Oligo Pool Uniformity & Dropout Calculator

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.