bio-crispr-screens-combinatorial-screens
Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical
npx skills add BioTender-max/awesome-bio-agent-skills --skill combinatorial-screens --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: MAGeCK 0.5.9+ (for MLE with interaction terms), Inzolia library annotation (Bayle 2024), pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `mageck --version`; `mageck mle --help` - For Cas12a libraries: verify against published Inzolia / in4mer / Big Papi annotations If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## Combinatorial CRISPR Screen Analysis **"Run a combinatorial CRISPR screen to find synthetic-lethal interactions"** -> Design a paired or multiplex library, screen for double-knockout fitness, score per-pair genetic interaction (GI = observed_double - expected_additive), and identify synthetic-lethal (negative GI) and synthetic-rescue (positive GI) interactions. - CLI: `mageck mle` with explicit interaction terms for paired-Cas9 (Big Papi-style) - Python: custom GI scoring for Cas12a multiplex (in4mer / Inzolia) - Modality: enCas12a / LbCas12a single-array multiplex (preferred for paralog screens) ## Combinatoria
- Version Compatibility
- Combinatorial CRISPR Screen Analysis
- Combinatorial Architecture Decision Tree
- Cas9 vs Cas12a for Multiplex
- The Paralog Buffering Phenomenon
- Genetic Interaction (GI) Scoring
- Run Combinatorial Screen Analysis (MAGeCK MLE with Interaction Indicator)
- Inzolia / in4mer 4-Guide Array Analysis
- Failure Modes
- Big Papi cassette fuses sgRNAs
- Cas12a screen with low editing efficiency
- GI scoring without singletons
- Single-gene LFCs from different cell line
- Confounding cell-cycle / proliferation in GI scoring
Design matrix encoding double-KO as a separate "interaction" indicator cat > combo_design.txt <<EOF mageck mle \ The "interaction" column beta captures additional joint effect beyond additive
What does the bio-crispr-screens-combinatorial-screens skill do?
Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill combinatorial-screens --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.