BGI-HangzhouAI/Benchmark_Dataset-variant_hotspot

Summary

This dataset provides a benchmark for evaluating the scalability of genomic models to even-longer DNA inputs through a mutation hotspot classification task. Using whole-genome variant data from the Chinese Pangenome Consortium (CPC) (Gao et al., 2023), we identify genomic regions (hotspots) exhibiting significantly higher mutation densities compared to local chromosomal backgrounds. Sequences of 8 Kbp, 32 Kbp, and 128 Kbp are extracted to create three parallel tasks, enabling model comparison across different input lengths. Each sequence is labeled as either hotspot (1) or non-hotspot (0), forming a balanced binary classification dataset designed for evaluating large-context genomic foundation models.

Usage

from datasets import load_dataset

# Download the full dataset, including 3 tasks and all splits
dataset = load_dataset("BGI-HangzhouAI/Benchmark_Dataset-variant_hotspot")

# Download a specific task
task_name = "CPC_8192"  
dataset = load_dataset(
    "BGI-HangzhouAI/Benchmark_Dataset-variant_hotspot",
    data_files = {
        "train": f"{task_name}/train.jsonl",
        "eval": f"{task_name}/eval.jsonl",
        "test": f"{task_name}/test.jsonl",
    }
)

Benchmark tasks

Task	task_name	Input fields	# Train Seqs	# Validation Seqs	# Test Seqs
CPC 8K	CPC_8192	{seq, label}	59,011	526	3,192
CPC 32K	CPC_32768	{seq, label}	14,471	132	788
CPC 128K	CPC_131072	{seq, label}	3,605	32	188

Data Processing

1. Window segmentation

Each chromosome from the CPC variant dataset was divided into non-overlapping windows of fixed lengths — 8,192 bp, 32,768 bp, or 131,072 bp — corresponding to the three tasks (CPC 8K, CPC 32K, and CPC 128K).

2. Variant counting

For each window, the number of observed mutations (single-nucleotide or small indel events) was counted across all CPC samples.

3. Statistical identification of mutation hotspots

To detect regions with significantly elevated mutation density, a Poisson right-tail test was applied under the null hypothesis that mutations occur independently and randomly along the chromosome:

$$p=P(X\ge k\mid \lambda )p\; =\; P(X\; \backslash geq\; k\; \backslash mid\; \backslash lambda)$$

where k is the observed mutation count in a window and λ is the background mutation rate, estimated as the mean mutation count of all windows within the same chromosome. P-values were corrected for multiple testing using the Benjamini–Hochberg FDR procedure, and windows with FDR < 0.05 were labeled as mutation hotspots (label = 1).

4. Non-hotspot sampling and balancing

To construct a balanced dataset, an equal number of non-hotspot windows (label = 0) were randomly sampled from the remaining genomic regions of the same chromosome.

5. Dataset splitting

Genomic sequences were split by chromosome to ensure no positional overlap across sets:

• Train: chromosomes 1–6, 9–21, X, Y
• Validation: chromosome 22
• Test: chromosomes 7 and 8

6. Final format

Datasets are saved in JSONL format. Each example contains:

• "seq" — the DNA sequence string (A/C/G/T, uppercase)
• "label" — binary hotspot indicator (1 = hotspot, 0 = non-hotspot)