3DGenBench

3DGenBench benchmarks computational models that predict three-dimensional genome organization by comparing model predictions with experimental chromatin contact data from Hi-C and capture Hi-C datasets.

Key Features:

• Curated Benchmark Dataset: Utilizes a manually curated dataset including 39 capture Hi-C profiles from wild-type and genome-edited mouse cells and five genome-wide Hi-C profiles from human, mouse, and Drosophila cells.
• Dual Analysis Modules: Provides two analysis modules with dedicated scoring modules for evaluating predicted genome structures against experimental chromatin interaction datasets.
• Model Performance Scoring: Calculates objective performance scores using multiple evaluation metrics to quantify agreement between computational predictions and experimental data.

Scientific Applications:

• 3D Genome Modeling Evaluation: Assesses the accuracy of computational methods that predict chromatin spatial organization from genomic data.
• Comparative Model Benchmarking: Enables standardized comparison of different computational approaches for predicting genome architecture.
• Genome Editing Studies: Evaluates predicted structural changes in chromatin organization using datasets from genome-edited cells.
• Chromatin Architecture Research: Supports studies investigating how three-dimensional genome structure relates to genomic regulation and cellular function.

Methodology:

3DGenBench compares computational predictions of genome organization with experimental Hi-C and capture Hi-C datasets and evaluates performance using scoring modules that apply multiple quantitative metrics.