testing new format

README.md

@@ -29,210 +29,327 @@ experimental_conditions:
           - 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
+  - annotated_feature_meta
+  - genome_map
+  - genome_map_meta
+  - annotated_feature_reprocess_yiming
+  - annotated_feature_reprocess_mindel
+  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
+  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
+  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
+  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
+  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_features
-  description: Calling Cards transcription factor binding data with enrichment scores and statistical significance
+- 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
-  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
   data_files:
   - split: train
-    path: annotated_features/*/*.parquet
+    path: annotated_feature/*/*.parquet
   dataset_info:
     features:
     - name: id
-      dtype: string
+      dtype: int64 
       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
+      role: quantitative_measure
 
-- config_name: annotated_features_meta
-  description: Metadata for annotated features datasets including regulator informatioand data quality indicators
+- config_name: annotated_feature_meta
+  description: Metadata for the annotated_features dataset.
   dataset_type: metadata
-  applies_to: ["annotated_features"]
+  applies_to: ["annotated_feature"]
   data_files:
   - split: train
-    path: annotated_features_meta.parquet
+    path: annotated_feature_meta.parquet
   dataset_info:
     features:
-    - name: db_id
-      dtype: string
-      description: Database identifier for the dataset
-      role: experimental_condition
-    - name: regulator_locus_tag
+    - 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: Systematic identifier for the regulatory factor
-      role: regulator_identifier
-    - name: regulator_symbol
+      description: >-
+        Identifier from a defunct database (promoter set sig id)
+      role: secondary_sample_id
+    - name: binding_id
       dtype: string
-      description: Standard symbol for the regulatory factor
-      role: regulator_identifier
+      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
-      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
+- config_name: annotated_feature_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.
+    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_features_combined/*/*.parquet
+    path: annotated_feature_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"
+      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: 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
+- config_name: annotated_feature_combined_meta
+  description: Metadata for the annotated_feature_combined dataset.
   dataset_type: metadata
-  applies_to: ["annotated_features_combined"]
+  applies_to: ["annotated_feature_combined"]
   data_files:
   - split: train
-    path: annotated_features_combined_meta.parquet
+    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_features_combined
+      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: 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
+        annotated_feature_meta for more details
 
 - 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
+    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:
@@ -246,41 +363,11 @@ configs:
         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
+  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
@@ -288,26 +375,24 @@ configs:
   dataset_info:
     features:
     - name: id
-      dtype: string
+      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: float64
+      dtype: int64
       description: Start position of genomic interval
     - name: end
-      dtype: float64
+      dtype: int64
       description: End position of genomic interval
     - name: depth
-      dtype: float64
+      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
-    - name: batch
-      dtype: string
-      description: Experimental batch identifier
     partitioning:
       enabled: true
       partition_by: ["batch"]
@@ -316,304 +401,158 @@ configs:
 - 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"]
+  applies_to: ["genome_map", "annotated_feature_reprocess_yiming", "annotated_feature_reprocess_mindel"]
   data_files:
   - split: train
     path: genome_map_meta.parquet
   dataset_info:
     features:
     - name: id
-      dtype: string
+      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: 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
+      dtype: float64
       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
+
+- config_name: annotated_feature_reprocess_yiming
   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.
+    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_features_orig_reprocess/*/*.parquet
+    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
   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)
+      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
+  dataset_info:
+    features:
+    - name: genome_map_id
+      dtype: int64
+      description: >-
+        Genome map identifier linking to the genome_map and genome_map_meta dataset
 ---
 # Calling Cards
 
-This is data produced in both the Brent Lab and Mitra Lab at Washington University
+This is data produced in both the Brent Lab and Mitra Lab at Washington University.
+
+## Accessing Data
 
-This repo provides 2 dataset and associated metadata:
+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`).
 
-- **annotated_features**: This data scores promoter regions associated with the nearest gene
-- **genome_map**: The binding location data in qbed format
+### Accessing Data with labretriever
 
-In the annotated features, in order to get the analysis set (you can use duckdb directory instead
-of `tfbpapi` -- see the usage section below):
+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
-import pandas as pd
-from tfbpapi.HfQueryAPI import HfQueryAPI
+from labretriever.virtual_db import VirtualDB
+from labretriever.datacard import DataCard
 
-# Initialize the Hugging Face query API with the calling cards dataset
-callingcards_hf = HfQueryAPI(
-    repo_id="BrentLab/callingcards", 
-    repo_type="dataset"
-)
+# Citation and metadata
+card = DataCard("BrentLab/callingcards")
+print([c.config_name for c in card.configs])  # list available datasets
 
-# Set a filter to only include records where data quality passes QC
-callingcards_hf.set_filter("annotated_features", data_usable="pass")
+# print citation
+info = card.info()
+print(info["citation"])
 
-# 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"
-)
+# path to the downloaded brentlab_yeast_collection.yaml
+vdb = VirtualDB("/path/to/brentlab_yeast_collection.yaml")
 
-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
-)
+print(vdb.get_dataset_description("callingcards"))
+vdb.query("SELECT * FROM callingcards LIMIT 5")
 ```
 
-## Usage
+### Direct parquet access
 
-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.
+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.
 
-### `tfbpapi`
+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.
 
-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.
+Single parquet file example:
 
 ```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"
+    allow_patterns="annotated_feature_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)
+# 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()
 ```
 
-We might choose to take a look at the file with id = 1:
+Partitioned dataset example (the `annotated_feature` directory):
 
 ```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"
+    allow_patterns="annotated_feature/**",
 )
-
-# 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)
+conn.execute(
+    "SELECT * FROM read_parquet(?) LIMIT 5",
+    [f"{repo_path}/annotated_feature/**/*.parquet"],
+).df()
 ```
 
-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:
+### Accessing using R
 
-```python
-
-repo_id = "BrentLab/callingcards"
-
-hf_token = os.getenv("HF_TOKEN")
+Clone the repository and read parquet files directly with
+[arrow](https://arrow.apache.org/docs/r/):
 
-# 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}")
+```r
+# install.packages("arrow")
+arrow::read_parquet("annotated_feature_meta.parquet")
+```