borzoi-data

Dataset Summary

This dataset contains the specific genomic intervals used for training, validating, and testing the Borzoi model, a deep learning architecture for predicting functional genomic tracks from DNA sequence. The intervals are provided for both human and mouse genomes. We modified the intervals provided in the original source by extending the input sequence to 524,288 bp to create the full interval that was supplied to the model.

• Publication: Avsec, Ž., et al. "Effective gene expression prediction from sequence by integrating long-range interactions." Nat Methods 18, 1196–1203 (2021).
• Original Source https://github.com/calico/borzoi/tree/main/data
• 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: 55,497 genomic regions (excluding header).
2. mouse_intervals.tsv: 49,369 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
fold	string	Fold assignment (fold0-fold7)
split	string	Data partition assignment (train, test, or val)

Statistics

File	Number of Regions	Genome Build
human_intervals.tsv	55,497	hg38
mouse_intervals.tsv	49,369	mm10

Usage

from huggingface_hub import hf_hub_download
import pandas as pd

file_path = hf_hub_download(repo_id="Genentech/borzoi-data", filename="human_intervals.tsv")
df_human = pd.read_csv(file_path, sep='\t')

file_path = hf_hub_download(repo_id="Genentech/borzoi-data", filename="mouse_intervals.tsv")
df_mouse = pd.read_csv(file_path, sep='\t')