RIP-Seq Services - RBP Target Discovery

Overview

Overview of Our RIP-Seq Services

RNA-binding proteins (RBPs) control virtually every aspect of RNA metabolism — from splicing and polyadenylation to transport, stability, and translation. Identifying the full set of RNA targets for a given RBP is the essential first step in understanding its regulatory function. RNA Immunoprecipitation Sequencing (RIP-Seq) answers this question: it captures endogenous RNA-protein complexes using a specific antibody against the RBP of interest, then sequences the co-immunoprecipitated RNA to reveal all bound transcripts across the transcriptome.

Unlike CLIP-seq methods that require UV crosslinking and produce binding-site-level resolution, RIP-Seq uses native or formaldehyde-crosslinked conditions to preserve RNA-protein complexes in their physiological state. This means RIP-Seq captures the full target repertoire — including indirect interactions mediated through protein complexes — making it the preferred approach for initial target discovery, screening across conditions, and studies where understanding the complete RBP-associated transcript network matters more than mapping individual binding footprints.

At CD Genomics, we offer a standardized RIP-Seq platform covering four RNA-type workflows — mRNA, lncRNA, circRNA, and small RNA — each with IgG control normalization, sequencing, and full bioinformatics support.

Technologies

Core Technologies: RIP-mRNA, RIP-lncRNA, RIP-circRNA, and RIP-Small RNA

RIP-Seq Technology Principle

RIP-Seq identifies RNA targets of a specific RNA-binding protein (RBP) through antibody-based immunoprecipitation followed by high-throughput sequencing. The core principle is antibody specificity: a validated IP-grade antibody recognizes the target RBP, pulling down the protein along with its bound RNA in native or formaldehyde-crosslinked conditions. An IgG isotype control is processed in parallel to distinguish specific enrichment from background.

Experimental Workflow:

1. Cell Lysis — Cells or tissue are lysed under mild conditions that release RNA-protein complexes while preserving their integrity. Lysis buffer composition, incubation time, and temperature are optimized to maintain RBP-RNA interactions and prevent complex dissociation.

2. Immunoprecipitation — The RBP-specific antibody is added to the lysate, forming an "antibody–RBP–RNA" ternary complex. Protein A/G magnetic beads are then added to capture the antibody-bound complexes. A parallel IgG isotype control IP is performed with the same lysate.

3. RNA Release and Purification — Proteinase K digestion removes protein components, releasing the co-immunoprecipitated RNA. RNA is purified by phenol-chloroform extraction or column-based methods, with concentration and purity verified by spectrophotometry.

4. Library Construction and Sequencing — Purified RNA is converted to sequencing libraries using RNA-type-appropriate protocols (see below). Libraries undergo QC and quantification before high-throughput sequencing. IP and IgG libraries are sequenced to comparable depth on the same flow cell.

RIP-Seq technology principle: cell lysis, antibody IP, RNA purification, library construction and sequencing

Service Options by Target RNA Type

After IP, the choice of RNA enrichment and library preparation strategy depends on the RNA class your RBP is known or suspected to target. Each option below uses the same core IP workflow; the difference lies in post-IP RNA handling.

RIP-mRNA

Poly(A) selection after IP enriches for protein-coding transcripts. The standard entry point for most RBPs — splicing regulators, stability factors such as HuR/ELAVL1, and translational regulators. Targets are identified by comparing IP enrichment over IgG after sequencing.

RIP-lncRNA

rRNA-depleted total RNA after IP captures both mRNA and lncRNA targets, with computational separation during annotation. Suited for RBPs implicated in chromatin regulation, X-chromosome inactivation, or processes where lncRNA-protein interactions are central.

RIP-circRNA

RNase R treatment after IP digests linear RNA, enriching the circular fraction before library construction. Enables focused identification of circRNA targets. circRNAs are covalently closed, exonuclease-resistant, and increasingly recognized as functional RBP partners.

RIP-Small RNA

Size selection (<200 nt) after IP with specialized small RNA library preparation. Captures miRNA, piRNA, snoRNA, and tRNA fragments that co-immunoprecipitate with the RBP. Suited for RBPs in miRNA processing (Drosha, Dicer, Argonaute) or other small RNA pathways.

Workflow

End-to-End Workflow and Quality Control

Our RIP-Seq service follows a standardized workflow with QC checkpoints at each stage. The IgG control is sequenced in parallel with every IP — not as a batch control, but as a sample-matched negative control that enables direct background subtraction.

1. Sample Receipt and Quality Assessment — QC Checkpoint: cell count and viability (if cells submitted); tissue weight and integrity; or RNA post-IP — concentration, purity (OD260/280, OD260/230), and integrity (Bioanalyzer/TapeStation).

2. RIP Assay — QC Checkpoint: cells/tissue lysed under mild conditions preserving RNA-protein complexes; RBP-specific antibody + IgG isotype control incubated in parallel with Protein A/G beads; wash stringency optimized per RBP; aliquot of post-IP RNA checked by qPCR for known positive and negative control transcripts before proceeding to library construction.

3. RNA Purification and QC — QC Checkpoint: protein digested; RNA extracted; concentration and purity verified; enrichment of known target transcripts confirmed by qPCR relative to IgG control.

4. Library Construction and Sequencing — QC Checkpoint: RNA-type-specific library preparation (poly(A) for mRNA, rRNA depletion for lncRNA, RNase R for circRNA, size selection for small RNA); library yield and fragment size; Q30 scores; IP and IgG libraries sequenced to comparable depth on the same flow cell to minimize batch effects.

5. Data QC and Bioinformatics — QC Checkpoint: IP vs IgG read distribution confirming enrichment over background; peak signal-to-noise; replicate correlation; peak reproducibility; deliverable completeness.

Sample

Sample and Antibody Requirements

The success of a RIP-Seq experiment depends on both sample quality and antibody specificity. We work with you to validate both before committing to full-scale sequencing.

Service Tier	Sample Types Accepted	Input Guideline	Antibody Requirement	Notes
RIP-mRNA-Seq	Cells, tissue, or post-IP RNA	≥ 100 ng IP RNA recommended; ≥ 40 ng minimum; ≥ 5 ng/μL	IP-grade antibody (5–10 μg per IP); validated for IP by customer or with in-house pilot	Most common workflow; poly(A) selection after IP
RIP-lncRNA-Seq	Cells, tissue, or post-IP RNA	Project-specific — contact us	IP-grade antibody	rRNA depletion after IP; captures both mRNA and lncRNA
RIP-circRNA-Seq	Cells, tissue, or post-IP RNA	Project-specific — contact us	IP-grade antibody	RNase R treatment after IP to enrich circRNA; linear RNA depletion verified by qPCR
RIP-Small RNA-Seq	Cells, tissue, or post-IP RNA	≥ 50 ng IP RNA recommended; ≥ 20 ng minimum; ≥ 5 ng/μL	IP-grade antibody	Size selection (<200 nt) after IP; specialized small RNA library prep

Antibody Guidelines:

The single most important factor in RIP-Seq success. Use an antibody validated for immunoprecipitation — not just western blot or immunofluorescence.
We recommend providing 5–10 μg of antibody per IP reaction. If antibody quantity is limited, contact us to discuss titration.
If no validated antibody is available, we can discuss commercial IP-grade antibodies for common RBPs (HuR, Ago2, FMRP, IGF2BP family, PTBP1, etc.).
For custom antibodies: an in-house pilot RIP-qPCR can validate IP efficiency before committing to sequencing.

Shipping: Samples on dry ice; include completed submission form. Avoid repeated freeze-thaw.

Bioinformatics

Bioinformatics Analysis and Deliverables

RIP-Seq data analysis centers on comparing IP to IgG control to identify transcripts specifically enriched by the antibody — your RBP's targets. Our pipeline produces publication-ready figures and fully documented analysis parameters.

Standard Deliverables:

Deliverable	Description
Raw sequencing data	Demultiplexed read files for IP and IgG libraries
Aligned reads	Reads aligned to reference genome for IP and IgG
RIP enrichment peaks	Statistically significant enriched regions (IP vs IgG)
Normalized signal tracks	IP and IgG coverage tracks for genome browser visualization
Peak annotation	Peaks annotated to genes and genomic features (5'UTR, CDS, 3'UTR, intron, intergenic)
QC report	IP vs IgG enrichment metrics; read distribution; library complexity; replicate correlation
Target gene list	High-confidence RBP target transcripts ranked by enrichment and significance
Differential binding analysis	Statistically significant differential RIP enrichment between user-defined condition groups
Motif discovery	De novo and known motif enrichment in peak regions
GO/KEGG enrichment	Functional enrichment of RBP target genes

Optional Advanced Analysis:

Integrative analysis with RNA-seq — compare RIP targets with differential expression data to identify post-transcriptionally regulated targets
Multi-RBP comparison — Venn diagrams, overlap statistics, and network visualization for multiple RBPs profiled in the same system
CLIP-seq / eCLIP data integration — overlay published CLIP data with your RIP targets for cross-validation
RBP-RNA interaction network construction — Cytoscape-compatible network files
Custom visualization — publication-ready figures including circos plots, heatmaps, and genome browser views
Epigenomic data analysis — see our dedicated Epigenomic Data Analysis service

Demo

Representative Data and Demo Results

The composite image below illustrates the data types delivered with each RIP-Seq project.

RIP Enrichment and Target Identification:

IP vs IgG enrichment track (Panel A) — IGV-style genome browser view showing IP coverage (color), IgG control coverage (gray), and called peaks above significance threshold across a representative genomic locus. Enrichment over IgG is the primary evidence for specific RBP binding.
Peak genomic distribution (Panel B) — pie or stacked bar chart showing the distribution of RIP peaks across 5'UTR, CDS, 3'UTR, intron, and intergenic regions. This distribution is RBP-specific and provides insight into the protein's regulatory role (e.g., 3'UTR-biased peaks suggest mRNA stability regulation).
Metagene profile (Panel C) — average RIP enrichment signal (IP/IgG ratio) plotted across a normalized gene body from transcription start site to transcription end site. Peak shape and position along the gene body reveal the RBP's preferred binding region.

Differential Analysis and Motif Discovery:

Differential RIP volcano plot (Panel D) — each point represents a transcript; x-axis shows log2 fold change in RIP enrichment between conditions; y-axis shows -log10 adjusted p-value. Transcripts with significantly altered RBP binding are highlighted.
Enriched sequence motif (Panel E) — consensus binding motif identified from RIP peak regions using de novo motif discovery. The motif recovered should match the RBP's known binding preference, serving as internal validation of IP specificity.
GO/KEGG enrichment bar chart (Panel F) — top enriched biological process terms among high-confidence RBP target genes, providing functional interpretation of the RBP's regulatory role.

All demo results are generated from representative datasets and reflect the standard analysis depth delivered with each project.

Applications

Applications Across Research Areas

Post-Transcriptional Regulation in Cancer

RBPs including HuR/ELAVL1, IGF2BP family members, and LIN28 are frequently dysregulated in cancer, where they stabilize oncogenic mRNAs, alter splicing, and drive tumor progression. RIP-Seq identifies the complete target repertoire of these RBPs in cancer versus normal cells, revealing which transcripts are differentially bound and may mediate the RBP's oncogenic or tumor-suppressive effects.

Neuroscience and Neurodegeneration

RBPs such as FMRP, TDP-43, FUS, and hnRNPA2/B1 are central to neuronal RNA regulation — and their dysfunction underlies fragile X syndrome, ALS, and frontotemporal dementia. RIP-Seq maps the neuronal RNA targets of these proteins in specific brain regions, cell types, or disease models, linking RBP dysfunction to specific transcript networks.

lncRNA and circRNA Mechanism Studies

Long non-coding RNAs and circular RNAs often function through specific RBP interactions — as scaffolds assembling protein complexes, as decoys titrating RBPs away from mRNA targets, or as guides directing RBPs to genomic loci. RIP-Seq with the RBP as bait identifies which lncRNAs and circRNAs participate in these complexes, providing functional leads for mechanistic follow-up.

miRNA Biogenesis and RISC Complex Analysis

Argonaute (Ago1-4) proteins are the core effectors of miRNA-mediated silencing. RIP-Seq with anti-Ago antibody identifies the full repertoire of miRNAs and their mRNA targets in a given cell type — an approach known as Ago-RIP or RIP-Chip when applied to miRNA target discovery. Similarly, RIP-Seq with Dicer or Drosha antibodies reveals miRNA processing intermediates.

Viral Infection and Host-Virus Interactions

Viruses hijack host RBPs to facilitate their replication, while host cells deploy RBPs to recognize and restrict viral RNA. RIP-Seq can identify which host and viral RNAs are bound by specific RBPs during infection, revealing both proviral and antiviral interaction networks. This approach has been applied to SARS-CoV-2, influenza, HIV, and hepatitis viruses.

Developmental Biology and Stem Cells

RNA-binding proteins drive cell fate decisions by coordinating post-transcriptional regulons — groups of mRNAs co-regulated at the level of splicing, stability, or translation. RIP-Seq in stem cells, developing tissues, or differentiation time courses reveals how RBP target networks are rewired during development and what goes wrong in developmental disorders.

Case

Case Study: AGO2-RIP-Seq Reveals miR-34/miR-449 Targetome in Sinonasal Cancers

Background

Sinonasal cancers (SNCs) are heterogeneous malignancies with variable histological features and clinical outcomes. The miR-34/miR-449 cluster had been previously identified by miRNome analysis as a miRNA superfamily involved in SNC pathogenesis, but its direct mRNA targets remained unknown. Tomasetti et al. set out to systematically identify the direct target genes (targetome) of miR-34/miR-449 in SNC tissue using an Argonaute-2 RIP-Seq approach — capturing Ago2-bound mRNAs that are actively targeted by these miRNAs in the RNA-induced silencing complex (RISC).

Methods

AGO2-RNA immunoprecipitation was performed on sinonasal cancer tissue lysates using an anti-AGO2 antibody, with parallel IgG isotype control. Co-immunoprecipitated mRNA was purified, converted to sequencing libraries, and sequenced on an Illumina platform. Enriched transcripts over IgG control were identified as AGO2-bound miRNA targets. Target identification integrated RIP-Seq enrichment data with miRNA target prediction algorithms to distinguish direct miR-34/miR-449 targets. Selected direct targets (STK3, C9orf78, STRN3) were validated by RIP-qPCR and correlated with clinical parameters for prognostic evaluation.

Results

RIP-Seq identified a set of direct miR-34/miR-449 target genes enriched in functional categories related to RNA-DNA metabolism, transcriptional regulation, and epigenetic processes. Three direct targets — STK3, C9orf78, and STRN3 — were bound and regulated by both miR-34c and miR-449a, with their expression levels predictive of tumour progression. The study demonstrated that AGO2-RIP-Seq can map the complete target repertoire of a miRNA cluster in clinical tissue samples, bridging miRNA expression data with direct target identification.

Conclusion

This study validates AGO2-RIP-Seq as an effective approach for miRNA targetome identification in clinical cancer specimens. By directly capturing the mRNAs engaged by the miR-34/miR-449 cluster through Ago2, the authors connected miRNA dysregulation to specific target gene networks and identified potential prognostic biomarkers. For researchers studying miRNA function in any cancer type, AGO2-RIP-Seq provides the experimental evidence that prediction algorithms alone cannot deliver — physically confirmed miRNA-mRNA target pairs in their native cellular context.

AGO2-RIP-Seq target identification workflow and miR-34/miR-449 target gene network from Tomasetti et al., PLoS ONE, 2024

Source: Tomasetti M, Monaco F, Rubini C, et al. AGO2-RIP-Seq reveals miR-34/miR-449 cluster targetome in sinonasal cancers. PLoS ONE, 2024, 19(1):e0295997. https://doi.org/10.1371/journal.pone.0295997 — CC BY 4.0.

Method Comparison

Method Comparison: RIP-Seq vs CLIP-Seq vs eCLIP

Feature	RIP-Seq	CLIP-Seq (HITS-CLIP)	eCLIP
Principle	Antibody IP of RBP-RNA complexes (native or formaldehyde)	UV crosslinking + antibody IP + SDS-PAGE purification + RNase footprinting	UV crosslinking + antibody IP + adaptor ligation + size-matched input control
Crosslinking	Formaldehyde or none (native)	UV 254 nm (covalent, zero-distance)	UV 254 nm (covalent, zero-distance)
Interaction type detected	Direct + indirect (complex-mediated)	Direct only	Direct only
Resolution	Transcript-level	~30–100 nt binding region	Near-nucleotide
Input needed	Lower (fewer cells acceptable)	Higher (millions of cells)	Higher (millions of cells)
Background correction	IgG isotype control	Stringent washes + SDS-PAGE	Size-matched input (SMI) control
Crosslinking efficiency	High (formaldehyde)	Low (UV 254 nm; ~1–5% crosslinking)	Low (~1–5%)
Antibody tolerance	Moderate — formaldehyde can compensate for weaker antibodies	Low — requires high-specificity IP-grade antibody	Low — requires high-specificity antibody
Bioinformatics complexity	Straightforward — peak calling, annotation, motif analysis	Moderate — crosslink-induced mutations/deletions complicate alignment	Advanced — SMI normalization, crosslink site calling
Best for	Target discovery; RBP network mapping; low-input samples; pilot experiments; physiological interactome studies	Binding site mapping; direct contact site identification; mechanistic follow-up	Large-scale RBP profiling; nucleotide-resolution maps; robust statistics with built-in controls

RIP-Seq occupies a distinct and valuable position in the RBP tool kit: it is the most accessible, lowest-input method for discovering the full RNA target repertoire of an RBP. While it does not provide binding-site resolution, it captures the complete interaction network — including complex-mediated contacts that UV-crosslinking methods miss — making it the method of choice for initial target identification and for studies where the biological question is "which RNAs does my protein bind?" rather than "exactly where does it bind?"

Selection Strategy:

Start with your question, not the method. If your goal is "which transcripts does my RBP bind?" — RIP-Seq answers this directly and efficiently. If your goal is "exactly where on the RNA does my RBP bind, at single-nucleotide resolution?" — eCLIP is the appropriate choice.
Consider input and feasibility. RIP-Seq works with fewer cells and is more tolerant of antibody variability because formaldehyde crosslinking preserves weaker interactions. CLIP methods require higher input and are less forgiving of suboptimal antibodies.
The discovery-to-mechanism pipeline. An efficient workflow is RIP-Seq to identify the full target repertoire, followed by eCLIP on 1–2 top candidates for mechanistic binding-site mapping.
Native vs crosslinked RIP. For RBPs with high-affinity, stable interactions, native RIP (no crosslinking) reduces background from formaldehyde-induced artifacts. For transient or low-affinity interactions, formaldehyde crosslinking stabilizes complexes before IP.

Speak with a RIP-Seq Specialist

FAQ

Frequently Asked Questions

Q: What is RIP-Seq and how does it work?

A: RNA Immunoprecipitation Sequencing (RIP-Seq) identifies all RNA transcripts bound by a specific RNA-binding protein (RBP). An antibody against the RBP of interest is used to pull down the protein along with its bound RNA from cell or tissue lysate. An IgG isotype control antibody is processed in parallel to control for non-specific binding. The co-immunoprecipitated RNA is purified and sequenced, and targets are identified by comparing IP enrichment to IgG background — transcripts significantly enriched in the IP over IgG are called as RBP targets.

Q: What is the difference between RIP-Seq and CLIP-Seq?

A: The main differences are crosslinking and resolution. RIP-Seq uses formaldehyde or native conditions and provides transcript-level target lists — it tells you which RNAs your RBP binds. CLIP-Seq uses UV crosslinking to create covalent bonds at direct protein-RNA contact sites, followed by RNase digestion to leave protected footprints — it tells you where on the RNA the RBP binds, at ~30–100 nt resolution. RIP-Seq captures direct + indirect (complex-mediated) interactions; CLIP captures only direct contacts. RIP-Seq requires fewer cells and is more tolerant of antibody variability. eCLIP builds on CLIP with improved library construction and built-in normalization controls for near-nucleotide resolution.

Q: Do I need to provide the antibody? What are the requirements?

A: Yes, the customer provides the RBP-specific antibody. The antibody must be validated for immunoprecipitation — not just western blot. We recommend 5–10 μg of antibody per IP reaction. If you do not have a validated IP-grade antibody, we can help source commercial options for common RBPs (HuR/ELAVL1, Ago2, FMRP, IGF2BP1-3, PTBP1, etc.), or we can run a pilot RIP-qPCR with your antibody to validate IP efficiency before committing to full-scale sequencing. Every sample includes an IgG isotype control processed in parallel.

Q: What resolution does RIP-Seq provide?

A: RIP-Seq provides transcript-level resolution — it identifies which transcripts are bound by the RBP, but not the exact binding site on each transcript. The resolution is determined by RNA fragment size during the IP (~100–300 nt after fragmentation). For researchers who need binding-site-level or nucleotide-level resolution, CLIP-Seq or eCLIP are more appropriate. Many groups use RIP-Seq for initial target discovery followed by eCLIP on key candidates for mechanistic mapping.

Q: How much starting material do I need?

Q: How are RIP peaks called and differential analysis performed?

Q: What bioinformatics deliverables are included?

A: Standard deliverables include: raw data (FASTQ), aligned reads (BAM), RIP peaks (BED), signal tracks (bigWig), peak annotation table, target gene list, QC report, differential binding analysis (if comparing conditions), de novo motif discovery, and GO/KEGG enrichment analysis. Optional add-ons include integrative analysis with RNA-seq, multi-RBP comparison, and custom visualization. See the Bioinformatics section and our Epigenomic Data Analysis page for details.

Q: Can RIP-Seq distinguish direct from indirect RNA-protein interactions?

A: Not on its own. Because RIP-Seq uses formaldehyde or native conditions, it captures the entire protein complex — including proteins that interact with your RBP indirectly and their associated RNAs. This can be an advantage (you see the full interaction network) or a limitation (you cannot claim direct binding from RIP-Seq data alone). For distinguishing direct from indirect, CLIP-Seq (UV crosslinking at zero-distance contacts) is the appropriate method. In practice, many groups use RIP-Seq for discovery and CLIP for mechanistic validation of direct binding.

Q: Is RIP-Seq compatible with non-model organisms?

References

Zhao J, Ohsumi TK, Kung JT, et al. Genome-wide identification of polycomb-associated RNAs by RIP-seq. Molecular Cell, 2010, 40(6):939-953. https://doi.org/10.1016/j.molcel.2010.12.011
Gagliardi M, Matarazzo MR. RIP: RNA Immunoprecipitation. Methods in Molecular Biology, 2016, 1480:73-86. https://doi.org/10.1007/978-1-4939-6380-5_7
Van Nostrand EL, Pratt GA, Shishkin AA, et al. Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nature Methods, 2016, 13(6):508-514. https://doi.org/10.1038/nmeth.3810
Hafner M, Katsantoni M, Koster T, et al. CLIP and complementary methods. Nature Reviews Methods Primers, 2021, 1:20. https://doi.org/10.1038/s43586-021-00018-1
Wheeler EC, Van Nostrand EL, Yeo GW. Advances and challenges in the detection of transcriptome-wide protein-RNA interactions. Wiley Interdisciplinary Reviews: RNA, 2018, 9(1):e1436. https://doi.org/10.1002/wrna.1436
Tomasetti M, Monaco F, Rubini C, et al. AGO2-RIP-Seq reveals miR-34/miR-449 cluster targetome in sinonasal cancers. PLoS ONE, 2024, 19(1):e0295997. https://doi.org/10.1371/journal.pone.0295997

RIP-Seq for RNA-Protein Interaction Analysis