Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+
+
+## 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](https://www.nature.com/articles/s41586-023-06173-7)), 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
+```python
+from datasets import load_dataset
+
+# Download the full dataset, including 3 tasks and all splits
+dataset = load_dataset("BGI-HangzhouAI/variant-hotspot")
+
+# Download a specific task
+task_name = "CPC_8192"  
+dataset = load_dataset(
+    "BGI-HangzhouAI/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:
+
+$$p = P(X \geq k \mid \lambda)$$
+
+where $k$ is the observed mutation count in a window and $\lambda$ 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)