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

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

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"

What Is Oligo Pool Uniformity?

Oligo pool uniformity describes how evenly different sequences are represented in a synthesized pool. Perfect uniformity means every unique sequence is present at exactly the same concentration. In practice, synthesis biases cause some sequences to be overrepresented (up to 10-100× above the mean) and others to be underrepresented or completely absent (dropout). Uniformity is typically quantified as the coefficient of variation (CV%) of sequence abundances measured by next-generation sequencing (NGS).

Dropout rate is the percentage of designed sequences that are completely absent or below a detection threshold in the synthesized pool. Array-based synthesis platforms (Twist, Agilent) typically have 1-5% dropout rates, while column-based pool construction (mixing individual oligos) has near-zero dropout. Sequences with extreme GC content, long homopolymers, or stable secondary structures are most likely to drop out.

Our Uniformity Estimator predicts expected CV%, dropout rate, and recommended NGS sequencing depth based on your pool size, synthesis platform, and sequence characteristics. This helps you plan experiments (especially CRISPR screens) by estimating how many cells you need to screen to achieve adequate coverage despite pool non-uniformity.

How to Use the Uniformity Estimator

  1. Enter your pool size (number of unique sequences) — ranges from 10 to 1,000,000.
  2. Select the synthesis platform: Twist, Agilent, IDT, GenScript, or Custom.
  3. The estimator calculates: expected dropout rate, CV%, fold-difference (max/min representation), and recommended NGS read depth for QC.
  4. Review the representation distribution chart to visualize expected uniformity.
  5. Use the NGS depth recommendation to plan your QC sequencing run.

Frequently Asked Questions

What is an acceptable CV% for an oligo pool?
For CRISPR screens: CV% below 30% is excellent, 30-50% is acceptable, above 50% may require overscreening. For gene assembly: CV% is less critical since you typically select individual clones. For mutagenesis libraries: CV% below 40% is recommended. Array-based synthesis typically produces pools with 20-50% CV, while individually synthesized and mixed pools can achieve CV% below 10%.
How does pool size affect uniformity?
Larger pools generally have slightly higher dropout rates due to increased sequence diversity and competition during synthesis. For array platforms: pools of <1,000 sequences typically have <1% dropout, 1,000-10,000 sequences have 1-3% dropout, and >100,000 sequences may have 3-5% dropout. The CV% also tends to increase modestly with pool size, but synthesis platform quality has a larger effect than pool size.
How much NGS sequencing depth do I need for pool QC?
As a rule of thumb: sequence at 500-1000× coverage (reads per unique sequence) for reliable uniformity assessment. For a pool of 10,000 sequences, that means 5-10 million reads. At 100× coverage, you can detect dropouts but cannot accurately measure CV%. At <50× coverage, sampling noise dominates and uniformity metrics are unreliable. Our calculator provides platform-specific depth recommendations.
What causes sequences to drop out during synthesis?
The main causes are: (1) Extreme GC content — sequences above 70% or below 25% GC have higher failure rates; (2) Stable secondary structures — hairpins and G-quadruplexes block the synthesis cycle; (3) Long homopolymer runs — especially poly-G runs >5 bases; (4) Sequence-specific synthesis biases — certain dinucleotide contexts have lower coupling efficiency; (5) Position-dependent effects on array platforms — oligos at certain chip positions may synthesize less efficiently.
How do I account for pool non-uniformity in CRISPR screens?
To ensure every guide RNA in your library is represented at ≥500 cells: divide 500 by the expected minimum fold-representation. For example, if the model predicts a 10× fold-difference (worst sequence is 10× lower than mean), you need 500 × 10 = 5,000 cells per guide on average, or 5,000 × library_size total cells. This "coverage" calculation is critical for designing adequately powered CRISPR screens.

Related Tools

Coverage Calculator

Calculate the cell coverage needed for your CRISPR screen accounting for pool uniformity.

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Identify and fix sequences likely to drop out before ordering your pool.

Error Rate Calculator

Predict per-sequence full-length yield based on length and coupling efficiency.

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