Barbosa Benchmark: Deep Intronic Variant Data

24.4 MB of benchmark data for evaluating deep intronic variant prediction models, hosted on GitHub. The dataset was last updated on May 13, 2026, and is shared under a CC-BY-4.0 license by author Nathan Fortier.

Use Cases

• Benchmarking variant effect prediction algorithms based on intronic sequence data.
• Training machine learning models for identifying pathogenic deep intronic variants.
• Evaluating the performance of genomic annotation tools on non-coding regions.

Strengths

• Dataset size is 24.4 MB, indicating a focused and manageable benchmark.
• License is clearly defined as CC-BY-4.0, permitting broad reuse.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

GitHub repository (PedroBarbosa/DeepIntronic_Benchmark)

Freshness

Last updated 2026-05-13 17:42:57; freshness should be verified.