--- 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](https://github.com/cmatKhan/labretriever#installation) (`pip install labretriever`) and/or the [HuggingFace Hub client](https://huggingface.co/docs/huggingface_hub/installation) (`pip install huggingface_hub`). ### Accessing Data with labretriever This repository is part of a collection configured as a unified database using [labretriever.VirtualDB](https://cmatkhan.github.io/labretriever/virtual_db_configuration/). Download the [collection config](https://github.com/BrentLab/tfbpshiny/blob/main/tfbpshiny/brentlab_yeast_collection.yaml) and use it to query the data directly in Python, or with an AI assistant using the [labretriever plugin](https://cmatkhan.github.io/labretriever/mcp_server/#quick-install-claude-code-plugin). ```python 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: ```python 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): ```python 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](https://arrow.apache.org/docs/r/): ```r # install.packages("arrow") arrow::read_parquet("annotated_feature_meta.parquet") ```