Standard Genome-Wide ChIRP-seq Map
Definitive binding map for a single condition, delivered with high-confidence peak curation and interpretable annotations.
ChIRP-seq Service
Genome-Wide RNA–Chromatin Mapping (lncRNA/circRNA)
Where does your lncRNA/circRNA bind the genome—confidently, not noisily?
Our ChIRP-seq service delivers genome-wide RNA–chromatin maps using Odd/Even tiling probes and Common Peaks to reduce false positives, with qPCR-backed verification and optional ChIRP-MS to identify co-purifying RNA-binding proteins. This solves three common problems: unclear target loci, background-driven artifacts, and disconnected multi-omics evidence.
Built for research CRO workflows, we combine stringent controls (lacZ/Input/Positive) with clean bioinformatics (annotation, GO/Pathway, motif) so you get interpretable, publication-ready results.
Start your project today—get a design consult and a tailored ChIRP-seq mapping plan for your lncRNA/circRNA.
What Is ChIRP-Seq?
Chromatin Isolation by RNA Purification followed by sequencing (ChIRP-seq) maps genome-wide binding sites of a known lncRNA or circRNA in intact cells. Biotinylated tiling probes (20–30 nt, ~100-nt spacing; Odd/Even pools) hybridize to the target RNA in crosslinked ncRNA:protein:chromatin complexes, which are captured on streptavidin beads. After RNase H release, the associated DNA is sequenced to pinpoint RNA-associated loci at high resolution. Pairing with ChIRP-MS (optional) identifies RNA-binding proteins (RBPs) from the same pull-down.
Typical Scientific Questions ChIRP-Seq Can Answer
- Where does my lncRNA/circRNA bind the genome in vivo?
- Are the sites enriched at promoters, enhancers, or other regulatory elements?
- Do Odd and Even probe pools reproduce the same loci (high-confidence Common Peaks)?
- Which nearby genes are implicated, and are specific pathways enriched?
- Are peaks associated with characteristic DNA motifs or chromatin marks?
- How do binding patterns change across conditions (e.g., knockdown, overexpression, stimuli)?
- Is my target RNA sufficiently nuclear for DNA-centric mapping, or should we consider protein-centric alternatives?
- Do ChIRP-seq peaks co-locate with known chromatin factors (ChIP-seq) or accessibility changes (ATAC-seq)?
- Do ChIRP-seq results align with expression changes of nearby genes (RNA-seq)?
Advantages of Our ChIRP-Seq Service
High-Confidence Peak Identification — Common Peaks from Odd/Even Tiling
Dual probe pools allow independent enrichment; only overlapping peaks (Odd ∩ Even) are retained as high-confidence signals.
Optimized Signal Quality — Typical Unique Mapping ≥65–80%
Careful fragmentation and library prep yield high-quality alignments for clear signal and effective downstream annotation.
Specificity-Controlled Assays — Built-in lacZ, Input, and Positive Controls
Each experiment includes reference controls to distinguish true enrichment from background or off-target hybridization.
qPCR-Backed Validation — Representative Peaks Verified Experimentally
Enrichment of selected loci is confirmed by qPCR, providing tangible confidence in sequencing results.
Integrated Biological Insights — GO, Pathway, and Motif Analysis Included
Downstream outputs include functional enrichment and motif discovery to support regulatory interpretation.
RBP Discovery-Ready — Seamless ChIRP-MS Integration (Optional)
For studies requiring protein context, add LC-MS/MS to identify RNA-binding proteins from the same experimental pull-down.
Technical Services
Service Scope
Method Comparison
Workflow
Platform
Input Requirements
Deliverables
FAQ
Get a Custom Proposal
ChIRP-seq Mapping Solutions: What We Offer at Creative Proteomics
Differential ChIRP-seq (State or Time Course)
Comparison across conditions (e.g., knockdown/overexpression/stimulus) to identify gained/lost or shifted RNA–chromatin sites.
ChIRP-seq + ChIRP-MS Bundle
Single study design linking genomic loci to RNA-binding proteins for a complete locus→protein view of mechanism.
Validation Pack (ChIRP-qPCR / WB Add-On)
Targeted confirmation of priority peaks or protein associations to support figures, internal reviews, or manuscripts.
ChIRP-seq Method Comparison Guide
| Method | ChIRP-seq | ChIRP-MS | CHART/RAP-seq | CLIP-seq (e/iCLIP) | RIP-seq | ChIP-seq |
| Primary question | Where does a specific lncRNA/circRNA contact the genome? | Which proteins co-purify with my RNA? | Where does an RNA bind chromatin (alt chemistry)? | Where does an RBP contact RNA in vivo? | What RNAs are bound by an RBP? | Where does a protein bind DNA? |
| Captures | RNA-associated DNA loci (in vivo), Odd/Even tiling | RBPs from same capture | RNA-associated DNA (protocol variant) | Protein–RNA crosslink sites | RNAs co-IP’d with an RBP | Protein–DNA occupancy |
| Output / resolution | Peaks; Common Peaks (Odd ∩ Even), annotations, GO/Pathway, motifs | LC-MS/MS IDs + quant tables | Genomic binding map | High-res RBP–RNA sites | RNA lists/transcripts | Protein-DNA peak landscape |
| Key strengths | Direct RNA→DNA map; genome-wide; strong specificity with controls | Links loci→proteins; not limited by RNA length | Useful for certain targets/contexts | RBP-centric, fine contact mapping | Simple discovery of RBP’s RNAs | Mature ecosystem for TF/mark mapping |
| Limitations | Needs known RNA seq; nuclear localization; manage nonspecifics | No DNA map; proteomics complexity | Less standardized | Antibody quality critical; not a DNA map | Lower positional resolution | Not RNA-guided |
| Best when | Nuclear lncRNA/circRNA target discovery; enhancer/promoter association; condition comparisons | You also need RBP identities for the same RNA | Literature/team prefers these chemistries | Starting from an RBP to find its RNA sites | Quick catalog of RNAs bound by an RBP | Testing TF/mark occupancy independent of RNA |
Quick chooser:
- Start with ChIRP-seq for nuclear lncRNA/circRNA genomic loci.
- Add ChIRP-MS to identify RBPs traveling with the same RNA.
- Use CLIP/eCLIP when the question is protein-first (RBP → RNAs).
- Pick RIP-seq for a fast RNA catalog bound by an RBP.
- Choose ChIP-seq for protein occupancy on DNA, not RNA-guided.
- Consider CHART/RAP only if prior data/experience favors those chemistries for your RNA.
HDX-MS Analysis Workflow at Creative Proteomics
1
Probe Design (Odd/Even)
20–30 nt reverse-complement oligos, ~100-nt spacing, 5'→3' indexing; split into Odd and Even pools.
2
Crosslink & Fragment
Formaldehyde (± brief glutaraldehyde as needed); controlled sonication to 100–500 bp.
3
Hybridization & Capture
Probes bind ncRNA in ncRNA:RBPs:chromatin complexes; streptavidin magnetic pull-down; stringent washes.
4
Partitioned Recovery
RNase H → DNA (NGS/qPCR); protein elution → LC-MS/MS (optional ChIRP-MS); qRT-PCR confirms RNA capture.
5
Sequencing & Bioinformatics
QC/trim → alignment → Odd/Even peaks → Common Peaks (Odd ∩ Even) → annotation → GO/pathway → motifs → browser tracks & figures.
6
Controls & Validation
lacZ external control, Input DNA control, Positive control (where applicable), qPCR on representative loci.
Experimental Design, Controls & QC
Odd vs. Even probe pools — Independent capture increases confidence and flags off-target enrichment.
Controls — lacZ external control for probe specificity; Input genomic DNA control; Positive control with known probes/RBPs where applicable.
Risk mitigation — Optimize fixation windows and shearing intensity to avoid chromatin disruption; stringent washes to reduce nonspecific hybridization.
Specificity checks — qPCR for target recovery; replicate concordance; background assessment.
ChIRP-Seq Instrumentation and Platform Capabilities
Sequencing (Illumina short-read)
- Platforms: NextSeq / NovaSeq class
- Read length: PE75–PE150 (paired-end recommended)
- Depth: ~25–60 M reads/sample (study-dependent)
Chromatin Shearing
- Instrument: Covaris-class ultrasonicator
- Target fragments: ~100–500 bp
Capture Chemistry
- Beads: high-binding streptavidin magnetic
- DNA release: RNase H from RNA:DNA hybrids
Library QC
- Size: Bioanalyzer/Fragment Analyzer (~300–500 bp inserts)
- Quant: Qubit + qPCR (cluster-ready molarity)
- On-run: alignment %, duplication %, insert size, FRiP
Probe Basics (design inputs)
- Oligos: 20–30 nt, tiled ~every 100 nt, Odd/Even pools
NextSeq 2000 (Fig from illumina)
NovaSeq X Plus(Fig from illumina)
ChIRP-Seq Sample Types and Input Guidelines
| Item | What to submit | Key notes |
| Sample types | Adherent cells, suspension cells, primary cells; optional organoids or nuclei preps | Expression of bait–ligase fusion required; provide localization intent |
| Sample amount | Cell pellets: ~5–20 million cells per condition Lysates: ~1–2 mg total protein |
Lower inputs may be feasible with optimized capture; discuss during scoping |
| Cell material | Snap-frozen pellets or clarified lysates | Avoid thaw cycles; keep identifiers consistent across conditions/replicates |
| Construct info | Construct map, amino acid sequence, tag orientation | Note targeting signals (e.g., NLS, ER, mito) and any linkers |
| Controls | Negative control sample | Non-tagged or inactive-tag control for background modeling |
| Labeling details | Biotin addition status, duration, concentration | Brief washout helps reduce free-biotin carryover before harvest |
| Lysis compatibility | Buffer recipe used | Compatible with streptavidin capture; list detergents/salts used |
| Additives | Inhibitors and special reagents | Include protease/phosphatase inhibitors; avoid biotin analogs that hinder capture |
| Prohibited/avoid | Problematic chemicals | High levels of PEG/polymers, heparin, or MS-unfriendly surfactants |
| Shipping | Packaging and temperature | Cold packs or dry ice; label tubes with project ID, condition, replicate |
Deliverables: What You Get from Our ChIRP-Seq Service
- Methods & QC Summary (PDF) — probe overview (Odd/Even), crosslink/shear settings, library/mapping metrics, FRiP, control outcomes
- Raw FASTQ (R1/R2) — per library with run-level QC
- Aligned BAM + BAI — sorted/indexed alignments with basic metrics
- Peak Files (Odd & Even) — BED/narrowPeak — pool-specific calls
- Common Peaks (Odd ∩ Even) — BED + rank table (TSV/CSV) — curated high-confidence set
- Annotated Peaks (TSV/CSV) — nearest gene and region class (promoter/enhancer/intron, etc.)
- Genome Tracks — bigWig/bedGraph — per-sample coverage and peak tracks
- GO/Pathway Enrichment Tables (TSV/CSV) — adjusted p-values and gene sets
- Motif Analysis Results — discovered/known motifs with target lists
- Figure Pack (PNG/SVG/PDF) — heatmaps, locus snapshots, summary plots with legends
- Sample Sheet & Probe List Summary (CSV/TSV) — sample IDs/groups and Odd/Even tiling overview
- Project README + Pipeline Manifest (TXT/YAML) — folder map, tool versions, and key parameters
Genome Browser Composite (Locus Panel)
Aggregate Signal + Heatmap (Common Peaks)
Differential Binding Summary (Condition Comparison)
Motif & Functional Enrichment Panel
You May Want to Know
What does ChIRP-seq actually measure compared with ChIP-seq or CLIP-seq?
ChIRP-seq is RNA-centric: it pulls down a specific lncRNA/circRNA with its chromatin and sequences the co-purified DNA to map RNA-associated genomic loci; ChIP-seq maps protein–DNA occupancy, while CLIP-seq maps protein–RNA contact sites, so they answer different questions and are often complementary.
Why do many protocols split probes into “Odd” and “Even” pools?
Independent Odd/Even tiling captures let you intersect the two peak sets and keep only the shared "Common Peaks," a widely used strategy to control off-target hybridization and raise confidence in site calls.
Is RNase H part of the core chemistry, and what does it do?
Yes—after capture, RNase H selectively digests RNA in RNA:DNA hybrids so the associated DNA fragments can be released and sequenced to localize the RNA’s binding sites genome-wide.
Do I need a known RNA sequence to run ChIRP-seq?
Yes—the method relies on biotinylated antisense tiling probes against the target RNA; without the sequence, probe design and specific capture aren’t possible.
How is ChIRP-seq different from CHART-seq or RAP-seq—when would I pick one over another?
All three are RNA-guided chromatin mapping methods; ChIRP-seq is broadly used with short tiling probes, CHART employs capture of accessible regions via RNase-H–sensitive sites, and RAP uses longer probes and more stringent washes to reduce nonspecifics—labs usually choose based on target behavior, prior experience, and probe chemistry preferences.
Can ChIRP-seq identify the proteins bound to my RNA?
Not directly; ChIRP-seq yields DNA maps, but running the matched proteomics pull-down (ChIRP-MS) on the same capture identifies co-purifying RNA-binding proteins for mechanistic context.
Does nuclear localization of the RNA matter?
Yes—DNA mapping requires that the RNA be present in the nucleus and associated with chromatin; cytoplasmic-restricted RNAs are generally better served by protein-centric assays (e.g., CLIP/RIP) rather than RNA→DNA mapping.
What sample handling details commonly affect specificity and background?
Stringent wash buffers, appropriately fragmented chromatin, and correct streptavidin-bead handling are critical; published protocols specify bead preparation and wash conditions that minimize nonspecific carry-over.
What QC or analysis conventions are typical for trustworthy calls?
Best practice is to call peaks separately for Odd and Even libraries and retain the intersection ("Common Peaks"), then report mapping metrics and show genome-browser evidence and motif/functional enrichments to support biological relevance.
How reproducible is the Odd/Even strategy in practice?
Foundational studies reported highly correlated signals between Odd and Even probe sets with comparable libraries, supporting use of their overlap as high-confidence sites.
Can ChIRP-seq reveal enhancer or promoter associations for my RNA?
Yes—once peaks are called, annotation against gene features and regulatory catalogs can show promoter/enhancer enrichment and support hypotheses about RNA-guided regulation.
