DKOsim: Synthetic Double-CRISPR Knockout Data for Genetic Interaction Studies

DKOsim is a simulation tool that generates synthetic double knockout CRISPR screen data where gene fitness effects and interactions are user-specified. The 13.5 KB Excel file contains simulated datasets designed to resemble real data in Log Fold Change (LFC) and GI distribution. Author Yue Gu published this resource under a CC-BY-4.0 license on figshare in April 2026.

Use Cases

• Benchmarking genetic interaction detection algorithms based on simulated ground-truth interactions.
• Optimizing CRISPR library design parameters like coverage depth and guide efficiency based on simulated outcomes.
• Studying the statistical properties of genetic interaction distributions in a controlled, synthetic environment.
• Training machine learning models for interaction prediction using data where true interactions are known.

Strengths

• Data is generated by a reproducible simulation tool (DKOsim) where ground-truth interactions are known.
• Simulated outputs are designed to closely match real data characteristics in LFC, GI distribution, and replicate correlation.
• Published under a permissive CC-BY-4.0 license for open reuse.

Limitations

• Dataset is synthetic (13.5 KB), not experimental, limiting direct biological application.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific modeling tasks.

Provenance

Source

figshare, authored by Yue Gu.

Collection Method

Data is synthetically generated by the DKOsim Monte-Carlo simulation tool.

Time Range

Synthetic data generation; no real temporal coverage.

Freshness

Last updated 2026-04-17 17:51:25; freshness should be verified.

Geography

Synthetic data; no real spatial coverage.