Oligo Pool Uniformity Calculator for Dropout and NGS Depth

Estimate oligo pool dropout, synthesis uniformity, CV%, and NGS QC sequencing depth. Compare synthesis platforms and predict concentration variation for CRISPR screens, capture panels, gene synthesis, and array-based experiments.

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 is the percentage of designed sequences that are completely absent or below a detection threshold in the synthesized pool. Array-based synthesis platforms can show measurable dropout risk, while column-based pool construction by mixing individual oligos usually has much lower dropout. Sequences with extreme GC content, long homopolymers, or stable secondary structures are most likely to drop out.

The Uniformity Estimator estimates expected CV%, dropout rate, and recommended NGS sequencing depth based on pool size, synthesis platform, and sequence characteristics. Use it to plan CRISPR screens, capture panels, or pooled assays where uneven representation can reduce effective coverage.

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 higher dropout risk due to increased sequence diversity and competition during synthesis. For array platforms, small pools often show lower dropout than very large pools, but synthesis platform quality, sequence composition, and QC depth can matter more than pool size alone. The CV% also tends to increase modestly with 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. The 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

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Related reading

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Oligo Pool GuidePre-Order Oligo Pool QCCRISPR Library Design GuideOligo Pool Vendor Comparison