BrentLab/yeast_genome_resources

BrentLab Yeast Genome Resources

This Dataset stores resources meant to make data exploration of yeast -omic data, curated by the Brent Lab, easier and more harmonious.
To cite, cite SGD

This repo provides 4 datasets:

• brentlab_features: BrentLab Yeast Genome Resources.
• gal_tss_sgd-5-1_verified_orf: Genomic coordinates of Transcription Start Sites (TSS) for verified ORFs under Galactose culture conditions.
• median_across_conds_tss_sgd-5-1_verified_orf: Median Transcription Start Site (TSS) coordinates for verified ORFs aggregated across different experimental conditions.
• ypd_tss_sgd-5-1_verified_orf: Genomic coordinates of Transcription Start Sites (TSS) for verified ORFs under standard YPD (rich medium) culture conditions.

Usage

The python package tfbpapi provides an interface to this data which eases examining the datasets, field definitions and other operations. You may also download the parquet datasets directly from hugging face by clicking on "Files and Versions", or by using the huggingface_cli and duckdb directly. In both cases, this provides a method of retrieving dataset and field definitions.

tfbpapi

After installing tfbpapi, you can adapt this tutorial in order to explore the contents of this repository.

huggingface_cli/duckdb

You can retrieves and displays the file paths for each configuration of the "BrentLab/yeast_genome_resources" dataset from Hugging Face Hub.

from huggingface_hub import ModelCard
from pprint import pprint

card = ModelCard.load("BrentLab/yeast_genome_resources", repo_type="dataset")

# cast to dict
card_dict = card.data.to_dict()

# Get partition information
dataset_paths_dict = {d.get("config_name"): d.get("data_files")[0].get("path") for d in card_dict.get("configs")}

pprint(dataset_paths_dict)

You may access just the Dataset metadata like this:

from huggingface_hub import ModelCard

card = ModelCard.load("BrentLab/yeast_genome_resources", repo_type="dataset")

# cast to dict
card_dict = card.data.to_dict()

# Get partition information
card_dict.get("dataset_info").get("partitioning").get("keys")

If you wish to pull the entire repo, due to its size you may need to use an authentication token. If you do not have one, try omitting the token related code below and see if it works. Else, create a token and provide it like so:

repo_id = "BrentLab/yeast_genome_resources"

hf_token = os.getenv("HF_TOKEN")

# Download entire repo to local directory
repo_path = snapshot_download(
    repo_id=repo_id,
    repo_type="dataset",
    token=hf_token
)

print(f"\n✓ Repository downloaded to: {repo_path}")

# Construct path to the rossi_annotated_features parquet file
parquet_path = os.path.join(repo_path, "brentlab_features.parquet")
print(f"✓ Parquet file at: {parquet_path}")