BrentLab/yeast_genome_resources

BrentLab Yeast Genome Resources

This Dataset stores resources meant to aid in the exploration of yeast -omic data, curated by the Brent Lab.

Terminology

Across all datasets in the BrentLab collection, we use the following terms consistently

• —locus_tag: The systematic ID of an ORF. Eg, YKL038W
• —symbol: The common name of an ORF. Eg, RGT1
• —target: when the genomic locus is the 'target' of location of measurement, then it is referred to as a 'target'. Eg, in RNAseq, the column target_locus_tag would store the counts over that gene.
• —regulator: This collection is made up of binding location assays, and perturbation of transcription factors and chromatin interacting proteins, which I refer to generally as 'regulators'

This repo provides the following:

You can find these by clicking on the files and versions tab. The file format is noted.

• —brentlab_features (csv): This is a simplified version of the SGD S288C-R64-3-1 annotations. We have used this file to standardize target and regulator locus_tag and symbol across the other datasets in this collection.
• —yiming_promoters (bed): The promoter regions used in Kang et al in the Dual threshold optimization paper. These promoter regions are used for callingcards.
• —mindel_promoters (csv): The promoters used by the Barkai lab to evaluate the overlap between perturbation and binding in the Mahendrawada 2025 set in this preprint. See scripts/createpromoterbedfrommindel.R. The column in_mahendrawada_features is TRUE if the locustag is in the feature set used by Mahendrawada 2025, which is only protein coding genes which are not labeled dubious. The column `promoterexact_aligns` provides a tally of how many times the promoter sequence aligns exactly from end to end with no gaps in the genome. Most align only once, which is what we would expect for a 700+ bp sequence. However, there are 12 loci with up to 4 exact alignments. I didn't examine these beyond confirming the multiple alignments -- I suspect that it is due to repeat regions. It is noted because it is surprising that such long sequences had multiple perfect alignments.
• —gal_tss_sgd-5-1_verified_orf (bed): Genomic coordinates of Transcription Start Sites (TSS) for verified ORFs under Galactose culture conditions. These are derived from annotation version S288C-R64-5-1. See scripts/createtssfiles.R
• —median_across_conds_tss_sgd-5-1_verified_orf (bed): Median Transcription Start Site (TSS) coordinates for verified ORFs aggregated across different experimental conditions. See scripts/createtssfiles.R
• —ypd_tss_sgd-5-1_verified_orf (bed): Genomic coordinates of Transcription Start Sites (TSS) for verified ORFs under standard YPD (rich medium) culture conditions. See scripts/createtssfiles.R
• —intergenic_regions_5_1 (bed and fasta): Both the genomic coordinates and sequences of the intergenic regions in version S288C-R64-5-1. See scripts/parseintergenicregions.R
• —chrmap (csv): This file provides a mapping between chromosome names between, eg UCSC, ensembl, etc.

Accessing Data

The examples below require the HuggingFace Hub client (pip install huggingface_hub).

Currently, we expect that this will be used for its raw files. Download individual files and open them in your preferred tool.

Direct parquet access

from huggingface_hub import snapshot_download
import duckdb

repo_path = snapshot_download(
    repo_id="BrentLab/yeast_genome_resources",
    repo_type="dataset",
    allow_patterns="brentlab_features.parquet",
)
conn = duckdb.connect()
# returns a pandas DataFrame with the first 5 rows
conn.execute(
    "SELECT * FROM read_parquet(?) LIMIT 5",
    [f"{repo_path}/brentlab_features.parquet"],
).df()

Accessing using R

Clone the repository and read parquet files directly with arrow:

# install.packages("arrow")
arrow::read_parquet("brentlab_features.parquet")