--- 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: 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"\nāœ“ 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"āœ“ Parquet file at: {parquet_path}")