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

DKOsim generates simulated datasets where the fitness effect of each gene and the interaction of each gene pair are user-specified. The 13.5 KB XLS file, created by Yue Gu and last updated in April 2026, produces data resembling real double knockout CRISPR screens in terms of Log Fold Change and GI distribution. This simulation scheme is designed to help identify optimal computational methods and experimental designs for genetic interaction detection.

Use Cases

• Benchmarking genetic interaction detection algorithms based on simulated ground-truth interactions.
• Optimizing CRISPR library design parameters like depth of coverage and guide efficiency using synthetic data.
• Testing statistical methods for analyzing Log Fold Change (LFC) distributions from double knockout experiments.
• Studying the properties of gene-gene interaction networks in a controlled, simulated environment.

Strengths

• Data is fully reproducible with user-specified gene fitness and interaction effects.
• Simulated outputs closely resemble real double knockout CRISPR datasets in key metrics like LFC and GI distribution.
• Released under a permissive CC-BY-4.0 license for broad reuse.

Limitations

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

Provenance

Source

figshare

Collection Method

Monte-Carlo randomization methods adapted from single knockout simulation to generate synthetic double knockout data.

Time Range

Synthetic data generation; no temporal coverage.

Freshness

Last updated 2026-04-17 17:51:26; freshness should be verified.

Geography

Not applicable for synthetic simulation data.