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
citation: >-
Mateusiak, C, Erdenebaatar, Z, Jia, E, Plaggenberg, JN, Wang, Y, Shively, C,
Liao, G, Mitra, RD, Brent, MR. 2026. Functional synergy partially explains why
most transcription factor binding is non-functional. bioRxiv 2026.
doi: https://doi.org/10.64898/2026.01.19.700460
features:
- applies_to:
- genome_map_meta
- annotated_feature_meta
- annotated_feature_combined_meta
fields:
- 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
- applies_to:
- annotated_feature_reprocess_intergenic
- annotated_feature_reprocess_intergenic_analysis
fields:
- name: ir_name
dtype: string
description: >-
Unique identifier of the intergenic region. See
yeast_genome_resources/intergenic_regions_metadata_5_1.csv for details
on the region (location, etc). Note that these intergenic regions are
defined as the region between the end of one ORF and the start of the
next, and are named according to the locus tags of the flanking ORFs
(e.g., YAL001C-YAL002W). A intergenic region is assigned to a
promoter only when the 5' end is continuous with the region.
- applies_to:
- annotated_feature
- annotated_feature_meta
- genome_map
- genome_map_meta
- annotated_feature_reprocess_yiming
- annotated_feature_reprocess_mindel
- annotated_feature_reprocess_start_codon_500
- annotated_feature_reprocess_intergenic
fields:
- name: batch
dtype: string
description: >-
Experimental batch identifier for controlling batch effects (partition
key)
role: experimental_condition
- applies_to:
- annotated_feature_meta
- annotated_feature_combined_meta
- genome_map_meta
- 2026_analysis_set
- annotated_feature_reprocess_mindel_analysis
- annotated_feature_reprocess_start_codon_500bp_analysis
- annotated_feature_reprocess_intergenic_analysis
fields:
- 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
- applies_to:
- annotated_feature
- annotated_feature_combined
- 2026_analysis_set
- annotated_feature_reprocess_yiming
- annotated_feature_reprocess_mindel
- annotated_feature_reprocess_mindel_analysis
- annotated_feature_reprocess_start_codon_500
- annotated_feature_reprocess_start_codon_500bp_analysis
- annotated_feature_reprocess_intergenic
- annotated_feature_reprocess_intergenic_analysis
fields:
- 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
- applies_to:
- annotated_feature
- annotated_feature_combined
- 2026_analysis_set
- annotated_feature_reprocess_yiming
- annotated_feature_reprocess_mindel
- annotated_feature_reprocess_mindel_analysis
- annotated_feature_reprocess_start_codon_500
- annotated_feature_reprocess_start_codon_500bp_analysis
- annotated_feature_reprocess_intergenic
- annotated_feature_reprocess_intergenic_analysis
fields:
- 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: 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
- applies_to:
- annotated_feature
- annotated_feature_combined
- 2026_analysis_set
fields:
- 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
- applies_to:
- annotated_feature_reprocess_yiming
- annotated_feature_reprocess_mindel
- annotated_feature_reprocess_mindel_analysis
- annotated_feature_reprocess_start_codon_500
- annotated_feature_reprocess_start_codon_500bp_analysis
- annotated_feature_reprocess_intergenic
- annotated_feature_reprocess_intergenic_analysis
fields:
- 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: 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
configs:
- config_name: annotated_feature
description: >-
This is data that was originally processed through
https://github.com/cmatKhan/callingCardsTools/ and stored (including some
more processing) in https://github.com/cmatKhan/yeastregulatorydb. It is
the data that was used for the QC and filtering decisions in the 2026
modeling paper. In general, unless you are trying to exactly replicate the
2026 modeling paper, you should use the 2026_analysis_set for analysis
that uses the published results. Or, to use data that can be reproduced
directly from the genome_map data, `annotated_feature_reprocess_*`. The
suffix indicates which promoter set was used to generate the results from
the genome_map data.
dataset_type: annotated_features
genome_resources:
region_sets:
Kang:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/yiming_promoters.bed
join_column: target_locus_tag
data_files:
- split: train
path: annotated_feature/*/*.parquet
partitioning:
enabled: true
partition_by:
- batch
path_template: annotated_feature/batch={batch}/*.parquet
dataset_info:
features:
- name: id
dtype: int64
description: Unique identifier for each binding measurement
- name: hypergeometric_pval
dtype: float64
description: >-
P-value from hypergeometric test for statistical significance of
binding enrichment
role: quantitative_measure
- config_name: annotated_feature_meta
description: Metadata for the annotated_features dataset.
dataset_type: metadata
applies_to:
- annotated_feature
data_files:
- split: train
path: annotated_feature_meta.parquet
dataset_info:
features:
- name: id
dtype: float64
description: Unique identifier for the metadata record
role: sample_id
- name: genome_map_id
dtype: float64
description: >-
Genome map identifier linking to the genome_map and genome_map_meta
dataset
role: secondary_sample_id
- name: pss_id
dtype: string
description: Identifier from a defunct database (promoter set sig id)
role: secondary_sample_id
- name: binding_id
dtype: string
description: Identifier from a defunct database (binding id)
role: secondary_sample_id
- name: data_usable
dtype: string
description: Indicator of whether the data is suitable for analysis
- 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
- config_name: annotated_feature_combined
description: >-
For the 2026 modeling paper, we labeled replicates passing if it has
`>=`3k hops and DTO `<=` 0.01 in either kemmeren or hackett. For a TF with
more than 1 passing replicate, a combined sample is created by summing the
hops across the passing replicates. This is the data that is used for the
2026 modeling paper as predictors. It is retained here for replication and
transparency, but we do not recommend using it for new analysis. Instead,
to use the published results, use the `2026_analysis_set` which includes
the same combined samples, but also includes the passing single
replicates. Otherwise, the annotated_feature_reprocess_*_analysis datasets
are more directly reproducible from the genome_map data, using a specified
promoter set, and have combined samples using the same logic.
dataset_type: annotated_features
genome_resources:
region_sets:
Kang:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/yiming_promoters.bed
join_column: target_locus_tag
data_files:
- split: train
path: annotated_feature_combined/*/*.parquet
dataset_info:
partitioning:
enabled: true
partition_by:
- genome_map_id_set
path_template: >-
annotated_feature_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: hypergeometric_pval
dtype: float64
description: >-
P-value from hypergeometric test for statistical significance of
binding enrichment
role: quantitative_measure
- config_name: annotated_feature_combined_meta
description: Metadata for the annotated_feature_combined dataset.
dataset_type: metadata
applies_to:
- annotated_feature_combined
data_files:
- split: train
path: annotated_feature_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_feature_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: 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_feature_meta for more details
- config_name: 2026_analysis_set
description: >-
This dataset is the dataset that was used in the 2026 modeling paper. A
passing replicate has >=3000 hops had a dto empirical pvalue < 0.01
against either kemmeren or hackett. Where a given regulator had multiple
passing replicates, those replicates were combined (see
annotated_feature_combined). This dataset should be used when you want to
use the published results from the 2026 modeling paper. If you want to use
data that can be reproduced directly from the genome_map data included in
this repo, especially when called against different promoter sets, then
use the annotated_feature_reprocess_*_analysis datasets.
default: true
genome_resources:
region_sets:
Kang:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/yiming_promoters.bed
join_column: target_locus_tag
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.
- config_name: genome_map
description: >-
This is the raw binding data (qbeds) from the nf-core/callingcards
pipeline. It can be processed into annotated_feature datasets suing the
scripts/quantify_regions.R script. You can use your own promoter
definitions (bed format) to do this, or those provided in
BrentLab/yeast_genome_resources
dataset_type: genome_map
data_files:
- split: train
path: genome_map/*/*.parquet
dataset_info:
features:
- name: id
dtype: int64
description: Unique identifier for each genomic interval
role: sample_id
- name: chr
dtype: string
description: Chromosome name (e.g., chrI, chrII, etc.)
- name: start
dtype: int64
description: Start position of genomic interval
- name: end
dtype: int64
description: End position of genomic interval
- name: depth
dtype: int64
description: >-
Number of transposon insertion events (read depth) in this genomic
interval
- name: strand
dtype: string
description: Strand information (+ or -) for the genomic interval
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_feature_reprocess_yiming
- annotated_feature_reprocess_mindel
- annotated_feature_reprocess_start_codon_500
- annotated_feature_reprocess_intergenic
data_files:
- split: train
path: genome_map_meta.parquet
dataset_info:
features:
- name: id
dtype: float64
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: replicate
dtype: float64
description: Biological replicate number, within batch
- name: notes
dtype: string
description: Additional notes or comments about the experiment
- config_name: annotated_feature_reprocess_yiming
description: >-
Calling Cards annotated features reprocessed from the genome_map data
using scripts/quantify_regions.R against the yiming promoters in
BrentLab/yeast_genome_resources. This is very nearly exactly the same as
annotated_features, though there may be some differences around the
boundaries (intentional), and this includes higher numeric resolution in
the most significant promoters by using hte log argument in the poisson
distribution function.
dataset_type: annotated_features
data_files:
- split: train
path: annotated_feature_reprocess_yiming/*/*.parquet
genome_resources:
region_sets:
Kang:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/yiming_promoters.bed
join_column: target_locus_tag
partitioning:
enabled: true
partition_by:
- batch
path_template: annotated_feature_reprocess_yiming/batch={batch}/*.parquet
dataset_info:
features:
- name: id
dtype: int64
description: >-
Genome map identifier linking to the genome_map and genome_map_meta
dataset
- config_name: annotated_feature_reprocess_mindel
description: >-
This is the genome_map data quantified against the Mindel promoters (see
BrentLab/yeast_genome_resources) using scripts/quantify_regions.R.
dataset_type: annotated_features
data_files:
- split: train
path: annotated_feature_reprocess_mindel/*/*.parquet
genome_resources:
region_sets:
Mindel:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/mindel_promoters.csv.gz
join_column: target_locus_tag
partitioning:
enabled: true
partition_by:
- batch
path_template: annotated_feature_reprocess_mindel/batch={batch}/*.parquet
dataset_info:
features:
- name: genome_map_id
dtype: int64
description: >-
Genome map identifier linking to the genome_map and genome_map_meta
dataset
- config_name: annotated_feature_reprocess_start_codon_500
description: >-
This is the genome_map data quantified against the promoters defined as
500bp upstream of the start codon for each gene (see
BrentLab/yeast_genome_resources) using
scripts/quantify_regions_500bp_intergenic.R.
dataset_type: annotated_features
data_files:
- split: train
path: annotated_feature_reprocess_start_codon_500/*/*.parquet
genome_resources:
region_sets:
start_codon_500bp:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/start_codon_500bp_upstream_promoters.bed
join_column: target_locus_tag
partitioning:
enabled: true
partition_by:
- batch
path_template: annotated_feature_reprocess_start_codon_500/batch={batch}/*.parquet
dataset_info:
features:
- name: genome_map_id
dtype: int64
description: >-
Genome map identifier linking to the genome_map and genome_map_meta
dataset
- config_name: annotated_feature_reprocess_intergenic
description: >-
This is the genome_map data quantified against the promoters defined as
the full intergenic region upstream of each gene (see
BrentLab/yeast_genome_resources) using
scripts/quantify_regions_500bp_intergenic.R.
dataset_type: annotated_features
data_files:
- split: train
path: annotated_feature_reprocess_intergenic/*/*.parquet
genome_resources:
region_sets:
intergenic:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/intergenic_regions_metadata_5_1.csv
join_column: ir_name
partitioning:
enabled: true
partition_by:
- batch
path_template: annotated_feature_reprocess_intergenic/batch={batch}/*.parquet
dataset_info:
features:
- name: genome_map_id
dtype: int64
description: >-
Genome map identifier linking to the genome_map and genome_map_meta
dataset
- config_name: annotated_feature_reprocess_mindel_analysis
description: >-
This is the analysis set for the mindel data. It is generated using the
same logic as the 2026_analysis_set, but using the results from the
reprocessing against the mindel promoters. A passing replicate has >=3000
hops had a dto empirical pvalue < 0.01 against either kemmeren or hackett.
Where a given regulator had multiple passing replicates, those replicates
were combined (see annotated_feature_combined). This dataset should be
used when you want to use data that can be reproduced directly from the
genome_map data included in this repo, using the mindel promoter
definitions. See scripts/quantify_regions.R as well as
BrentLab/yeast_comparative_analysis/scripts for details of how this was
conducted.
dataset_type: annotated_features
metadata_fields:
- combined_id
- regulator_locus_tag
- regulator_symbol
- total_experiment_hops
- total_background_hops
data_files:
- split: train
path: annotated_feature_reprocess_mindel_analysis.parquet
genome_resources:
region_sets:
Mindel:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/mindel_promoters.csv.gz
join_column: target_locus_tag
dataset_info:
features:
- name: combined_id
dtype: string
description: >-
The genome map identifier of either a single passing, or multiple
passing replicates (hyphen-delimited) that were combined for the
analysis set. replicates were combined if the DTO empirical pvalue
was <= 0.01 in either kemmeren or hackett. After combining, if they
have more than 3k hopes (single or combined), then they are included
in the analysis set.
role: sample_id
- config_name: annotated_feature_reprocess_start_codon_500bp_analysis
description: >-
This is the analysis set for promoters created with 500bp regions upstream
of the start codon. It uses the same passing replicates as the
2025_analysis_set. see R/scripts/quantify_regions_500bp_intergenic.R for
details of how this was conducted.
dataset_type: annotated_features
metadata_fields:
- combined_id
- regulator_locus_tag
- regulator_symbol
- total_experiment_hops
- total_background_hops
data_files:
- split: train
path: annotated_feature_reprocess_start_codon_500bp_analysis.parquet
genome_resources:
region_sets:
start_codon_500bp:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/start_codon_500bp_upstream_promoters.bed
join_column: target_locus_tag
dataset_info:
features:
- name: combined_id
dtype: string
description: >-
The genome map identifier of either a single passing, or multiple
passing replicates (hyphen-delimited) that were combined for the
2026 analysis set.
role: sample_id
- config_name: annotated_feature_reprocess_intergenic_analysis
description: >-
This is the analysis set for promoters created with the full intergenic
region upstream of a given target. It uses the same passing replicates as
the 2025_analysis_set. see R/scripts/quantify_regions_500bp_intergenic.R
for details of how this was conducted.
dataset_type: annotated_features
metadata_fields:
- combined_id
- regulator_locus_tag
- regulator_symbol
- total_experiment_hops
- total_background_hops
data_files:
- split: train
path: annotated_feature_reprocess_intergenic_analysis.parquet
genome_resources:
region_sets:
intergenic:
path: >-
https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/intergenic_regions_metadata_5_1.csv
join_column: ir_name
dataset_info:
features:
- name: combined_id
dtype: string
description: >-
The genome map identifier of either a single passing, or multiple
passing replicates (hyphen-delimited) that were combined for the
2026 analysis set.
role: sample_id
Calling Cards
This is data produced in both the Brent Lab and Mitra Lab at Washington University.
Accessing Data
The examples below require
labretriever
(pip install labretriever) and/or the
HuggingFace Hub client
(pip install huggingface_hub).
Accessing Data with labretriever
This repository is part of a collection configured as a unified database using labretriever.VirtualDB. Download the collection config and use it to query the data directly in Python, or with an AI assistant using the labretriever plugin.
from labretriever.virtual_db import VirtualDB
from labretriever.datacard import DataCard
# Citation and metadata
card = DataCard("BrentLab/callingcards")
print([c.config_name for c in card.configs]) # list available datasets
# print citation
info = card.info()
print(info["citation"])
# path to the downloaded brentlab_yeast_collection.yaml
vdb = VirtualDB("/path/to/brentlab_yeast_collection.yaml")
print(vdb.get_dataset_description("callingcards"))
vdb.query("SELECT * FROM callingcards LIMIT 5")
Direct parquet access
The repository contains more data than what is exposed through the collection
configuration. Use DataCard.info() to inspect available files, then download
and query with DuckDB.
Some files are single parquet files (e.g. metadata files); others are partitioned datasets. Download a metadata file first to identify relevant partitions before fetching the full data.
Single parquet file example:
from huggingface_hub import snapshot_download
import duckdb
repo_path = snapshot_download(
repo_id="BrentLab/callingcards",
repo_type="dataset",
allow_patterns="annotated_feature_meta.parquet",
)
conn = duckdb.connect()
# returns a pandas DataFrame with the first 5 rows
conn.execute(
"SELECT * FROM read_parquet(?) LIMIT 5",
[f"{repo_path}/annotated_feature_meta.parquet"],
).df()
Partitioned dataset example (the annotated_feature directory):
repo_path = snapshot_download(
repo_id="BrentLab/callingcards",
repo_type="dataset",
allow_patterns="annotated_feature/**",
)
conn.execute(
"SELECT * FROM read_parquet(?) LIMIT 5",
[f"{repo_path}/annotated_feature/**/*.parquet"],
).df()
Accessing using R
Clone the repository and read parquet files directly with arrow:
# install.packages("arrow")
arrow::read_parquet("annotated_feature_meta.parquet")