Datasets:
metadata
license: mit
language:
- en
tags:
- biology
- genomics
- yeast
- transcription-factors
- callingcards
- transposon
- binding
- gene-expression
pretty_name: Calling Cards Transcription Factor Binding Dataset
experimental_conditions:
temperature_celsius: room
media:
name: synthetic_complete_minus_ura_his_leu
carbon_source:
- compound: D-galactose
concentration_percent: 2
nitrogen_source:
- compound: amino_acid_dropout_mix
concentration_percent: unspecified
specifications:
- minus_ura
- minus_his
- minus_leu
configs:
- config_name: annotated_features
description: >-
Calling Cards transcription factor binding data with enrichment scores and
statistical significance
dataset_type: annotated_features
default: true
data_files:
- split: train
path: annotated_features/*/*.parquet
dataset_info:
features:
- name: id
dtype: string
description: Unique identifier for each binding measurement
- 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: experiment_hops
dtype: float64
description: >-
Number of transposon insertion events (hops) at target locus in
experimental sample
- name: background_hops
dtype: float64
description: >-
Number of transposon insertion events (hops) at target locus in
background control
- name: background_total_hops
dtype: float64
description: >-
Total number of background hops across all loci in the control
sample
- name: experiment_total_hops
dtype: float64
description: >-
Total number of experimental hops across all loci in the
experimental sample
- name: callingcards_enrichment
dtype: float64
description: >-
Enrichment score calculated as ratio of normalized experimental to
background hops
- name: poisson_pval
dtype: float64
description: >-
P-value from Poisson test for statistical significance of binding
enrichment
- name: hypergeometric_pval
dtype: float64
description: >-
P-value from hypergeometric test for statistical significance of
binding enrichment
- name: batch
dtype: string
description: Experimental batch identifier for controlling batch effects
- config_name: annotated_features_meta
description: >-
Metadata for annotated features datasets including regulator informatioand
data quality indicators
dataset_type: metadata
applies_to:
- annotated_features
data_files:
- split: train
path: annotated_features_meta.parquet
dataset_info:
features:
- name: db_id
dtype: string
description: Database identifier for the dataset
role: experimental_condition
- name: regulator_locus_tag
dtype: string
description: Systematic identifier for the regulatory factor
role: regulator_identifier
- name: regulator_symbol
dtype: string
description: Standard symbol for the regulatory factor
role: regulator_identifier
- name: data_usable
dtype: string
description: Indicator of whether the data is suitable for analysis
role: experimental_condition
- name: preferred_replicate
dtype: string
description: Boolean indicator for preferred biological replicate
role: experimental_condition
- name: batch
dtype: string
description: Experimental batch identifier
role: experimental_condition
- name: single_binding
dtype: int64
description: Count or score for single binding events
role: quantitative_measure
- name: composite_binding
dtype: int64
description: Count or score for composite binding events
role: quantitative_measure
- name: analysis_set
dtype: bool
description: >-
TRUE if this record is to be used for analysis. FALSE otherwise.
This was determined in 2025. Replicates needed `>=`3k hops and DTO
`<=` 0.01 in either kemmeren or hackett
- name: id
dtype: string
description: Unique identifier for the metadata record
- config_name: annotated_features_combined
description: >-
Calling Cards replicate data combined at the qbed (genome map) level, with
enrichment and significance called via callingCardsTools. Partitioned by
genome_map_id_set, where each partition corresponds to a set of combined
replicate genome maps for a single regulator.
dataset_type: annotated_features
data_files:
- split: train
path: annotated_features_combined/*/*.parquet
dataset_info:
partitioning:
enabled: true
partition_by:
- genome_map_id_set
path_template: >-
annotated_features_combined/genome_map_id_set={genome_map_id_set}/*.parquet
features:
- name: genome_map_id_set
dtype: string
description: >-
Hyphen-delimited set of genome map IDs corresponding to the combined
replicates for this regulator (partition key)
- name: target_locus_tag
dtype: string
description: Systematic gene identifier for the target gene
role: target_identifier
- name: target_symbol
dtype: string
description: Standard gene symbol for the target gene
role: target_identifier
- name: experiment_hops
dtype: float64
description: >-
Number of transposon insertion events (hops) at target locus in the
experimental sample
role: quantitative_measure
- name: background_hops
dtype: float64
description: >-
Number of transposon insertion events (hops) at target locus in the
background control
role: quantitative_measure
- name: background_total_hops
dtype: float64
description: >-
Total number of background hops across all loci in the control
sample
role: quantitative_measure
- name: experiment_total_hops
dtype: float64
description: >-
Total number of experimental hops across all loci in the
experimental sample
role: quantitative_measure
- name: callingcards_enrichment
dtype: float64
description: >-
Enrichment score calculated as ratio of normalized experimental to
background hops
role: quantitative_measure
- name: poisson_pval
dtype: float64
description: >-
P-value from Poisson test for statistical significance of binding
enrichment
role: quantitative_measure
- name: hypergeometric_pval
dtype: float64
description: >-
P-value from hypergeometric test for statistical significance of
binding enrichment
role: quantitative_measure
- config_name: annotated_features_combined_meta
description: >-
Sample-level metadata for combined Calling Cards experiments including
regulator information, QC flags, and experimental conditions
dataset_type: metadata
applies_to:
- annotated_features_combined
data_files:
- split: train
path: annotated_features_combined_meta.parquet
dataset_info:
features:
- name: genome_map_id_set
dtype: string
description: >-
Hyphen-delimited set of genome map IDs used as the partition key in
annotated_features_combined
- name: pss_id
dtype: string
description: >-
Passing sample set identifier grouping replicates used in this
combined analysis
- name: binding_id
dtype: string
description: Unique identifier for this combined binding measurement record
- name: regulator_locus_tag
dtype: string
description: Systematic gene identifier for the transcription factor
role: regulator_identifier
- name: regulator_symbol
dtype: string
description: Standard gene symbol for the transcription factor
role: regulator_identifier
- name: batch
dtype: string
description: Experimental batch identifier for controlling batch effects
- name: analysis_set
dtype: bool
description: >-
For a TF with more than 1 passing replicate, a combined samples is
created. This is based on the QC done in 2025 for the modeling
paper. See the annotated_features_meta for more details
- name: condition
dtype: string
description: Experimental condition for this sample
role: experimental_condition
- config_name: 2026_analysis_set
description: >-
This is a combination of the combined annotated_features_combined dataset,
and the passing single replicates from the annotated_features dataset.
This is the data that is used for the 2026 modeling paper as predictors
dataset_type: annotated_features
metadata_fields:
- gm_id
- regulator_locus_tag
- regulator_symbol
- experiment_total_hops
- background_total_hops
data_files:
- split: train
path: 2026_analysis_set.parquet
dataset_info:
features:
- name: gm_id
dtype: string
description: >-
genome_map id. If the sample is a combination of multiple samples,
then it is a hyphen-delimited set of genome map IDs corresponding to
the combined replicates for this regulator.
- name: target_locus_tag
dtype: string
description: Systematic gene identifier for the target gene
role: target_identifier
- name: target_symbol
dtype: string
description: Standard gene symbol for the target gene
role: target_identifier
- name: experiment_hops
dtype: float64
description: >-
Number of transposon insertion events (hops) at target locus in the
experimental sample
role: quantitative_measure
- name: background_hops
dtype: float64
description: >-
Number of transposon insertion events (hops) at target locus in the
background control
role: quantitative_measure
- name: background_total_hops
dtype: float64
description: >-
Total number of background hops across all loci in the control
sample
role: quantitative_measure
- name: experiment_total_hops
dtype: float64
description: >-
Total number of experimental hops across all loci in the
experimental sample
role: quantitative_measure
- name: callingcards_enrichment
dtype: float64
description: >-
Enrichment score calculated as ratio of normalized experimental to
background hops
role: quantitative_measure
- name: poisson_pval
dtype: float64
description: >-
P-value from Poisson test for statistical significance of binding
enrichment
role: quantitative_measure
- config_name: genome_map
description: Genome-wide calling cards insertion density data partitioned by batch
dataset_type: genome_map
data_files:
- split: train
path: genome_map/*/*.parquet
dataset_info:
features:
- name: id
dtype: string
description: Unique identifier for each genomic interval
- name: chr
dtype: string
description: Chromosome name (e.g., chrI, chrII, etc.)
- name: start
dtype: float64
description: Start position of genomic interval
- name: end
dtype: float64
description: End position of genomic interval
- name: depth
dtype: float64
description: >-
Number of transposon insertion events (read depth) in this genomic
interval
- name: strand
dtype: string
description: Strand information (+ or -) for the genomic interval
- name: batch
dtype: string
description: Experimental batch identifier
partitioning:
enabled: true
partition_by:
- batch
path_template: genome_map/batch={batch}/*.parquet
- config_name: genome_map_meta
description: >-
Metadata for genome map datasets including regulator information and
experimental details
dataset_type: metadata
applies_to:
- genome_map
- annotated_features_orig_reprocess
data_files:
- split: train
path: genome_map_meta.parquet
dataset_info:
features:
- name: id
dtype: string
description: Unique identifier for the metadata record
- name: binding_id
dtype: string
description: >-
current django managed database identifier for the dataset to the
'binding' table
- name: regulator_locus_tag
dtype: string
description: Systematic identifier for the regulatory factor
role: regulator_identifier
- name: regulator_symbol
dtype: string
description: Standard symbol for the regulatory factor
role: regulator_identifier
- name: batch
dtype: string
description: Experimental batch identifier
role: experimental_condition
- name: replicate
dtype: int64
description: Biological replicate number, within batch
- name: notes
dtype: string
description: Additional notes or comments about the experiment
- name: condition
dtype:
class_label:
names:
- standard
- rapa
- starvation
- glu_1_gal_1
- del_MET28
- glu_1_gal_2
- del_FKH2
- del_TYE7
description: >-
Experimental condition of the sample, including standard growth,
rapamycin treatment, nutrient starvation, mixed carbon source
conditions, and gene deletion strains
role: experimental_condition
definitions:
standard:
media:
name: synthetic_complete
carbon_source:
- compound: D-glucose
concentration_percent: 2
rapa:
perturbation_method:
type: chemical_treatment
compound: rapamycin
description: Rapamycin treatment to inhibit TORC1 signaling
starvation:
description: >-
Nutrient starvation condition - specific media composition not
defined in source
glu_1_gal_1:
media:
carbon_source:
- compound: D-glucose
concentration_percent: 1
- compound: D-galactose
concentration_percent: 1
glu_1_gal_2:
media:
carbon_source:
- compound: D-glucose
concentration_percent: 1
- compound: D-galactose
concentration_percent: 2
del_MET28:
genotype:
deletions:
- gene: MET28
description: MET28 deletion strain
del_FKH2:
genotype:
deletions:
- gene: FKH2
description: FKH2 deletion strain
del_TYE7:
genotype:
deletions:
- gene: TYE7
description: TYE7 deletion strain
- config_name: annotated_features_orig_reprocess
description: >-
Calling Cards annotated features reprocessed from the original qbed genome
maps using scripts/quantify_regions.R. Each record corresponds to a single
genome map (replicate-level), where the id field links to genome_map_meta.
Includes log-transformed p-values and FDR-adjusted q-values not present in
the original annotated_features_combined.
dataset_type: annotated_features
data_files:
- split: train
path: annotated_features_orig_reprocess/*/*.parquet
dataset_info:
features:
- name: id
dtype: int64
description: >-
Genome map identifier linking to the genome_map and genome_map_meta
dataset
- name: target_locus_tag
dtype: string
description: Systematic gene identifier for the target gene
role: target_identifier
- name: target_symbol
dtype: string
description: Standard gene symbol for the target gene
role: target_identifier
- name: experiment_hops
dtype: float64
description: >-
Number of transposon insertion events (hops) at target locus in the
experimental sample
role: quantitative_measure
- name: background_hops
dtype: float64
description: >-
Number of transposon insertion events (hops) at target locus in the
background control
role: quantitative_measure
- name: total_background_hops
dtype: float64
description: >-
Total number of background hops across all loci in the control
sample
role: quantitative_measure
- name: total_experiment_hops
dtype: float64
description: >-
Total number of experimental hops across all loci in the
experimental sample genomic (not mito) chromosomes
role: quantitative_measure
- name: callingcards_enrichment
dtype: float64
description: >-
Enrichment score calculated as ratio of normalized experimental to
background hops
role: quantitative_measure
- name: poisson_pval
dtype: float64
description: >-
P-value from Poisson test for statistical significance of binding
enrichment
role: quantitative_measure
- name: log_poisson_pval
dtype: float64
description: >-
Log-transformed Poisson p-value. This has greater numeric resolution
for significant loci
role: quantitative_measure
- name: poisson_qval
dtype: float64
description: FDR-adjusted q-value from Poisson test (multiple testing correction)
role: quantitative_measure
- name: hypergeometric_pval
dtype: float64
description: >-
P-value from hypergeometric test for statistical significance of
binding enrichment
role: quantitative_measure
- name: log_hypergeometric_pval
dtype: float64
description: Log-transformed hypergeometric p-value
role: quantitative_measure
- name: hypergeometric_qval
dtype: float64
description: >-
FDR-adjusted q-value from hypergeometric test (multiple testing
correction)
role: quantitative_measure
- name: batch
dtype: string
description: >-
Experimental batch identifier for controlling batch effects
(parition key)
Calling Cards
This is data produced in both the Brent Lab and Mitra Lab at Washington University
This repo provides 2 dataset and associated metadata:
- annotated_features: This data scores promoter regions associated with the nearest gene
- genome_map: The binding location data in qbed format
In the annotated features, in order to get the analysis set (you can use duckdb directory instead
of tfbpapi -- see the usage section below):
import pandas as pd
from tfbpapi.HfQueryAPI import HfQueryAPI
# Initialize the Hugging Face query API with the calling cards dataset
callingcards_hf = HfQueryAPI(
repo_id="BrentLab/callingcards",
repo_type="dataset"
)
# Set a filter to only include records where data quality passes QC
callingcards_hf.set_filter("annotated_features", data_usable="pass")
# Query all columns from the annotated_features table
# Returns the data as a pandas DataFrame
callingcards_data = callingcards_hf.query(
"SELECT * FROM annotated_features",
"annotated_features"
)
analysis_data = (
callingcards_data
.assign(
# Create a flag: does this regulator have any composite binding?
has_composite = lambda df: df.groupby('regulator_locus_tag')['composite_binding']
.transform(lambda x: x.notna().any())
)
.query(
# If composite exists for this regulator, require composite to be non-null
# Otherwise, require single_binding to be non-null
'(has_composite & composite_binding.notna()) | '
'(~has_composite & single_binding.notna())'
)
.drop(columns=['has_composite']) # Remove the helper column
)
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/callingcards" dataset from Hugging Face Hub.
from huggingface_hub import ModelCard
from pprint import pprint
card = ModelCard.load("BrentLab/callingcards", 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/callingcards",
repo_type="dataset",
allow_patterns="annotated_features_meta.parquet"
)
dataset_path = os.path.join(repo_path, "annotated_features_meta.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 id = 1:
# Download only a specific sample's genome coverage data
repo_path = snapshot_download(
repo_id="BrentLab/callingcards",
repo_type="dataset",
allow_patterns="annotated_features/id=1/*.parquet"
)
# Query the specific partition
dataset_path = os.path.join(repo_path, "annotated_features")
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/callingcards"
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 annotated_features_meta parquet file
parquet_path = os.path.join(repo_path, "annotated_features_meta.parquet")
print(f"✓ Parquet file at: {parquet_path}")