Library Design

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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/crispr-screens/library-designThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9,…

---
name: bio-crispr-screens-library-design
description: Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.
tool_type: mixed
primary_tool: CRISPOR
---

## Version Compatibility

Reference examples tested with: CRISPOR 5.01+, BioPython 1.83+, pandas 2.2+, numpy 1.26+, Azimuth 2.0+ (Doench 2016), CRISPRon 1.0+ (Xiang 2021), DeepSpCas9 1.0+ (Kim 2019).

Before using code patterns, verify installed versions match. If versions differ:
- CLI: `crispor.py --help` from the crisporWebsite clone
- Python: Azimuth has no console script; call `azimuth.model_comparison.predict(...)`

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

## sgRNA Library Design

**"Design a CRISPR library for my screen"** -> Pick a chemistry (Cas9 KO, CRISPRi, CRISPRa, Cas12a, base or prime editor), score candidate guides for on-target activity and off-target liability, position them relative to gene/TSS, add appropriate controls, lay out the oligo for synthesis, and validate the cloned pool.

- Python: `crispor.py` (web + CLI) for batch genome-wide guide scoring with CFD+MIT off-target
- Python: `azimuth` (Microsoft Research) for Rule Set 2 on-target predictions (Brunello-style)
- Python: `CRISPRon`, `DeepSpCas9` for modern deep-learning predictors
- R: `crisprDesign` (Bioconductor) for integrated annotation-aware design

## Library Chemistry Decision Tree

| Goal | Chemistry | Canonical library | Guides/gene | TSS / target window |
|------|-----------|-------------------|-------------|---------------------|
| Loss-of-function essentiality, fitness | SpCas9 KO | Brunello, TKOv3, Avana | 4 (Brunello), 4 (TKOv3), 6 (Avana) | Constitutive exons, prefer aa 5-65% from N-terminus |
| Knockdown of non-cuttable genes, dosage-sensitive | dCas9-KRAB (CRISPRi) | Dolcetto, Horlbeck v2 | 6 (Dolcetto), 5 (Horlbeck) | Optimum +25 to +75 downstream of FANTOM5 TSS, searched out to -50/+300 (Dolcetto, Sanson 2018); -25 to +500 (Horlbeck v2) |
| Gain-of-function, gene activation | dCas9-VP64 / SAM / SunTag (CRISPRa) | Calabrese, Horlbeck-CRISPRa | 6 (Calabrese), 5 (Horlbeck) | -150 to -75 from TSS (Calabrese); -550 to -25 (Horlbeck v2) |
| Paralog buffering, GI screens | enAsCas12a multiplex | Inzolia, in4mer | 4-guide arrays | Constitutive exons |
| Variant function, SNV scanning | CBE / ABE | Custom tiling library | Tile editing windows | Editing window pos 4-8 from PAM-distal end |
| Precise edit, indel-free | Prime editor | Custom PRIDICT-designed | Tile pegRNAs | Anywhere with NGG PAM within 30 nt of edit |

**Fails when:**
- CRISPRi/a targeting wrong TSS: any TSS without FANTOM5 CAGE evidence is suspect; guides positioned against the wrong TSS lose most of their knockdown.
- Cas9 KO of essential paralogs: single-KO buffering hides paralog-redundant essentials (42% of constitutively expressed genes never score, Dede 2020); switch to Cas12a multiplex.
- Base editor over an exon-intron boundary: editing-window bystanders create splice variants instead of the intended SNV.

## On-Target Scoring: Algorithmic Taxonomy

| Predictor | Year | Training set | Strengths | Fails when |
|-----------|------|--------------|-----------|------------|
| Doench Rule Set 1 | 2014 | Flow-sorted GFP+ knockouts | Simple, interpretable | Limited training data; sub-optimal at >NGG context |
| Doench Rule Set 2 / Azimuth 2.0 | 2016 | 1,841 flow-cytometry guides (Doench 2014) plus new guides tiling additional genes | Gold-standard for SpCas9; basis of Brunello | Trained on dropouts; under-predicts efficacy for nuclear-localized targets |
| DeepSpCas9 | 2019 | 12,832 synthetic targets integrated in HEK293T | Spearman ~0.77 vs measured indel frequency on held-out data | Black-box; sensitive to chromatin/context features it wasn't trained on |
| CRISPRon | 2021 | High-throughput indel sequencing | Best for therapeutic-grade target nomination | Slow per-guide; over-fits to its specific cell line |
| DeepHF | 2019 | ~171k guides in HEK293T (WT 55,604; eSpCas9(1.1) 58,167; HF1 56,888) | Separate model per enzyme variant, including WT | Pick the model matching the enzyme actually used |

**Reconciliation:** When predictors disagree, prefer the model whose training cell line matches the screen line (DeepSpCas9 was trained on synthetic targets integrated in HEK293T). For Brunello selection, Azimuth/Rule Set 2 is sufficient because the library was built with it -- introducing a different scorer creates apples-to-oranges ranking with the original library.

## Off-Target Scoring

| Score | Year | Math | Cutoff convention |
|-------|------|------|--------------------|
| MIT (Hsu) | 2013 | Position-weighted mismatch penalty | Specificity score 0-100, higher is better; CRISPOR treats >=50 as a good guide |
| CFD (Doench) | 2016 | Position+nucleotide-specific penalty fit on Brunello | Per-site CFD >0.2 counts a candidate off-target (Doench 2016); CRISPOR's aggregate CFD specificity score is 0-100, higher is better |
| Elevation | 2018 | ML on CFD + mismatch positions | Tighter than CFD |

CFD remains the default for genome-wide library design. **Critical pitfall:** CFD penalizes only mismatches, not bulges; for ≤1 mismatch + 1-bp bulge off-targets, validate empirically with GUIDE-seq or CIRCLE-seq. CRISPOR reports both t

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/maximilianh/crisporWebsite   # CRISPOR is not on PyPI biopython pandas numpy

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Library Design

• github.com/GPTomics/bioSkills/tree/main/crispr-screens/library-d — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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