What uniformity level should I aim for in my oligo pool?

Target uniformity depends on application: CRISPR screens need 90%+ within 5-10 fold for statistical power. Gene synthesis tolerates 80%+ within 10-20 fold. Capture/enrichment requires 85%+ within 10 fold. DNA data storage needs 95%+ within 5 fold for reliable decoding.

How much oligo dropout is acceptable?

Acceptable dropout depends on redundancy: With no redundancy, aim for <1% dropout. With 2-3x redundancy, up to 10% is manageable. Array synthesis typically shows 2-5% dropout, while column synthesis achieves 0.5-1% dropout.

How do I calculate NGS QC sequencing depth for oligo pools?

Use formula: Pool Size × 30 × Fold-Difference × 1.5. The 30x ensures reliable quantification per oligo, fold-difference accounts for concentration variation, and 1.5x provides safety margin. Example: 10,000 oligos with 10-fold variation needs 4.5M reads.

What is the difference between array-based and column-based oligo synthesis uniformity?

Array synthesis (Twist, Agilent): 85% within 10-fold uniformity, 2-5% dropout, best for large pools (1K-1M oligos). Column synthesis (IDT, GenScript): 95% within 5-fold uniformity, 0.5-1% dropout, best for small high-precision pools (<1K oligos).

Oligo Pool Uniformity & Dropout Calculator

Calculate oligo pool dropout rate, synthesis uniformity (CV%), and NGS QC sequencing depth. Compare synthesis platforms (Twist, Agilent, IDT, GenScript) and predict concentration variation for CRISPR libraries, gene synthesis, and capture applications. Free calculator with 2025 vendor data.

Input Parameters

Pool Size (Number of Unique Oligos) *

Range: 10 - 1,000,000. The total number of different oligonucleotides in your pool.

Synthesis Platform *

Array-Based Synthesis

High-throughput chip synthesis (Twist, Agilent). Typical uniformity: 85% within 10-fold. Expected dropout: ~2-5%.

Column-Based Synthesis

Traditional synthesis with pooling (IDT, GenScript). Typical uniformity: 95% within 5-fold. Expected dropout: ~0.5-1%.

Target Uniformity Metric

Defines acceptable concentration variation. "90% within 10-fold" means 90% of oligos are within 10× of each other in concentration.

💡 Understanding Uniformity

• Dropout Rate: % of designed oligos that fail to synthesize or amplify
• Fold-Difference: Concentration variation range (e.g., 10-fold = 10× difference)
• Uniformity: % of oligos within acceptable concentration range
• Higher uniformity reduces experimental bias and improves reproducibility

Results

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Enter parameters and click"Estimate Uniformity"

Understanding Pool Uniformity Estimation

Step-by-Step Usage Guide

Step 1: Enter Pool Size

Input the total number of unique oligonucleotides in your pool. This value ranges from 10 to 1,000,000 oligos. For example, a CRISPR library targeting 20,000 genes would have a pool size of 20,000 (assuming one gRNA per gene).

Step 2: Select Synthesis Platform

Choose between array-based synthesis (high-throughput chip synthesis like Twist Bioscience or Agilent) or column-based synthesis (traditional individual synthesis with pooling, such as IDT or GenScript). The platform selection significantly impacts expected uniformity and dropout rates.

Step 3: Set Target Uniformity

Define your acceptable uniformity metric. Common targets include"90% within 10-fold" (standard),"85% within 10-fold" (relaxed),"95% within 5-fold" (stringent), or"80% within 20-fold" (very relaxed). This metric determines what percentage of oligos should fall within a specified concentration range.

Step 4: Calculate and Review Results

Click"Estimate Uniformity" to generate predictions. Review the expected dropout rate, concentration variation (fold-difference), uniformity score, and recommended QC sequencing depth. Compare platform options to optimize your experimental design.

Real-World Calculation Examples

Example 1: CRISPR Screen Library (50,000 oligos)

Input: Pool size = 50,000, Synthesis method = Array-based, Target uniformity = 85% within 10-fold

Expected Results: Dropout rate ≈ 3-4%, Fold-difference ≈ 10-12×, Uniformity ≈ 85-88% within target, Recommended QC depth ≈ 22-25 million reads

Interpretation: This pool size is typical for genome-wide CRISPR screens. With 3-4% dropout, expect 1,500-2,000 missing gRNAs. Plan for 2-3× redundancy (design 2-3 gRNAs per gene) to ensure target coverage. QC sequencing at 25M reads will provide ~500× average coverage, sufficient to detect low-abundance oligos.

Example 2: Gene Synthesis Pool (5,000 oligos)

Input: Pool size = 5,000, Synthesis method = Column-based, Target uniformity = 95% within 5-fold

Expected Results: Dropout rate ≈ 0.5-1%, Fold-difference ≈ 4-6×, Uniformity ≈ 95-97% within target, Recommended QC depth ≈ 1.5-2 million reads

Interpretation: Column-based synthesis provides excellent uniformity for smaller pools. With <1% dropout, only 25-50 oligos may be missing, which is acceptable for gene assembly applications. The 5-fold variation is manageable as long as all fragments are present above detection threshold.

Example 3: Large-Scale Capture Library (200,000 oligos)

Input: Pool size = 200,000, Synthesis method = Array-based, Target uniformity = 80% within 15-fold

Expected Results: Dropout rate ≈ 8-12%, Fold-difference ≈ 15-20×, Uniformity ≈ 75-80% within target, Recommended QC depth ≈ 90-120 million reads

Interpretation: Very large pools show increased variation. Expect 16,000-24,000 dropouts and significant concentration differences. For capture applications, this may require computational normalization or selective amplification strategies. Consider splitting into smaller sub-pools or using redundancy to improve reliability.

Understanding Your Results

Expected Dropout Rate

This percentage indicates how many oligonucleotides may completely fail to synthesize or amplify. A dropout rate of 3% means 3 out of every 100 designed oligos will be absent from the final pool. Dropouts are critical failures - they cannot be recovered post-synthesis without re-ordering individual oligos.

• <1%: Excellent - suitable for applications requiring all oligos
• 1-3%: Good - acceptable with redundancy or for tolerant applications
• 3-5%: Moderate - requires redundancy or tolerance for missing oligos
• >5%: High - plan for significant redundancy or consider alternative platforms

Fold-Difference (Concentration Variation)

This metric represents the range of concentration differences between the highest and lowest abundance oligos. A 10-fold difference means the most abundant oligo is 10 times more concentrated than the least abundant. Lower fold-differences indicate better uniformity.

• 3-5×: Excellent uniformity - ideal for quantitative applications
• 5-10×: Good uniformity - suitable for most applications
• 10-15×: Moderate uniformity - may require normalization
• >15×: Poor uniformity - consider redesign or platform change

Recommended NGS QC Sequencing Depth

This value indicates the minimum number of sequencing reads needed to reliably validate pool uniformity and detect dropouts. The calculation accounts for pool size, expected variation, and ensures ≥30× coverage of even the lowest-abundance oligos. Formula: Pool Size × 30 × Fold-Difference × 1.5

Practical NGS QC Depth Examples (2025)

Pool Size	Platform	Fold-Diff	Required Reads	NGS Run	Approx Cost
1,000	IDT	5×	225K	MiSeq Nano	$300-500
5,000	GenScript	5×	1.1M	MiSeq v2	$500-700
10,000	Twist	10×	4.5M	MiSeq v3	$800-1,200
50,000	Twist	12×	27M	NextSeq 550	$1,500-2,500
200,000	Agilent	15×	135M	NovaSeq 6000	$3,000-5,000

*2025 pricing estimates. Actual costs vary by provider and multiplexing. Consider multiplexing multiple pools to reduce per-pool cost.

Critical: QC sequencing is non-negotiable for critical experiments (therapeutics, publications, CRISPR screens). It validates synthesis quality, identifies dropouts, and enables computational correction. The cost ($300-5,000) is minimal compared to wasted experimental time ($10,000+) and reagents if you proceed with a failed pool.

PCR Amplification Bias Impact on Pool Uniformity

PCR amplification is a major source of oligo pool non-uniformity. Even with perfectly uniform synthesis, PCR introduces bias based on GC content, secondary structure, and primer binding efficiency.

PCR Cycle vs Uniformity Loss

PCR Cycles	Expected Fold-Difference	CV% Increase	Recommendation
5-8 cycles	3-5×	+10-20%	Ideal - minimal bias
10-12 cycles	5-10×	+20-35%	Acceptable for most applications
15-18 cycles	10-20×	+40-60%	Significant bias - use if necessary
>20 cycles	20-100×	+70-150%	Severe bias - avoid if possible

Strategy: Always minimize PCR cycles. Use qPCR to determine optimal cycle number - stop amplification in early exponential phase. Use high-fidelity polymerases (Q5, KAPA HiFi) that show reduced GC bias compared to Taq.

Calculation Methodology and 2025 Standards

The uniformity estimation algorithm is based on 2025 industry standards and empirical data from major synthesis platforms (Twist Bioscience, Agilent SurePrint, IDT xGen, GenScript). The calculations incorporate platform-specific coupling efficiencies, amplification biases, and sequence-dependent effects observed in large-scale oligo pool production.

Key Formula Components (2025 Updated)

Dropout Rate Calculation: Based on platform-specific failure rates (Twist/Agilent: 2-5%, IDT/GenScript: 0.5-1.5%) adjusted for pool size and sequence complexity. Larger pools (>50K) and complex sequences (high GC, secondary structures) increase dropout probability exponentially. Calculate synthesis error impact with our Error Rate Calculator.
Concentration Variation (CV%): Derived from synthesis efficiency distributions and PCR amplification bias. Array platforms show 45-75% CV (5-20× fold-difference), while column platforms achieve 20-40% CV (3-10× fold-difference). PCR cycle number is the dominant post-synthesis factor affecting variation.
NGS QC Sequencing Depth: Uses the formula: Pool Size × 30 × Fold-Difference × 1.5. The 30× multiplier ensures reliable quantification per oligo (minimum for statistical significance), fold-difference accounts for concentration spread (low-abundance oligos need more reads), and 1.5× provides safety margin for sampling variation (updated per 2025 NGS QC guidelines).

These calculations align with 2025 best practices from leading synthesis vendors and reflect current understanding of oligo pool uniformity factors. For detailed methodology and references, see our Scientific References page.

Related Resources

Learn more about oligo pool design and quality control:

• Batch Sequence QC Tool - Identify problematic sequences before synthesis
• Error Rate Calculator - Estimate synthesis error rates
• Oligo Pool QC Workflow - Complete quality control guide
• User Guide - Comprehensive design strategies

Frequently Asked Questions

Target uniformity depends on your application:

CRISPR Screens (Quantitative): Aim for 90%+ within 5-10 fold. Poor uniformity causes statistical power loss and false negatives.
Gene Synthesis: 80%+ within 10-20 fold is usually acceptable. Assembly is tolerant of some variation as long as all fragments are present.
Capture/Enrichment: 85%+ within 10 fold recommended. Uneven capture reduces coverage uniformity in target regions.
DNA Data Storage: 95%+ within 5 fold ideal. High uniformity ensures reliable decoding and error correction.

General rule: If your experiment is quantitative or requires all oligos to work equally, aim for <10-fold variation. Qualitative applications can tolerate more variation.

For comprehensive quality control workflows, check our Oligo Pool QC workflow or use the Batch Sequence QC tool.

Acceptable dropout depends on redundancy and application:

With No Redundancy: <1% dropout is ideal. Even 2-5% can be problematic if you need every oligo (e.g., single gRNA per gene).
With 2-3× Redundancy: Up to 10% dropout is manageable. Majority of targets will still have at least one working oligo.
With 5× Redundancy: Can tolerate 20%+ dropout. Multiple backups ensure representation.

Strategy: For array synthesis (2-5% expected dropout), design 2-3 oligos per target. For column synthesis (0.5-1% dropout), single oligos are often sufficient.

Estimate synthesis error rates with our Error Rate Calculator or check the User Guide for design strategies.

It depends on the criticality of your application:

Always QC for: CRISPR screens, therapeutic oligos, commercial products, publishable research. The cost of QC (<$1000 for 5M reads) is trivial compared to wasted experiments.
Can Skip for: Preliminary experiments, internal tool development, non-critical applications where failure is acceptable.
Partial QC Option: For very large pools (>100K oligos), sample sequencing (10-20% of recommended depth) can identify major uniformity issues at lower cost.

Best practice: Always QC the first batch from a new vendor or design. Once validated, subsequent similar pools may not need full QC.

Several factors can increase dropout beyond platform averages:

Sequence Complexity: High GC (>70%), homopolymer runs, or strong secondary structures dramatically increase synthesis failure and amplification bias.
Oligo Length: Longer oligos (>200 nt) have higher failure rates, especially on array platforms.
Amplification Bias: Too many PCR cycles or suboptimal conditions cause severe bias. Some sequences amplify 100× better than others.
Vendor Variability: Quality varies between vendors and even between batches. Always check vendor's actual QC data if available.

Solution: Use Batch Sequence QC tool to identify problematic sequences before synthesis. Redesign or exclude sequences with issues.

Limited options exist, but some approaches can help:

Spike-in Dropouts: If QC identifies specific dropouts, order those oligos separately and spike them in at appropriate concentrations.
Computational Correction: For screens, computationally account for variation by normalizing counts based on QC sequencing. Can't fix dropouts but can adjust for fold-differences.
Selective Amplification: Advanced: use limiting dilution PCR or degenerate primers to preferentially amplify under-represented sequences. Technically challenging.
Re-synthesis: For critical applications, the most reliable solution is to re-synthesize problematic oligos (identified by QC) individually using column-based synthesis.

Prevention is better: Design for uniformity (avoid problematic sequences) and choose appropriate synthesis platform from the start.

Larger pools generally have worse uniformity:

<1,000 oligos: Best uniformity possible. Column synthesis can achieve 95%+ within 3-5 fold.
1,000-10,000 oligos: Good uniformity achievable. Array synthesis typical: 85-90% within 10 fold.
10,000-100,000 oligos: Moderate uniformity. Expect 80-85% within 10-15 fold. Plan for 5-10% dropout.
>100,000 oligos: Variable uniformity. May see 15-20 fold variation and 10-15% dropout. Redundancy critical.

Why: Larger pools have more synthesis positions (more variation), more PCR cycles needed (more bias), and statistically higher chance of problematic sequences.

Related Tools

Error Rate Calculator

Calculate synthesis error rate and full-length product percentage

Batch Sequence QC

Identify problematic sequences that may cause dropouts

Coverage Calculator

Plan redundancy and coverage for robust pool design

Secondary Structure Predictor

Detect structures that cause amplification bias

GC Content Analyzer

Analyze GC distribution to predict amplification bias

Dilution Calculator

Prepare pools at uniform concentrations for experiments