DKOsim: Synthetic Double-CRISPR Knockout Simulation Data

A synthetic dataset simulating double knockout CRISPR screens for genetic interaction analysis. The dataset, generated using the DKOsim Monte-Carlo method, mimics real data in terms of Log Fold Change, genetic interaction distribution, and replicate correlation. It was created by Yue Gu and last updated in April 2026.

Use Cases

• Benchmarking computational methods for genetic interaction detection based on simulated double knockout data.
• Investigating optimal CRISPR library designs based on simulated parameters like coverage depth and guide efficiency.
• Studying the statistical properties of genetic interaction distributions in a controlled, synthetic environment.

Strengths

• Dataset is small (13.5 KB), facilitating quick download and prototyping.
• Simulation parameters like single gene fitness effects and gene pair interactions are user-specifiable, offering control.
• Released under a permissive CC-BY-4.0 license for broad reuse.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• Data is entirely synthetic; findings may not directly translate to real biological systems.

Provenance

Source

figshare

Collection Method

Generated via the DKOsim Monte-Carlo simulation method.

Time Range

Synthetic data; no temporal coverage.

Freshness

Last updated 2026-04-17 17:51:27.

Geography

Synthetic data; no spatial coverage.