Creative Proteomics offers advanced CLIP-Seq Analysis Services to precisely map RNA–protein interactions at single-nucleotide resolution across the transcriptome. Unlock insights into RNA processing, splicing regulation, localization, and stability with comprehensive, high-throughput data tailored to your research needs.
Why Choose Us:
Get high-resolution maps. Uncover hidden regulatory networks.
CLIP-Seq (Crosslinking and Immunoprecipitation Sequencing) is a high-throughput technique that enables the identification of direct RNA targets of RNA-binding proteins (RBPs) in living cells. By combining UV crosslinking, immunoprecipitation, and next-generation sequencing (NGS), CLIP-Seq maps RBP-RNA interaction sites at single-nucleotide resolution, distinguishing direct physical interactions from indirect associations.
This method is invaluable for revealing complex RNA regulatory networks, characterizing protein-RNA binding motifs, and understanding how RBPs influence transcriptome dynamics in normal and pathological conditions.
High-Resolution Mapping — Up to Single-Nucleotide Precision
We employ optimized crosslinking and enzymatic trimming protocols that enable detection of RNA-protein interaction sites with 1–5 nucleotide resolution, empowering confident identification of direct binding motifs and regulatory elements.
Deep Sequencing for Comprehensive Coverage
Each sample is sequenced to a depth of up to 60–100 million reads, ensuring sufficient coverage for both abundant and low-expression transcripts, and enabling accurate detection of rare or transient RNA-protein interactions.
Superior Mapping Efficiency – >90% Alignment Rate
Our in-house developed quality control and preprocessing pipeline consistently delivers:
This maximizes usable data and minimizes background noise.
Proven Antibody Optimization – >80% Enrichment Consistency
Using validated antibodies and customized immunoprecipitation protocols, we achieve an average >80% enrichment efficiency, ensuring high signal-to-noise ratios and reproducible peak calling across replicates.
Accurate Binding Site Annotation – False Discovery Rate <1%
Our peak-calling algorithms incorporate machine learning-based noise reduction and statistical control, delivering high-confidence binding site identification with an estimated FDR below 1%.
Rapid, Actionable Reporting – Data Delivered in 3 Levels
We offer results in a tiered data package that includes:
Flexible Throughput – From 1 to 100+ Samples per Project
Our lab infrastructure and modular pipeline allow flexible scalability, supporting everything from pilot studies to large-scale clinical cohort projects, with batch processing options to ensure consistency.
100% Customized Bioinformatics Analysis
We don’t rely on generic pipelines. Our team provides:
At Creative Proteomics, our CLIP-Seq analysis service is designed to answer your most critical biological questions about RNA-protein interactions. Whether you're studying disease mechanisms, post-transcriptional regulation, or therapeutic targets, we offer a full-spectrum analytical workflow to meet your specific research objectives.
We help you pinpoint direct RNA-protein contact points with single-nucleotide resolution, enabling:
By integrating CLIP-Seq with quantitative modeling, we deliver:
We go beyond mapping by helping you interpret the biological relevance:
Our motif discovery pipeline enables:
To provide context and multi-omics depth, we offer:
We also support specialized requests, including:
At Creative Proteomics, we offer multiple CLIP-Seq variants to match your specific research needs:
Each method has unique strengths depending on your experimental goals, sample types, and the resolution required. Our team can help you choose the optimal approach for your project.
Sample Preparation & Crosslinking
Cell Lysis & RNA Fragmentation
Immunoprecipitation of RNA–Protein Complexes
RNA Linker Ligation & Reverse Transcription
Library Amplification & Sequencing
Bioinformatics Analysis
Illumina NovaSeq 6000 / NextSeq 2000
UV Crosslinking System (254 nm / 365 nm)
High-Specificity Immunoprecipitation Workflow
NovaSeq 6000 (Fig from Illumina)
Custom Bioinformatics Pipeline
Low-Input Sample Compatibility
NextSeq 2000 (Fig from Illumina)
| Sample Type | Required Amount | Notes |
| Cultured Cells | ≥ 1 × 10⁷ cells | Preferably in PBS, snap-frozen in liquid nitrogen or dry ice |
| Tissue Samples | ≥ 50 mg | Fresh-frozen or RNAlater preserved; avoid RNase contamination |
| Total RNA (for eCLIP) | ≥ 100 ng | RIN > 7 recommended; DNase-treated |
| Crosslinked Lysate | Equivalent to ≥ 1 × 10⁷ cells | Must be UV-crosslinked (254 nm), stored at -80°C |
| Antibodies (client-provided) | ≥ 5–10 μg (if applicable) | Must be IP-grade; monoclonal or polyclonal validated for CLIP preferred |
Notes:
| Feature / Client Need | CLIP-Seq (Standard) | PAR-CLIP | RIP-Seq | ChIRP / RAP / CHART |
| Resolution | Single-nucleotide resolution | Single-nucleotide (enhanced crosslink density) | Low (region-level, no crosslink site) | Varies (region-specific, depends on probe design) |
| Crosslinking Method | UV 254 nm (native RNA-protein complexes) | UV 365 nm + photoactivatable nucleosides (e.g., 4SU) | No crosslinking (native complexes preserved) | Formaldehyde (DNA/RNA crosslinking) |
| Binding Site Precision | High (direct crosslinked sites) | High (with enriched mutation/truncation signatures) | Moderate (indirect/co-bound RNAs may co-purify) | Low-to-moderate (depends on probe specificity) |
| Requires Antibody | Yes | Yes | Yes | No (uses probes targeting specific RNA/DNA) |
| Target Discovery (Finding Unknown RBPs/RNAs) | Suitable for transcriptome-wide screening | Particularly useful for high-turnover RNAs | Better suited for known RBP targets | Requires prior knowledge of RNA/DNA targets |
| Applicable to Low-Abundance Targets | Achievable with deep sequencing | Enhanced crosslinking increases sensitivity | Often limited by weak or nonspecific enrichment | Better when targeting abundant lncRNAs or repetitive elements |
| Input Requirement | Medium–High (≥10⁷ cells or ~50 mg tissue) | High (due to labeling step; requires metabolic activity) | Low–Medium (≥10⁶ cells) | Medium–High (depends on RNA abundance) |
| Motif Discovery Capability | Strong, enables sequence and structural motif identification | Excellent, due to crosslink-induced mutations | Limited | Not applicable |
| Bioinformatics Complexity | High; requires crosslink site detection, peak calling, motif analysis | High; mutation/truncation analysis required | Low–Moderate; standard RNA-seq pipelines | Moderate; mapping, enrichment analyses, background removal |
| Throughput / Scalability | High; suitable for genome-wide studies | High; genome-wide with increased sensitivity | Moderate; can be transcriptome-wide but prone to noise | Low–Moderate; focused on specific targets, not genome-wide |
| Quantitative Capability | Moderate–High; allows semi-quantitative binding strength estimation | High; enables quantification of binding dynamics | Moderate; influenced by nonspecific signals | Low; rarely used for quantitative binding assessment |
| Specificity / Background | High; captures direct interactions with minimal background | High; improved signal-to-noise ratio from photoreactive labeling | Moderate; high background due to indirect binding | Low–Moderate; depends on probe design and hybridization stringency |
| Compatible with Clinical / Fixed Samples | Not recommended; requires fresh or quickly frozen samples | Not compatible; needs metabolic labeling not feasible in clinical tissue | Sometimes feasible with frozen lysates or IP-ready extracts | Compatible with FFPE or fixed samples using specialized protocols |
| Sample Types | Cultured cells, fresh/frozen tissue | Cultured cells only (requires metabolic labeling) | Cultured cells, frozen or fixed tissues | Cultured cells, frozen tissues, FFPE, chromatin |
| Recommended Controls | Input RNA, mock IP, knockdown/knockout | Input RNA, mock IP, knockdown/knockout | Input RNA, mock IP | Scrambled probes, input RNA |
| Relative Time / Cost | High; labor-intensive protocol, requires deep sequencing | High; requires metabolic labeling and specialized reagents | Low–Moderate; simpler protocol, less expensive | High; extensive optimization, probe design, and validation required |
| Ideal For | Precise mapping of RBP binding sites transcriptome-wide | Studying dynamic, high-turnover RNA–RBP interactions | Validation of known RBP–RNA associations | Investigating RNA localization, lncRNA function, chromatin association |
| Limitations | Technically demanding; requires large input, deep sequencing, sensitive to RNA degradation | Limited to cell culture; not applicable to tissues or clinical samples | Lower resolution; high background; indirect binding complicates interpretation | Poor resolution; dependent on probe efficiency; requires significant optimization |
High-Resolution Binding Maps
Genome-wide RNA–protein binding sites
Single-nucleotide precision for exact interaction mapping
Motif Discovery Reports
De novo detection of sequence or structural motifs
Comparison with known RBP motifs for validation
Differential Binding Analysis
Binding differences across conditions (e.g. treated vs. control)
Identification of condition- or tissue-specific targets
Functional Enrichment Insights
Pathway and GO analysis to uncover regulatory networks
Integration with disease- or phenotype-related data
Data Integration Outputs
Overlay with RNA-Seq, miRNA-Seq, or ribosome profiling
Insights into splicing, mRNA stability, and translation control
Comprehensive Data Package
Raw sequencing data (FASTQ)
Processed alignments (BAM/BedGraph)
Peak files with binding coordinates
Custom interpretation tailored to your study
CLIP-Seq results: binding peaks, motifs, differential binding, and pathway analysis.
➤ Client Use Case
A client seeks to systematically profile multiple RNA-binding proteins (RBPs) across the human transcriptome to:
➤ Case Analysis
In this study, the authors leveraged eCLIP to map binding sites for 150 human RBPs as part of the ENCODE project. Key outcomes included:
Relevance for Clients:
Service Directions:
Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins
Can CLIP-Seq be performed on both coding and non-coding RNAs?
Yes. CLIP-Seq captures interactions across the entire transcriptome, including mRNAs, lncRNAs, pri-miRNAs, and other non-coding RNAs.
How much starting material do I need for a CLIP-Seq experiment?
Standard protocols typically require ~10⁷ cells or ~50 mg tissue, but we also offer low-input protocols starting from as little as 1×10⁶ cells or 100 ng total RNA.
Is CLIP-Seq suitable for studying dynamic changes in RNA–protein interactions?
Absolutely. CLIP-Seq can compare samples under different conditions (e.g., treated vs. control) to detect shifts in binding profiles, making it ideal for studying regulatory dynamics.
Does CLIP-Seq detect direct or indirect RNA–protein interactions?
Primarily direct interactions. The UV crosslinking step captures physical contact points between proteins and RNA, reducing false positives from indirect associations.
How does CLIP-Seq differ from RIP-Seq?
Unlike RIP-Seq, CLIP-Seq uses UV crosslinking to pinpoint direct binding sites with high resolution, while RIP-Seq may capture indirect or transient interactions without precise binding site localization.
Can CLIP-Seq help identify unknown RBPs binding to a specific RNA?
Not directly. CLIP-Seq is usually designed around a known RBP of interest. However, techniques like ChIRP-MS might be more suitable for discovering unknown RBPs binding to a specific RNA.
Are crosslinking conditions customizable?
Yes. We can optimize crosslinking parameters (UV intensity, wavelength) based on the RBP and cell type to improve efficiency and specificity.
Can CLIP-Seq data be integrated with epigenomic datasets?
Definitely. CLIP-Seq results can be compared with ChIP-Seq, ATAC-Seq, or DNA methylation profiles to explore multi-layered gene regulation.
Is CLIP-Seq analysis limited to human samples?
No. CLIP-Seq can be performed on samples from various organisms, provided there’s a suitable reference genome or transcriptome for alignment.
How reproducible are CLIP-Seq results across replicates?
We assess reproducibility using correlation metrics and irreproducible discovery rate (IDR) analysis, ensuring high confidence in detected binding sites.
Can you analyze public CLIP-Seq data instead of generating new data?
Yes. We offer bioinformatics-only services to re-analyze publicly available CLIP-Seq datasets, extract new insights, or integrate them with your own data.
What factors influence CLIP-Seq data quality the most?
Key factors include antibody specificity, crosslinking efficiency, RNA integrity, and sequencing depth. We provide guidance to optimize each step.
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