README.md

---
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
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):

```python
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](https://github.com/BrentLab/tfbpapi/?tab=readme-ov-file#installation), you can
adapt this [tutorial](https://brentlab.github.io/tfbpapi/tutorials/hfqueryapi_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.

```python
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.

```python
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:

```python
# 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](https://huggingface.co/docs/hub/en/security-tokens).
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:

```python

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}")