metadata
license: mit
tags:
- transcription-factor
- binding
- chipexo
- genomics
- biology
language:
- en
pretty_name: Rossi ChIP-exo 2021
configs:
- config_name: metadata
description: Metadata describing the tagged regulator in each experiment
dataset_type: metadata
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
dataset_type: genome_map
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
- config_name: rossi_annotated_features
description: ChIP-exo regulator-target binding features with peak statistics
dataset_type: annotated_features
default: true
metadata_fields:
- regulator_locus_tag
- regulator_symbol
- target_locus_tag
- target_symbol
data_files:
- split: train
path: yeastepigenome_annotatedfeatures.parquet
dataset_info:
features:
- name: sample_id
dtype: int32
description: Unique identifier for each ChIP-exo experimental sample.
- name: pss_id
dtype: float64
description: >-
Current brentlab promotersetsig table id. This will eventually be
removed.
- name: binding_id
dtype: float64
description: Current brentlab binding table id. This will eventually be removed.
- name: yeastepigenome_id
dtype: float64
description: Unique identifier in the yeastepigenome database.
- name: regulator_locus_tag
dtype: string
description: Systematic ORF name of the regulator.
role: regulator_identifier
- name: regulator_symbol
dtype: string
description: Common gene name of the regulator.
role: regulator_identifier
- name: target_locus_tag
dtype: string
description: >-
The systematic ID of the feature to which the effect/pvalue is
assigned. See hf/BrentLab/yeast_genome_resources
role: target_identifier
- name: target_symbol
dtype: string
description: >-
The common name of the feature to which the effect/pvalue is
assigned. If there is no common name, the `target_locus_tag` is
used.
role: target_identifier
- name: n_sig_peaks
dtype: float64
description: Number of peaks in the promoter region of the the target gene
role: quantitative_measure
- name: max_fc
dtype: float64
description: >-
If there are multiple peaks in the promoter region, then the maximum
is reported. Otherwise, it is the fold change of the single peak in
the promoter.
role: quantitative_measure
- name: min_pval
dtype: float64
description: 'The most significant p-value among peaks for this interaction. '
role: quantitative_measure
- config_name: reprocess_annotatedfeatures
description: Annotated features reprocessed with updated peak calling methodology
dataset_type: annotated_features
data_files:
- split: train
path: reprocess_annotatedfeatures.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: target_locus_tag
dtype: string
description: Systematic gene name (ORF identifier) of the target gene
- name: target_symbol
dtype: string
description: Standard gene symbol of the target gene
- name: baseMean
dtype: float64
description: >-
Average of normalized count values, dividing by size factors, taken
over all samples
- name: log2FoldChange
dtype: float64
description: Log2 fold change between comparison and control groups
- name: lfcSE
dtype: float64
description: Standard error estimate for the log2 fold change estimate
- name: stat
dtype: float64
description: Value of the test statistic for the gene
- name: pvalue
dtype: float64
description: P-value of the test for the gene
- name: padj
dtype: float64
description: Adjusted p-value for multiple testing for the gene
Rossi 2021
This data is gathered from yeastepigenome.org. This work was published in
This repo provides 4 datasets:
- rossi_2021_metadata: Metadata describing the tagged regulator in each experiment.
- genome_map: ChIP-exo 5' tag coverage data partitioned by sample accession.
- reprocess_annotatedfeatures: This data was reprocessed from the fastq files on GEO. See scripts/reprocessing_details.txt for more information.
- yeastepigenome_annotatedfeatures: ChIP-exo regulator-target binding features with peak statistics.
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/rossi_2021" dataset from Hugging Face Hub.
from huggingface_hub import ModelCard
from pprint import pprint
card = ModelCard.load("BrentLab/rossi_2021", 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)
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)
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/rossi_2021"
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, "yeastepigenome_annotatedfeatures.parquet")
print(f"✓ Parquet file at: {parquet_path}")