites.

RAP-Seq encompasses two complementary readout strategies: RAP-DNA identifies genomic DNA loci bound by the target RNA, providing a genome-wide chromatin interaction map; RAP-RNA identifies RNA molecules that base-pair with or are indirectly associated with the target RNA, revealing its RNA-RNA interactome. When combined with control probes (e.g., scrambled sequence or input normalization) and appropriate crosslinking conditions, RAP-Seq delivers high-confidence interaction maps that distinguish direct from indirect contacts.

RAP-Seq is closely related to other antisense capture methods including ChIRP-MS and RAP-MS, but differs in its sequencing-based readout. Our service integrates seamlessly with complementary approaches such as RNA Interaction analysis, RIC-seq, and RADICL-seq for comprehensive RNA-centric interaction profiling.

Feature	RAP-Seq	ChIRP-Seq	CHART-Seq
Probe Length	120 nt long ssDNA probes	~20 nt short oligos	~25 nt short oligos
Probe Tiling	Tiled every ~15 nt across full RNA	Tiled every ~100 bp across full RNA	Targets RNase H-accessible regions only
Probe Design Requirement	No prior knowledge of binding domains or accessible regions needed	No prior knowledge needed	Requires RNase H mapping to identify accessible regions
Background Noise	Low — long probes provide high binding affinity and specificity	Moderate — shorter probes may have higher off-target binding	Low — but requires pre-selection of accessible regions
Crosslinking	FA, DSG+FA, AMT, or UV	GA or FA	FA
Readout	DNA-seq (RAP-DNA) + RNA-seq (RAP-RNA) from same capture	DNA-seq (ChIRP-seq); separate MS for proteins (ChIRP-MS)	DNA-seq (CHART-seq)
Controls	Scramble probe, input, no-crosslink, RNase-treated	Scramble probe, input, RNase A+H treatment	Scramble probe, input, RNase H treatment
Structured RNA Capture	Robust — long probes overcome secondary structure	Moderate — shorter probes may fail at structured regions	Limited — only targets accessible regions
Low-Abundance RNA	Good — long probes increase capture efficiency	Moderate	Moderate

Technical Advantages of Our RAP-Seq Service

Long Tiled Probe Design for Robust Capture

Our probe design pipeline generates 120-nt biotinylated ssDNA probes tiled every ~15 nt across the full-length target RNA sequence. This tiling strategy ensures efficient capture even in the presence of extensive RNA secondary structure, protein-RNA interactions, or partial RNA degradation — all common challenges with lncRNAs. The long probes provide higher binding affinity and lower background noise compared with short-oligo methods. Each probe set is computationally filtered against the reference genome to remove cross-reactive sequences and optimized for GC content (40–60%). Even/odd probe pool splitting enables independent biological replicate validation.

Dual RAP-DNA and RAP-RNA Readout from a Single Capture

RAP-Seq uniquely enables parallel sequencing of both the bound DNA (RAP-DNA) and co-purified RNA (RAP-RNA) fractions from the same capture experiment. This dual-readout capability provides a comprehensive view of lncRNA function: RAP-DNA reveals the genomic loci where the lncRNA is localized (chromatin binding sites), while RAP-RNA identifies RNA molecules that directly or indirectly interact with the target. Integrated analysis of both datasets enables researchers to distinguish between lncRNAs that act as chromatin guides, RNA sponges, or scaffold molecules.

Versatile Crosslinking Strategies for Interaction Specificity

Different crosslinking chemistries capture distinct classes of molecular interactions. Our service offers multiple crosslinking options: formaldehyde (FA) crosslinks RNA-protein and protein-DNA contacts, capturing both direct and indirect RNA-chromatin interactions; DSG+FA dual crosslinking enhances capture of protein-mediated RNA-chromatin contacts; AMT (4'-aminomethyltrioxsalen) specifically crosslinks directly base-paired RNA-RNA duplexes without protein crosslinking. This modular crosslinking approach allows researchers to dissect direct from indirect interactions and build mechanistic models of lncRNA function.

Bioinformatics and Data Analysis

Our bioinformatics pipeline for RAP-Seq data processing is designed to deliver high-confidence interaction sites with comprehensive functional annotation. The pipeline handles both RAP-DNA and RAP-RNA data streams, with tailored analytical modules for each readout type.

Analysis Package	Content Description
Standard Analysis
1. Raw Data QC and Preprocessing	FastQC quality assessment, adapter trimming (Cutadapt), read filtering for quality (Q ≥ 30), PCR duplicate removal. Alignment statistics including mapping rate, duplication rate, and library complexity metrics.
2. Read Alignment	RAP-DNA: alignment to reference genome (Bowtie2 / BWA-MEM). RAP-RNA: alignment to reference transcriptome and genome (STAR). Spike-in normalization where applicable. Multi-mapping read resolution.
3. Peak Calling (RAP-DNA)	Identification of significantly enriched genomic intervals (MACS2, SICER, or custom peak caller). Input normalization and scramble probe subtraction. Irreproducible Discovery Rate (IDR) analysis across even/odd probe replicates. Peak annotation to genomic features (promoters, gene bodies, enhancers, intergenic).
4. Target RNA Identification (RAP-RNA)	Transcript quantification and identification of enriched RNAs compared to scramble control. Identification of specific RNA-RNA interaction partners including mRNA, lncRNA, snoRNA, snRNA targets. Base-pairing prediction for directly crosslinked RNAs (AMT datasets).
5. Genomic Feature and Functional Annotation	Peak-to-gene assignment, GREAT analysis for biological process enrichment, KEGG and Reactome pathway analysis. Chromatin state enrichment analysis using Roadmap Epigenomics or ENCODE reference epigenomes.
Advanced Analysis
6. Motif and Sequence Analysis	De novo motif discovery (MEME, HOMER) at RAP-DNA peak regions. Sequence composition analysis, GC content profiling, and repeat element enrichment. Transcription factor binding motif enrichment for mechanistic inference.
7. Comparative and Differential Binding Analysis	Differential peak analysis between conditions (e.g., differentiation time points, disease vs. control, knockdown vs. WT). Quantitative comparison of RAP-DNA enrichment across experimental groups using DiffBind or similar.
8. Multi-Omics Integration	Integration with RNA-seq, ChIP-seq, ATAC-seq, and Hi-C data to contextualize lncRNA binding sites within the chromatin landscape. Correlation analysis with gene expression changes upon lncRNA perturbation.
9. Network and Functional Inference	Construction of lncRNA-centric interaction networks integrating DNA binding sites, RNA interaction partners, and protein interaction data. Functional enrichment of target genes and pathway over-representation analysis.

Our analysis team provides a comprehensive report with publication-ready figures including genome browser tracks, peak heatmaps, motif logos, functional enrichment plots, and integrated network visualizations. Data are delivered in standard formats (BAM, bigWig, BED, peak BED, interaction tables) compatible with downstream analysis tools.

Applications

RAP-Seq has broad applications across lncRNA biology, chromatin regulation, and gene expression control. The following application areas are particularly well-suited to our approach.

LncRNA-Chromatin Interaction Mapping

RAP-DNA is the method of choice for identifying the genomic binding sites of chromatin-associated lncRNAs. Whether studying X-chromosome inactivation (Xist), imprinting control (Kcnq1ot1, Airn), enhancer-associated lncRNAs, or chromatin looping regulators (Firre), RAP-Seq provides genome-wide maps of lncRNA occupancy at high resolution. This application is foundational for understanding how lncRNAs contribute to gene silencing, activation, and three-dimensional genome organization.

RNA-RNA Interactome Discovery

RAP-RNA enables systematic identification of RNA molecules that interact with a target lncRNA, including mRNA, other lncRNAs, small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), and repetitive RNA elements. This application has revealed that many lncRNAs interact with nascent pre-mRNAs to localize to active chromatin, and that RNA-RNA base-pairing can direct lncRNAs to specific genomic regions. AMT-crosslinked RAP-RNA specifically captures directly base-paired interactions.

LncRNA Functional Characterization

Integrating RAP-DNA binding maps with transcriptomic data (RNA-seq after lncRNA knockdown or knockout) provides mechanistic insights into lncRNA function. Genes proximal to lncRNA binding sites that change expression upon lncRNA perturbation represent likely direct targets. Combined with motif analysis and chromatin state data, RAP-Seq can distinguish between lncRNAs that act as guides (recruiting chromatin modifiers to specific loci), scaffolds (bridging proteins and chromatin), or decoys (sequestering proteins from chromatin).

Nuclear RNA Localization and Dynamics

RAP-Seq can be applied across different cellular states — differentiation time courses, disease models, or drug treatments — to study how lncRNA chromatin localization changes dynamically. Comparative RAP-DNA analysis between conditions reveals condition-specific binding sites and identifies genomic regions where lncRNA occupancy correlates with transcriptional changes. Spatial genomic context analysis (through integration with Hi-C or similar data) can reveal whether lncRNAs bind preferentially at topologically associating domain (TAD) boundaries, active/inactive compartments, or specific chromatin states.

Mechanistic Studies of LncRNA in Development and Disease

Many disease-associated lncRNAs exert their effects through chromatin binding. RAP-Seq can identify the genomic targets of lncRNAs implicated in cancer (MALAT1, HOTAIR, NEAT1, NORAD, PVT1), cardiovascular disease, neurological disorders, and developmental defects. By mapping lncRNA binding sites in disease-relevant cell types and comparing with disease-associated genetic variants, researchers can connect lncRNA dysfunction to specific gene regulatory circuits. The combination of RAP-DNA and RAP-RNA data provides a comprehensive mechanistic view of lncRNA function in disease contexts.

Sample Requirements

Sample Type	Recommended Amount	Notes
Adherent cells	≥2 × 10⁷ cells per capture	2–3 biological replicates recommended; 1× T175 flask typically yields ~1 × 10⁷ cells
Suspension cells	≥2 × 10⁷ cells per capture	Crosslinking performed in suspension prior to lysis
Fresh tissue	≥100 mg per capture	Minced and crosslinked immediately after collection; tissue disaggregation required
Cell pellet (snap-frozen)	≥5 × 10⁷ cells	Must be crosslinked before freezing; pre-crosslinked pellets accepted
Target RNA information	Full-length transcript sequence	Transcript ID (RefSeq/Ensembl) or FASTA sequence required for probe design

Important Notes:

All cell samples must be processed as live cells for in vivo crosslinking. Formaldehyde-fixed cell pellets are the preferred format for sample shipment.
Each RAP-Seq experiment requires a minimum of 2 capture reactions (even and odd probe pools) plus 1–2 control captures (scramble probe and/or input), requiring ≥6 × 10⁷ total cells for a standard design.
The target lncRNA expression level should be confirmed by RT-qPCR prior to RAP-Seq. Recommended minimum expression: >1 copy per cell (or Cq < 32 in 10 ng cDNA from 10⁶ cell equivalents).
For low-expressed lncRNAs, additional input material may be required. Please consult with our team for feasibility assessment.
Control samples (scramble probe capture, input chromatin) are essential for background subtraction and are included as standard in our service.
For species without an annotated reference genome, de novo sequencing and assembly may be required for probe design — please consult with our project team.

Case Study: RAP Reveals Intronic Determinants of Charme LncRNA Chromatin Retention and Myogenic Function

A 2020 study published in Cell Reports by Desideri and colleagues used RNA Antisense Purification (RAP) combined with mass spectrometry and functional genomics to investigate how the muscle-specific lncRNA Charme (Chromatin architect of muscle expression) is retained on chromatin and how its intronic sequences coordinate nuclear activity through RNA-binding protein interactions.

Charme is a muscle-specific lncRNA essential for myogenesis — the process of muscle cell differentiation. Previous work had shown that Charme exists in two isoforms generated by alternative splicing: a nuclear, chromatin-retained isoform (pCharme) that retains a large ~11 kb intron-1, and a cytoplasmic, spliced isoform (mCharme). The mechanism by which pCharme is retained on chromatin and the functional role of the retained intron in mediating chromatin interactions were unknown.

Study design for Charme lncRNA RAP analysis showing probe design and experimental workflow Figure 1. Experimental strategy for RAP-based identification of Charme lncRNA interaction partners.
Biotinylated antisense DNA probes were designed tiled across the pCharme lncRNA sequence. C2C12 myoblast cells were crosslinked with formaldehyde, and RAP was performed to capture pCharme along with its bound protein and nucleic acid partners. Mass spectrometry identified MATR3 and PTBP1 as the principal protein interactors. Adapted from Desideri et al. 2020 (CC BY 4.0).

RAP-based approach: The authors designed biotinylated antisense DNA probes tiled across the pCharme lncRNA sequence. C2C12 mouse myoblasts were crosslinked with formaldehyde to stabilize RNA-protein and protein-DNA contacts. RAP was performed by hybridizing the probe set to the crosslinked lysate, capturing the probe-RNA-chromatin complexes on streptavidin beads, and eluting the bound material. Captured proteins were identified by quantitative mass spectrometry (RAP-MS), while the RAP capture methodology used is directly applicable to RAP-DNA and RAP-RNA sequencing readouts for comprehensive interaction mapping. RNA-seq was performed after Charme knockdown and intron-1 deletion to assess downstream transcriptional effects. CRISPR-Cas9 was used to generate mice carrying a deletion of the alternatively spliced intron-1 to validate its functional importance in vivo.

Figure 2. RAP-identified mechanism of Charme lncRNA chromatin retention.
The pCharme lncRNA isoform retains intron-1, which serves as a docking platform for MATR3 and PTBP1 binding. The MATR3/pCharme interaction is required for chromatin retention of the lncRNA. Deletion of intron-1 by CRISPR-Cas9 releases pCharme from chromatin and causes cardiac defects in mice, phenocopying the full-length Charme knockout. Adapted from Desideri et al. 2020 (CC BY 4.0).

Key findings: (1) RAP-MS identified MATR3 (a multifunctional RNA/DNA binding protein) and PTBP1 (a splicing regulator) as the principal nuclear interactors of pCharme. (2) Both proteins bind specifically to sequences within the retained intron-1 of pCharme, forming nuclear ribonucleoprotein aggregates. (3) PTBP1 acts as a splicing repressor of intron-1 — its depletion increases the mCharme/pCharme ratio by promoting intron-1 removal. (4) MATR3 is required for pCharme's chromatin localization — upon MATR3 depletion, pCharme is released from chromatin and redistributes to the nucleoplasm. (5) Conversely, pCharme influences MATR3's own chromatin binding — Charme depletion causes MATR3 to relocate from chromatin to the nucleoplasm. (6) CRISPR-Cas9 deletion of intron-1 in mice caused pCharme release from chromatin and resulted in cardiac defects, matching the phenotype of full-length Charme knockout. This study demonstrates how RAP-based approaches can dissect the molecular mechanisms of lncRNA chromatin function and identify critical sequence determinants of lncRNA nuclear activity.

FAQs — Frequently Asked Questions

- 1) What is the difference between RAP-Seq and RAP-MS?
  - RAP-Seq and RAP-MS share the same core methodology — biotinylated antisense probes capture a target RNA along with its bound molecules from crosslinked cell lysate. The difference lies in the readout: RAP-Seq sequences the captured DNA (RAP-DNA for chromatin binding sites) or RNA (RAP-RNA for RNA interaction partners) using high-throughput sequencing, while RAP-MS identifies the captured proteins using mass spectrometry. The choice depends on your research question — RAP-Seq is optimal for mapping genomic binding sites and RNA interaction networks, while RAP-MS is used for identifying protein interactors.
- 2) What types of RNA can be studied by RAP-Seq?
  - RAP-Seq is most commonly applied to nuclear-enriched lncRNAs that interact with chromatin, including Xist, MALAT1, NEAT1, HOTAIR, NORAD, Firre, Kcnq1ot1, and many others. It can also be applied to other chromatin-associated RNAs including promoter-associated RNAs (paRNAs), enhancer RNAs (eRNAs), and certain snRNAs (e.g., U1, U2) that interact with nascent transcripts. The method is suitable for any RNA that can be crosslinked to its interaction partners, with the main requirement being sufficient expression level (recommended >1 copy per cell) and knowledge of the full-length transcript sequence.
- 3) How do you validate RAP-Seq results?
  - RAP-Seq results are validated at multiple levels: (1) RT-qPCR confirmation that the target lncRNA is specifically enriched in the capture vs. scramble control; (2) independent validation of top RAP-DNA binding sites by ChIRP-qPCR or CHART-qPCR using an independent probe set; (3) even/odd probe split analysis — only peaks reproducible across independent probe pools are retained; (4) IDR (Irreproducible Discovery Rate) analysis between replicates; (5) comparison of RAP-DNA binding sites with orthogonal datasets (e.g., published ChIRP-seq, CHART-seq data for the same lncRNA); (6) validation of selected target genes by RNA-seq after lncRNA perturbation to confirm functional relevance.
- 4) What controls are essential for RAP-Seq experiments?
  - Essential controls include: (1) Scramble/non-targeting probe control — a set of probes with no predicted target in the genome, used to identify non-specific background signal; (2) Input chromatin/RNA — a sample of the lysate taken before capture, representing the starting material; (3) No-antibody/no-probe control — beads-only capture to measure non-specific binding to streptavidin beads; (4) RNase-treated control — treatment with RNase A+H before capture to confirm that signal depends on intact RNA; (5) For RAP-RNA, a no-crosslink control is essential to distinguish crosslinked (specific) from post-lysis (non-specific) interactions.
- 5) How many cells are needed for a RAP-Seq experiment?
  - A standard RAP-Seq experiment requires ≥2 × 10⁷ cells per capture reaction, with a minimum of 2 probe pools (even and odd) plus controls (scramble probe and input). This typically requires ≥6–8 × 10⁷ total cells. For lncRNAs with lower expression, the cell requirement may increase to 5 × 10⁷–1 × 10⁸ cells per capture. We assess expression levels before recommending the optimal cell count. For limited cell types or difficult-to-obtain samples, we offer optimization consultations to design miniaturized protocols where feasible.
- 6) What sequencing depth is recommended for RAP-Seq?
  - For RAP-DNA, we recommend 30–50 million reads per sample (PE150) for robust peak calling. For RAP-RNA, we recommend 20–30 million reads per sample (PE150). Deeper sequencing (50–100 million reads) may be beneficial for low-abundance interaction partners or for identifying weaker binding sites. Paired-end sequencing is preferred for both applications as it improves alignment accuracy, especially for repetitive regions, and enables fragment size analysis. We include appropriate sequencing depth recommendations in our project planning based on the target lncRNA properties and research objectives.
- 7) Can RAP-Seq be combined with other RNA interaction methods?
  - Yes — RAP-Seq is most powerful when integrated with complementary approaches. We commonly recommend combining RAP-Seq with: (1) RAP-MS for simultaneous identification of DNA binding sites and protein interactors from the same cell system; (2) RNA-seq after lncRNA perturbation to connect binding sites with functional gene expression changes; (3) RIC-seq for global RNA-RNA interaction maps of the entire transcriptome; (4) RADICL-seq for unbiased, genome-wide RNA-chromatin interaction mapping. Our team provides integrated multi-omics analysis to combine these datasets into comprehensive mechanistic models.
- 8) How is RAP-Seq data analyzed and interpreted?
  - RAP-Seq data analysis involves: (1) Quality control and preprocessing (adapter trimming, read filtering, alignment); (2) Peak calling (RAP-DNA) or transcript enrichment analysis (RAP-RNA) comparing the target capture to scramble and input controls; (3) Genomic feature annotation — assigning peaks to promoters, gene bodies, enhancers, or intergenic regions; (4) Chromatin state enrichment analysis using reference epigenomes (Roadmap/ENCODE); (5) Motif discovery to identify sequence features enriched at binding sites; (6) Functional enrichment analysis of target gene sets; (7) Integration with RNA-seq data after lncRNA perturbation to identify directly regulated target genes. Our bioinformatics team provides a comprehensive report with all analysis results and publication-ready figures.

References:

Engreitz JM, Pandya-Jones A, McDonel P, et al. The Xist lncRNA exploits three-dimensional genome architecture to spread across the X chromosome. Science. 2013;341(6147):1237973.
Engreitz JM, Sirokman K, McDonel P, et al. RNA-RNA interactions enable specific targeting of noncoding RNAs to nascent pre-mRNAs and chromatin sites. Cell. 2014;159(1):188-199.
Desideri F, Cipriano A, Petrezselyova S, et al. Intronic Determinants Coordinate Charme lncRNA Nuclear Activity through the Interaction with MATR3 and PTBP1. Cell Reports. 2020;33(12):108548.
McHugh CA, Chen CK, Chow A, et al. The Xist lncRNA interacts directly with SHARP to silence transcription through HDAC3. Nature. 2015;521(7551):232-236.

For Research Use Only. This service is intended for exploratory and mechanistic research applications, including lncRNA-chromatin interaction mapping, RNA-RNA interactome discovery, and lncRNA functional characterization. It is not intended for clinical diagnosis, treatment selection, patient stratification, or therapeutic decision-making.

RAP-Seq Service

RAP-Seq Overview

RAP-Seq vs. ChIRP-Seq vs. CHART-Seq: Method Comparison