upload

README.md

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+---
+annotations_creators:
+- found
+language:
+- en
+license: mit
+multilinguality: monolingual
+pretty_name: vep_mendelian_traits_chr11_split
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- sequence-classification
+---
+# vep_clinvar_chr1_split
+
+- 字段: ref, alt, label, chromosome, position
+- 划分: chromosome=1为test，其余为train
+- 支持自动生成ref/alt序列
+
+## 用法
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "Bgoood/vep_mendelian_traits_chr11_split",
+    sequence_length=2048,
+    fasta_path="/path/to/hg38.fa.gz",
+    data_dir="."
+)
+```
+```
+
+---
+
+## 5. 上传到 HuggingFace
+
+1. **初始化git repo（如果还没有）**
+   ```bash
+   git lfs install
+   git clone https://huggingface.co/datasets/Bgoood/vep_mendelian_traits_chr11_split
+   cd vep_mendelian_traits_chr11_split
+   # 把 train.csv, test.csv, vep_mendelian_traits_chr11_split.py, README.md 放到这个目录
+   git add .
+   git commit -m "init dataset with script"
+   git push
+   ```
+
+2. **或者直接网页上传**  
+   在你的数据集页面，点击“Add file”，上传上述文件。
+
+---
+
+## 6. 用户使用方式
+
+用户只需这样调用即可自动生成ref/alt序列：
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "Bgoood/vep_mendelian_traits_chr11_split",
+    sequence_length=2048,
+    fasta_path="/path/to/hg38.fa.gz",
+    data_dir="."
+)
+```
+
+---
+
+## 7. 依赖
+
+确保用户环境已安装：
+```bash
+pip install datasets pyfaidx pandas
+```
+
+---
+
+## 8. 注意事项
+
+- `fasta_path` 必须是本地可访问的 hg38.fa.gz 路径。
+- 你上传到HF的数据集只需包含原始csv和脚本，不需要包含fasta文件。
+
+---
+
+如需自动化脚本生成csv、或有其他定制需求，请随时告知！

omim.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1fc127098a5182ef41e2308b637201463eb201ad867b2f143387cc9d648fb836
+size 69715279

vep_omim.py

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+import os
+import pandas as pd
+import gzip
+from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split, BuilderConfig, Features, Value
+from Bio import SeqIO
+from typing import Dict, Any
+
+class VepOmimConfig(BuilderConfig):
+    def __init__(self, sequence_length=2048, fasta_path=None, **kwargs):
+        super().__init__(**kwargs)
+        self._sequence_length = sequence_length
+        self._fasta_path = fasta_path
+    
+    @property
+    def sequence_length(self):
+        return self._sequence_length
+    
+    @property
+    def fasta_path(self):
+        return self._fasta_path
+
+class VepOmimSplit(GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = VepOmimConfig
+    BUILDER_CONFIGS = [
+        VepOmimConfig(name="default", sequence_length=2048, fasta_path=None)
+    ]
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        return DatasetInfo(
+            features=Features({
+                "ref_forward_sequence": Value("string"),
+                "alt_forward_sequence": Value("string"),
+                "label": Value("int32"),
+                "chromosome": Value("string"),
+                "position": Value("int32"),
+                "ref": Value("string"),
+                "alt": Value("string"),
+                "consequence": Value("string"),
+            })
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = {
+            "omim": "omim.csv",
+        }
+        downloaded_files = dl_manager.download(data_files)
+        return [
+            SplitGenerator(name="omim", gen_kwargs={"filepath": downloaded_files["omim"]}),  # type: ignore
+        ]
+
+    def _load_fasta_sequences(self, fasta_path: str) -> Dict[str, str]:
+        """加载 fasta 序列到内存字典，支持 gzip 压缩"""
+        sequences = {}
+        if fasta_path.endswith('.gz'):
+            with gzip.open(fasta_path, 'rt') as f:
+                for record in SeqIO.parse(f, 'fasta'):
+                    sequences[record.id] = str(record.seq)
+        else:
+            with open(fasta_path, 'r') as f:
+                for record in SeqIO.parse(f, 'fasta'):
+                    sequences[record.id] = str(record.seq)
+        return sequences
+
+    def _generate_examples(self, filepath: str):
+        df = pd.read_csv(filepath)
+        config: VepOmimConfig = self.config  # type: ignore
+        seq_len = config.sequence_length
+        fasta_path = config.fasta_path
+        if fasta_path is None:
+            raise ValueError("You must provide fasta_path when loading the dataset!")
+        
+        # 加载所有序列到内存
+        sequences = self._load_fasta_sequences(fasta_path)
+        
+        for idx, row in df.iterrows():
+            chrom = str(row['chromosome'])
+            if not chrom.startswith('chr'):
+                chrom = 'chr' + chrom
+            
+            if chrom not in sequences:
+                raise ValueError(f"Chromosome {chrom} not found in fasta. Available: {list(sequences.keys())[:5]}...")
+            
+            pos = int(row['position'])
+            ref = str(row['ref'])
+            alt = str(row['alt'])
+            half = seq_len // 2
+            start = max(0, pos - half - 1)  # 0-based indexing
+            end = pos + half - 1
+            
+            seq = sequences[chrom][start:end]
+            seq_list = list(seq)
+            center_idx = half
+            ref_seq = seq_list.copy()
+            ref_seq[center_idx] = ref
+            ref_seq = ''.join(ref_seq)
+            alt_seq = seq_list.copy()
+            alt_seq[center_idx] = alt
+            alt_seq = ''.join(alt_seq)
+            yield idx, {
+                "ref_forward_sequence": ref_seq,
+                "alt_forward_sequence": alt_seq,
+                "label": int(row["label"]),
+                "chromosome": str(row["chromosome"]),
+                "position": int(row["position"]),
+                "consequence": row["consequence"],
+                "ref": row["ref"],
+                "alt": row["alt"]
+            }