README.md

metadata

license: mit
tags:
  - transcription-factor
  - binding
  - chipexo
  - genomics
  - biology
  - duckdb
pretty_name: Rossi ChIP-exo 2021
configs:
  - config_name: metadata
    description: Metadata describing the tagged regulator in each experiment
    default: true
    data_files:
      - split: train
        path: rossi_2021_metadata.parquet
    dataset_info:
      features:
        - name: regulator_locus_tag
          dtype: string
          description: Systematic gene name (ORF identifier) of the transcription factor
        - name: regulator_symbol
          dtype: string
          description: Standard gene symbol of the transcription factor
        - name: run_accession
          dtype: string
          description: GEO run accession identifier for the sample
        - name: yeastepigenome_id
          dtype: string
          description: Sample identifier used by yeastepigenome.org
  - config_name: genome_map
    description: ChIP-exo 5' tag coverage data partitioned by sample accession
    data_files:
      - split: train
        path: genome_map/*/*.parquet
    dataset_info:
      features:
        - name: chr
          dtype: string
          description: Chromosome name (e.g., chrI, chrII, etc.)
        - name: pos
          dtype: int32
          description: Genomic position of the 5' tag
        - name: pileup
          dtype: int32
          description: Depth of coverage (number of 5' tags) at this genomic position
      partitioning:
        enabled: true
        partition_by:
          - run_accession
        path_template: genome_map/accession={run_accession}/*.parquet
        filter_patterns:
          - accession=([^/]+)
language:
  - en

Rossi 2021

This data is gathered from yeastepigenome.org. This work was published in

Rossi MJ, Kuntala PK, Lai WKM, Yamada N, Badjatia N, Mittal C, Kuzu G, Bocklund K, Farrell NP, Blanda TR, Mairose JD, Basting AV, Mistretta KS, Rocco DJ, Perkinson ES, Kellogg GD, Mahony S, Pugh BF. A high-resolution protein architecture of the budding yeast genome. Nature. 2021 Apr;592(7853):309-314. doi: 10.1038/s41586-021-03314-8. Epub 2021 Mar 10. PMID: 33692541; PMCID: PMC8035251.

Dataset details

genome_map is fully reprocessed data from the sequence files. I used the nf-core/chipseq pipeline, details for which can be found in scripts/. With those bams, I filtered the reads using samtools and the same settings specified in Rossi et al 2021, and then counted 5' ends using bedtools. See scripts/count_tags.sh.

Data Structure

Metadata

Field	Description
regulator_locus_tag	Systematic gene name (ORF identifier) of the transcription factor
regulator_symbol	Standard gene symbol of the transcription factor
run_accession	GEO run accession identifier for the sample
yeastepigenome_id	Sample identifier used by yeastepigenome.org

Genome Map

Field	Description
chr	Chromosome name, ucsc (e.g., chrI, chrII, etc.)
pos	Genomic position of the 5' tag
pileup	Depth of coverage (number of 5' tags) at this genomic position

Usage

The entire repository is large. It may be preferable to only retrieve specific files or partitions. You can use the metadata files to choose which files to pull.

from huggingface_hub import snapshot_download
import duckdb
import os

# Download only the metadata first
repo_path = snapshot_download(
    repo_id="BrentLab/rossi_2021",
    repo_type="dataset",
    allow_patterns="rossi_2021_metadata.parquet"
)

dataset_path = os.path.join(repo_path, "rossi_2021_metadata.parquet")
conn = duckdb.connect()
meta_res = conn.execute("SELECT * FROM read_parquet(?) LIMIT 10", [dataset_path]).df()
print(meta_res)

We might choose to take a look at the file with accession SRR11466106:

# Download only a specific sample's genome coverage data
repo_path = snapshot_download(
    repo_id="BrentLab/rossi_2021",
    repo_type="dataset",
    allow_patterns="genome_map/accession=SRR11466106/*.parquet"
)

# Query the specific partition
dataset_path = os.path.join(repo_path, "genome_map")
result = conn.execute("SELECT * FROM read_parquet(?) LIMIT 10", 
                     [f"{dataset_path}/**/*.parquet"]).df()
print(result)