---
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. As done in the publication, we modified the Basenji2 dataset by extending the input sequence to 196,608 bp from the original 131,072 bp using the hg38 reference genome.

- **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 and two Jupyter notebooks:
1. `human_intervals.tsv`: 38,171 genomic regions (excluding header).
2. `mouse_intervals.tsv`: 33,521 genomic regions (excluding header).
3. `data_human.ipynb`: Code to create `human_intervals.tsv`.
4. `data_mouse.ipynb`: Code to create `mouse_intervals.tsv`.


## 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')
```