Last Updated: November 24, 2025 | Content Status: 2025 Latest Edition
Quality Control for Large Oligo Pools: 2025 Complete Guide
What is quality control for large oligo pools? It's a systematic process of validating thousands of oligonucleotide sequences before synthesis to ensure experimental success. This comprehensive QC workflow identifies problematic sequences (extreme GC content, homopolymers, secondary structures), predicts synthesis success rates, and estimates pool uniformity to minimize dropout. By combining batch sequence analysis, synthesis error prediction, and uniformity estimation, researchers can optimize pool composition, reduce experimental failures, and maximize synthesis efficiency for large-scale applications like NGS library preparation and multiplexed assays.
Key Takeaways
- ✓5-step QC pipeline: sequence preparation, batch QC, synthesis estimation, uniformity check, and export
- ✓Acceptable metrics: GC 30-70%, dropout <10%, CV <30%, sequencing depth 500-1000x
- ✓Batch processing supports up to 10,000 sequences with comprehensive validation
- ✓Predict synthesis success before ordering using error rate calculations
- ✓Reduce dropout by optimizing pool size, amount, and sequence composition
- ✓Post-synthesis NGS validation confirms predicted quality metrics
Why Quality Control Matters for Large Oligo Pools
Large oligonucleotide pools—containing hundreds to thousands of unique sequences—are essential for modern molecular biology applications including next-generation sequencing (NGS) library preparation, CRISPR screening libraries, multiplex PCR assays, and high-throughput functional genomics. However, without proper quality control, these pools can suffer from high dropout rates, synthesis failures, and non-uniform representation that compromise experimental results.
Quality control for large oligo pools addresses three critical challenges: (1) sequence-level issues—identifying problematic sequences that fail synthesis or cause amplification bias; (2) synthesis efficiency—predicting how many sequences will be synthesized successfully; and (3) pool uniformity—ensuring all sequences are represented at similar concentrations after synthesis and amplification.
The cost of poor QC can be substantial. A pool with 20% dropout means 200 out of 1,000 sequences are missing, potentially invalidating entire experimental datasets. By implementing comprehensive QC workflows before synthesis, researchers can identify and remove problematic sequences, optimize pool composition, and predict outcomes accurately—saving time, money, and experimental resources.
The Evolution of Oligo Pool QC in 2025
Modern oligo pool QC has evolved significantly from simple sequence checks. Early approaches relied on basic rules: avoid extreme GC content, limit homopolymers, ensure minimum length. Today's QC workflows integrate sophisticated algorithms for batch sequence analysis, synthesis error modeling, uniformity prediction, and post-synthesis validation using next-generation sequencing.
The 2025 approach emphasizes predictive QC—using computational tools to forecast synthesis outcomes before ordering. This shift enables researchers to optimize pool design proactively, reducing the need for expensive re-synthesis and experimental troubleshooting. Advanced QC tools now account for modern synthesis technologies (array-based vs column-based), sequence complexity metrics, and statistical models for dropout prediction.
Worked Example: 500-Oligo Pool QC Workflow
Scenario
You're designing a CRISPR sgRNA library targeting 500 genes. Each gene gets 1 sgRNA (20 nt guide + 80 nt scaffold = 100 nt total). You need to validate pool quality before ordering from Twist Bioscience (array-based synthesis).
Initial Pool Size
500 oligos
Oligo Length
100 nt
Synthesis Method
Array-based
1Batch Sequence QC Results
Upload 500 sequences to Batch Sequence QC tool
- 15 sequences: GC content <30% or >70%
- 8 sequences: Homopolymers ≥4 bp
- 12 sequences: Strong secondary structures (ΔG < -5 kcal/mol)
- 3 sequences: Multiple issues (overlap)
Decision: Remove 32 flagged sequences (6.4% of pool)
Remaining: 468 sequences pass QC
2Synthesis Quality Estimation
Use Error Rate Calculator with parameters:
Synthesis method: Array-based
Coupling efficiency: 98.5% (typical for arrays)
Calculation: Full-length % = (0.985)^100 = 22.1%
This means ~22% of each oligo will be full-length, the rest will be truncated. For pooled CRISPR libraries, this is acceptable with downstream purification.
3Pool Uniformity Prediction
Use Pool Uniformity Estimator:
Total amount ordered: 100 nmol (recommended for this pool size)
Expected amount per oligo: 100 nmol / 468 = 0.214 nmol each
Predicted metrics for array synthesis:
Expected CV: 25-30% (acceptable for CRISPR screening)
Sequences expected to dropout: ~40-55 sequences
Interpretation: With 468 sequences ordered, expect ~420-430 sequences to be present at usable concentrations. This is acceptable for 500-gene coverage with some redundancy.
4Final Decision & Ordering
✅ Quality Assessment: ACCEPTABLE
- • 93.6% of sequences pass pre-synthesis QC (468/500)
- • Expected post-synthesis success: ~88-92% (420-430/468)
- • Final gene coverage: ~84-86% (420-430/500 genes)
- • Synthesis quality (22% full-length) acceptable with purification
📋 Order Specifications:
- • Pool size: 468 sequences (after QC removal)
- • Total amount: 100 nmol
- • Format: FASTA file with sequence IDs matching gene names
- • Special instructions: Request PAGE purification for full-length enrichment
- • Estimated cost: ~$230-470 ($0.50-1.00 per oligo for this pool size)
🔬 Post-Synthesis Validation Plan:
- • Perform NGS sequencing: MiSeq Nano (~1M reads, estimated $200-400 reagent cost)
- • Target depth: 1,000-2,000× per sequence
- • Validate actual dropout rate vs predicted 8-12%
- • Identify any systematic failures (GC bias, specific sequence patterns)
- • Document results to refine future QC thresholds
💡 Key Lessons from This Example
- →Pre-synthesis QC removed 6.4% of problematic sequences, preventing synthesis failures
- →Predictive modeling set realistic expectations: 84-86% final coverage vs 100% naive expectation
- →Adequate pool amount (100 nmol) provides ~0.21 nmol per oligo, reducing dropout risk
- →NGS validation budget ($200-300) is small compared to synthesis cost ($230-470)
- →PAGE purification improves full-length percentage from 22% to 60-80%, worth the added cost
Complete QC Workflow Overview
Our comprehensive quality control pipeline consists of five sequential steps, each addressing a critical aspect of pool validation. Follow this workflow to ensure optimal pool quality before synthesis.
Prepare Sequences
Batch QC
Estimate Quality
Check Uniformity
Export & Order
Step-by-Step QC Workflow
Prepare Your Sequences
Format your sequences for batch analysis. Supported formats include FASTA, CSV, or plain text. Ensure sequences are clean (no ambiguous bases unless necessary) and properly labeled for traceability. For individual sequence validation, use Oligo Properties Calculator to check GC content, Tm, and molecular weight.
📋 Format Requirements:
- FASTA: Each sequence starts with >header, sequence on next line(s)
- CSV: Columns: id, sequence (or name, sequence)
- Plain Text: One sequence per line
- Maximum: 10,000 sequences per batch
- Remove ambiguous bases (N, R, Y, etc.) unless functionally required
Example FASTA Format:
Batch Sequence QC
Run comprehensive quality checks on all sequences using Batch Sequence QC. This tool analyzes thousands of sequences simultaneously, flagging problematic sequences based on multiple criteria including GC content, homopolymer runs, sequence complexity, and predicted secondary structures.
📋 Instructions:
- Navigate to Batch Sequence QC
- Upload your FASTA/CSV file or paste sequences directly
- Review flagged sequences: GC extremes, homopolymers, low complexity
- Export QC report (CSV or Excel format) for detailed analysis
- Document sequences removed and reasons for removal
QC Criteria Summary:
| Parameter | Acceptable Range | Ideal Range | Action if Out of Range |
|---|---|---|---|
| GC Content | 30-70% | 40-60% | Remove or redesign |
| Homopolymers | ≤3 bp | ≤2 bp | Remove if ≥4 bp |
| Sequence Length | 20-200 nt | 40-150 nt | Review if outside range |
| Secondary Structure (ΔG) | >-5 kcal/mol | >-3 kcal/mol | Remove if ΔG <-5 |
| Complexity | Moderate-High | High | Review low complexity |
🚫 Sequences to Remove:
- 1. Extreme GC content (<20% or >80%) - verify with GC Content Analyzer
- 2. Long homopolymers (AAAA, TTTT, GGGG, CCCC - 4+ repeats)
- 3. Very low complexity (e.g., repetitive patterns)
- 4. Sequences with predicted strong secondary structures (ΔG <-5 kcal/mol) - check with Secondary Structure Predictor
- 5. Sequences shorter than 20 nt or longer than 200 nt (unless required)
✅ Keep Sequences With:
- GC content: 30-70% (ideal: 40-60%) - analyze with GC Analyzer
- No homopolymers longer than 3 bp
- Length: 20-200 nt (depending on synthesis method)
- No strong secondary structures (ΔG > -5 kcal/mol)
- Moderate to high sequence complexity
- For primers, also check Tm consistency using Tm Calculator
Estimate Synthesis Quality
Predict expected synthesis success rate using the Error Rate Calculator. This tool models synthesis efficiency based on oligo length, coupling efficiency, and synthesis method (array-based vs column-based). Understanding expected full-length product percentages helps set realistic expectations and optimize pool design.
📋 Instructions:
- Navigate to Error Rate Calculator
- Enter your average oligo length (or calculate from your sequences)
- Select synthesis method (array-based vs column-based)
- Enter coupling efficiency (default: 98.5% for arrays, 99.5% for columns)
- Review expected full-length product percentage
- Compare results across different length ranges if pool is heterogeneous
Typical Synthesis Quality Results:
| Method | Oligo Length | Coupling Efficiency | Full-Length % | Application Suitability |
|---|---|---|---|---|
| Array-Based | 100 nt | 98.5% | ~22% | Acceptable with purification |
| Array-Based | 150 nt | 98.5% | ~8% | Requires purification or redundancy |
| Array-Based | 200 nt | 98.5% | ~4% | Not recommended without purification |
| Column-Based | 100 nt | 99.5% | ~61% | Excellent for most applications |
| Column-Based | 150 nt | 99.5% | ~47% | Good for most applications |
| Column-Based | 200 nt | 99.5% | ~37% | Acceptable with purification |
* Full-length product percentage decreases exponentially with length. Array-based synthesis is cost-effective for large pools but requires downstream purification or design redundancy. Column-based synthesis offers higher quality but at higher cost.
Major Oligo Pool Synthesis Vendors (2025):
| Vendor | Technology | Max Pool Size | Max Length | Typical Uniformity | Notes |
|---|---|---|---|---|---|
| Twist Bioscience | Silicon array | 300,000 | 300 nt | CV ~20-30% | High capacity, NGS-validated pools |
| Agilent | Inkjet array | 244,000 | 230 nt | CV ~25-35% | Established platform, SureSelect |
| IDT (Integrated DNA Technologies) | Array/Column | 100,000 | 300 nt | CV ~15-25% | Flexible options, xGen libraries |
| GenScript | Array | 50,000 | 200 nt | CV ~20-30% | Cost-effective, custom projects |
| CustomArray | Array | 200,000 | 200 nt | CV ~25-35% | Specialized arrays, research focus |
** Specifications reflect typical capabilities as of 2024-2025 and may vary by product line, synthesis conditions, and regional availability. Always verify current specifications and pricing directly with vendors. Typical turnaround: 2-4 weeks. Cost estimates: $0.05-0.50+ per oligo depending on pool size, length, purification requirements, and volume discounts.
⚠️ Important Considerations:
- Low full-length percentages (<10% for arrays) mean most oligos will be truncated
- This is acceptable for pooled applications IF you use downstream purification (PAGE, HPLC)
- Design with redundancy (multiple oligos per target) to compensate for dropout
- For applications requiring high purity, consider column-based synthesis or shorter oligos
- Factor synthesis quality into experimental design and statistical power calculations
Check Pool Uniformity
Estimate expected uniformity and dropout rate using the Pool Uniformity Estimator. Uniformity metrics predict how evenly sequences will be represented after synthesis and amplification. Poor uniformity leads to biased results and reduced experimental power, making this step critical for large pools.
📋 Instructions:
- Navigate to Pool Uniformity Estimator
- Enter pool size (number of unique oligos after QC)
- Enter total amount (nmol or µg) you plan to order
- Select synthesis method (affects expected uniformity)
- Review predicted dropout rate and uniformity metrics
- Adjust pool size or amount if dropout rate is unacceptable
Uniformity Quality Standards:
| Metric | Excellent | Acceptable | Poor (Action Required) |
|---|---|---|---|
| Dropout Rate | <5% | 5-10% | >10% |
| Coefficient of Variation (CV) | <20% | 20-30% | >30% |
| Sequencing Depth | >1000x | 500-1000x | <500x |
| Coverage Uniformity | >90% at 100x | 80-90% at 100x | <80% at 100x |
✅ Good Uniformity Indicators:
- Dropout rate: <10% (acceptable), <5% (excellent)
- Coefficient of variation (CV): <30% (acceptable), <20% (excellent)
- Minimum sequencing depth: 500-1000x per oligo for validation
- Coverage uniformity: >80% of sequences at 100x depth
- Even distribution across GC content ranges
🚫 Poor Uniformity Warning Signs:
- Dropout rate: >20% → Too many sequences, insufficient amount
- High CV: >50% → Synthesis bias or insufficient mixing
- Low sequencing depth: <100x → Inadequate for QC validation
- Skewed GC distribution → GC bias in synthesis or amplification
- Uneven coverage → Systematic bias requiring investigation
Final Review & Export
Compile all QC results and prepare final sequences for synthesis order. This step ensures all quality checks are complete, documentation is thorough, and sequences are formatted correctly for your chosen vendor. Proper documentation facilitates post-synthesis validation and troubleshooting.
Final QC Checklist:
📋 Export Formats:
- FASTA: Standard format for most vendors, includes headers and sequences
- CSV: Include metadata (IDs, gene names, QC metrics) for tracking
- Excel: Comprehensive QC report with all metrics and flags
- Vendor Format: Check vendor requirements - major providers include Twist, IDT, Agilent, GenScript (see vendor comparison table above)
- NGS Libraries: For library prep applications, see our NGS Library Preparation guide
Post-Synthesis NGS Validation Protocol
Next-generation sequencing (NGS) validation is the gold standard for verifying oligo pool quality after synthesis. This protocol quantifies actual dropout rates, identifies failed sequences, validates uniformity predictions, and provides data for refining future QC workflows.
Recommended for: Pools >100 sequences, critical applications requiring >95% success rate, method development, and vendor qualification.
NGS QC Workflow
Library Preparation
Add Illumina adapters via PCR (8-12 cycles max to minimize bias)
Input amount: 10-100 ng of oligo pool
Indexing: Use unique barcodes for multiplexing
Purification: AMPure XP beads (0.8×) to remove primer dimers
Sequencing Parameters
Platform: Illumina MiSeq, NextSeq, or NovaSeq
Read length: Match oligo length (e.g., 2×150 for 100-200 nt oligos)
Depth: Target 500-1000× per unique oligo minimum
PhiX spike-in: 5-10% for quality control
Data Analysis Pipeline
Quality filtering: Q30 > 80%, remove low-quality reads
Adapter trimming: Cutadapt or Trimmomatic
Alignment: Bowtie2 or BWA to reference sequences
Count quantification: HTSeq or custom scripts
Metrics Calculation
Dropout rate: % of sequences with <10 reads
CV calculation: (StdDev / Mean) × 100
Coverage uniformity: % at 100× depth threshold
GC bias analysis: Coverage vs GC content plot
📊 Sequencing Depth Calculation
Estimated cost (2024-2025): MiSeq Nano (~1M reads): ~$200-400 | MiSeq v2 (~15M reads): ~$600-1000 (reagents only, varies by region and volume)
Multiplexing strategy: Pool multiple samples with unique barcodes to reduce per-sample cost
Interpreting NGS Results
| Observation | Likely Cause | Action |
|---|---|---|
| High dropout (>20%) | Synthesis failure, insufficient amount | Increase pool amount, reduce pool size, tighten pre-QC |
| High CV (>50%) | Amplification bias, synthesis bias | Reduce PCR cycles, optimize annealing temp, use digital PCR |
| GC-rich oligos under-represented | PCR bias, synthesis coupling inefficiency | Use GC-enhanced polymerase, lower annealing temp |
| AT-rich oligos over-represented | Preferential amplification | Increase annealing temp, reduce extension time |
| Specific sequences always fail | Secondary structure, synthesis difficulty | Redesign sequences, check for hairpins/dimers |
📈 Recommended QC Plots
- 1. Read count distribution histogram: Shows uniformity, identifies outliers
- 2. Coverage vs GC content scatter: Reveals GC bias in synthesis/amplification
- 3. Rank-order plot: Expected (uniform) vs observed counts, visualizes dropout
- 4. Lorenz curve: Quantifies representation inequality (Gini coefficient)
- 5. Per-base quality heatmap: Identifies positional synthesis errors
Tools: R (ggplot2), Python (matplotlib/seaborn), or specialized packages like QoRTs for NGS QC
Best Practices for Large Oligo Pool QC
Pre-QC During Design
Design sequences with QC criteria in mind from the start. Use design algorithms that incorporate GC content limits, homopolymer avoidance, and secondary structure prediction. This reduces the need for redesign and removal later, saving time and improving pool quality.
Validate Post-Synthesis
Always perform NGS QC after synthesis to verify actual uniformity and identify sequences that failed synthesis. Compare predicted vs actual dropout rates to refine QC thresholds. Document discrepancies to improve future predictions.
Monitor Key Metrics
Track dropout rate, GC distribution, and uniformity across multiple pools to identify systematic issues with your design pipeline. Maintain QC databases to learn which sequence features correlate with synthesis success in your applications.
Balance Quality vs. Cost
Stricter QC reduces dropout but may remove functional sequences. Adjust thresholds based on your application's tolerance for dropout. For critical applications, prioritize quality; for exploratory screens, accept higher dropout to maximize coverage.
Iterative Refinement
Use post-synthesis validation results to refine QC criteria. If certain sequence features consistently fail, tighten thresholds. If borderline sequences perform well, consider relaxing criteria to maximize pool diversity.
Document Everything
Maintain detailed records of QC decisions, removed sequences, predicted metrics, and actual results. This documentation enables troubleshooting, method refinement, and reproducibility across projects and collaborators.
Understanding Dropout at the Molecular Level
Sequence dropout and synthesis failure occur through well-defined chemical mechanisms. Understanding these molecular processes enables rational design choices and explains why certain QC thresholds exist.
Primary Synthesis Failure Modes
1. Coupling Inefficiency
Each phosphoramidite coupling cycle has ~98.5-99.5% efficiency. Failed couplings result in chain termination or deletion sequences. The probability of full-length synthesis is (coupling efficiency)^n where n = oligo length.
2. Depurination Events
Acid-labile glycosidic bonds between purines (A, G) and deoxyribose can cleave during deprotection steps. Depurination creates abasic sites that cause strand breaks, particularly in homopurine runs.
3. Secondary Structure Interference
Strong intramolecular base pairing (hairpins, self-dimers) sterically hinders phosphoramidite access to the 5'-hydroxyl group. Structures with ΔG < -5 kcal/mol at synthesis temperature (room temp) reduce coupling efficiency by 5-20%.
4. Capping Failure and Deletion Sequences
Uncapped failure sequences (where coupling failed but capping also failed) continue through synthesis cycles, creating deletion mutants (n-1, n-2, etc.). These deletion products compete during amplification, reducing target representation.
Why GC Content Matters: Thermodynamic Basis
GC Content vs Synthesis Success Rate
Synthesis success rate shows a bell-shaped relationship with GC content. Extreme GC values (<30% or >70%) significantly reduce synthesis efficiency due to thermodynamic and kinetic factors.
GC-Rich Sequences (>70%)
- • 3 hydrogen bonds per GC pair vs 2 for AT → stronger stacking
- • Higher Tm: +0.5°C per GC→AT swap, causing synthesis issues
- • Aggregation: GC-rich oligos form intermolecular G-quartets
- • PCR bias: Under-amplified in standard PCR conditions
- • Synthesis failure: Significantly higher dropout vs balanced GC (reported 2-5× in literature)
AT-Rich Sequences (<30%)
- • Lower Tm: Unstable during primer annealing steps
- • Homopolymer tendency: AT runs more common, depurination risk
- • PCR bias: Over-amplified, dominate pool composition
- • Synthesis issues: TTT/AAA runs increase deletion frequency
- • Uniformity impact: Skew final pool distribution
Optimal GC range (40-60%): Balances synthesis efficiency, thermodynamic stability, and amplification uniformity. Within this range, coupling efficiency is ~99.0±0.2%, secondary structures are minimal (ΔG > -3 kcal/mol typical), and PCR bias is <2-fold.
Quantitative Dropout Prediction Model
Dropout probability for a given sequence can be modeled as a function of multiple factors:
Pamplification_fail = f(GC, Tm, structure)
Psequence_loss = concentration_effect × pool_size_factor
Example calculation for problematic sequence:
100 nt, GC=75%, 4-bp G-run, ΔG=-6 kcal/mol hairpin
• Psynthesis_fail ≈ 1 - (0.983)^100 = 83% (reduced coupling from GC+structure)
• Pamplification_fail ≈ 40% (GC bias in PCR)
• Combined dropout ≈ 92% → Almost certain failure
Example for optimal sequence:
100 nt, GC=50%, no homopolymers >3 bp, ΔG=-2 kcal/mol
• Psynthesis_fail ≈ 1 - (0.9985)^100 = 14% (high coupling)
• Pamplification_fail ≈ 5% (minimal bias)
• Combined dropout ≈ 18% → 82% success probability
Troubleshooting Common QC Issues
Problem: High Dropout Rate (>20%)
High dropout indicates insufficient representation of sequences in the pool. This compromises experimental power and can invalidate results.
Solutions:
- Reduce pool size (fewer sequences = more amount per oligo)
- Order more total amount (e.g., 100 nmol instead of 50 nmol)
- Remove sequences with extreme GC or homopolymers before synthesis
- Consider column-based synthesis for critical sequences
- Use equimolar pooling with concentration normalization
Problem: Many Sequences Flagged in QC
If a large proportion of sequences fail QC, the design pipeline may need optimization or thresholds may be too strict.
Solutions:
- Relax QC thresholds if sequences are borderline (e.g., GC 29% instead of 30%)
- Redesign sequences with alternative algorithms or parameters
- Accept flagged sequences if they're functionally necessary (monitor closely)
- Review design constraints that may be causing systematic issues
- Consider application-specific tolerance for suboptimal sequences
Problem: Poor Uniformity (High CV)
High coefficient of variation indicates uneven representation, leading to biased results and reduced statistical power.
Solutions:
- Ensure adequate mixing during resuspension (vortex, pipette mixing)
- Use equimolar pooling (normalize concentrations before pooling)
- Reduce PCR cycles if amplifying before use (PCR bias)
- Consider digital PCR for more uniform amplification
- Investigate GC bias in synthesis or amplification steps
- Increase sequencing depth to compensate for uneven coverage
Problem: Discrepancy Between Predicted and Actual Dropout
If actual dropout significantly exceeds predictions, synthesis conditions or QC models may need adjustment.
Solutions:
- Review synthesis vendor specifications and actual coupling efficiency
- Check for vendor-specific issues (contact vendor if dropout is unusually high)
- Validate QC predictions with pilot pools before large orders
- Adjust error rate calculator parameters based on historical data
- Consider vendor-specific synthesis characteristics in predictions
Frequently Asked Questions
What is quality control for large oligo pools?▾
How do you perform QC on large oligo pools?▾
What are acceptable QC metrics for oligo pools?▾
How do you reduce dropout rate in oligo pools?▾
What tools are needed for oligo pool QC?▾
How do you validate oligo pool quality after synthesis?▾
Scientific References & Further Reading
Our QC workflows are based on established scientific methods and algorithms. For detailed information about the underlying calculations, visit our Scientific References page, which includes citations for key algorithms like the SantaLucia nearest-neighbor method for Tm calculation and synthesis error modeling approaches.
For authoritative information on oligo pool design and QC principles, consult established protocols such as those published by NCBI protocols and molecular biology handbooks. Large-scale oligo pool synthesis and QC methods are documented in publications from leading research institutions, including guidance from Addgene CRISPR resources and NGS library preparation protocols from sequencing platform manufacturers.
Related Resources
PCR Primer Design Workflow
Learn step-by-step PCR primer design using Tm Calculator, GC Analyzer, and Secondary Structure Predictor.
CRISPR Library Design
Design and validate CRISPR sgRNA libraries with coverage calculation and QC workflows.
Batch Sequence QC Tool
Analyze up to 10,000 sequences simultaneously for quality issues and problematic features.
Pool Uniformity Estimator
Estimate dropout rates and concentration variations for oligo pools before synthesis.
Error Rate Calculator
Predict synthesis success rates and full-length product percentages based on oligo length and method.
Tutorials & Guides
Access comprehensive tutorials for all OligoPool.com tools and workflows.
Ready to Start Your QC Workflow?
Begin quality control for your large oligo pool using our comprehensive tools. Start with Batch Sequence QC to validate your sequences, then use Pool Uniformity Estimator to predict dropout rates. Need help? Explore our tutorials or contact us for personalized guidance.