Datasets:
File size: 1,892 Bytes
76d33e8 83b86ae 76d33e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
license: mit
task_categories:
- tabular-regression
tags:
- biology
- genomics
pretty_name: "Enformer Intervals"
size_categories:
- 10K<n<100K
---
# enformer-data
## Dataset Summary
This dataset contains the specific genomic intervals used for training, validating, and testing the Enformer model, a deep learning architecture for predicting functional genomic tracks from DNA sequence. The intervals are provided for both human and mouse genomes.
- **Source Publication:** [Avsec, Ž., et al. "Effective gene expression prediction from sequence by integrating long-range interactions." Nat Methods 18, 1196–1203 (2021).](https://www.nature.com/articles/s41592-021-01252-x)
- **Genome Builds:**
- Human: hg38
- Mouse: mm10
## Repository Content
The repository includes two tab-separated values (TSV) files:
1. `human_intervals.tsv`: 38,171 genomic regions (excluding header).
2. `mouse_intervals.tsv`: 33,521 genomic regions (excluding header).
## Dataset Structure
### Data Fields
Both files follow a standard genomic interval format:
| Column | Type | Description |
| :--- | :--- | :--- |
| `chrom` | string | Chromosome identifier (e.g., `chr18`, `chr4`) |
| `start` | int | Start coordinate of the interval |
| `end` | int | End coordinate of the interval |
| `split` | string | Data partition assignment (`train`, `test`, or `val`) |
### Statistics
| File | Number of Regions | Genome Build |
| :--- | :--- | :--- |
| `human_intervals.tsv` | 38,171 | hg38 |
| `mouse_intervals.tsv` | 33,521 | mm10 |
## Usage
```python
from huggingface_hub import hf_hub_download
import pandas as pd
file_path = hf_hub_download(repo_id="Genentech/enformer-data", filename="human_intervals.tsv")
df_human = pd.read_csv(file_path, sep='\t')
file_path = hf_hub_download(repo_id="Genentech/enformer-data", filename="mouse_intervals.tsv")
df_mouse = pd.read_csv(file_path, sep='\t')
``` |