--- license: mit tags: - transcription-factor - binding - chipexo - genomics - biology language: - en pretty_name: Rossi ChIP-exo 2021 experimental_conditions: temperature_celsius: 25 cultivation_method: unspecified growth_phase_at_harvest: phase: mid_log od600: 0.8 media: name: yeast_peptone_dextrose carbon_source: - compound: D-glucose concentration_percent: unspecified nitrogen_source: - compound: yeast_extract concentration_percent: unspecified - compound: peptone concentration_percent: unspecified # Heat shock applied only to SAGA strains # note that im not sure which strains this # applies to -- it is a TODO to better # document this heat_shock: induced: true temperature_celsius: 37 duration_minutes: 6 pre_induction_temperature_celsius: 25 method: equal_volume_medium_transfer configs: - config_name: metadata description: Metadata describing the tagged regulator in each experiment dataset_type: metadata data_files: - split: train path: rossi_2021_metadata.parquet dataset_info: features: - 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: run_accession dtype: string description: GEO run accession identifier for the sample - name: yeastepigenome_id dtype: string description: Sample identifier used by yeastepigenome.org - config_name: genome_map description: "ChIP-exo 5' tag coverage data partitioned by sample accession" dataset_type: genome_map data_files: - split: train path: genome_map/*/*.parquet dataset_info: features: - name: chr dtype: string description: Chromosome name (e.g., chrI, chrII, etc.) - name: pos dtype: int32 description: "Genomic position of the 5' tag" - name: pileup dtype: int32 description: "Depth of coverage (number of 5' tags) at this genomic position" - config_name: rossi_annotated_features description: ChIP-exo regulator-target binding features with peak statistics dataset_type: annotated_features default: true metadata_fields: - regulator_locus_tag - regulator_symbol - target_locus_tag data_files: - split: train path: yeastepigenome_annotatedfeatures.parquet dataset_info: features: - name: sample_id dtype: int32 description: >- Unique identifier for each ChIP-exo experimental sample. - name: pss_id dtype: float64 description: >- Current brentlab promotersetsig table id. This will eventually be removed. - name: binding_id dtype: float64 description: >- Current brentlab binding table id. This will eventually be removed. - name: yeastepigenome_id dtype: float64 description: >- Unique identifier in the yeastepigenome database. - name: regulator_locus_tag dtype: string description: >- Systematic ORF name of the regulator. role: regulator_identifier - name: regulator_symbol dtype: string description: >- Common gene name of the regulator. role: regulator_identifier - name: target_locus_tag dtype: string description: >- The systematic ID of the feature to which the effect/pvalue is assigned. See hf/BrentLab/yeast_genome_resources role: target_identifier - name: target_symbol dtype: string description: >- The common name of the feature to which the effect/pvalue is assigned. If there is no common name, the `target_locus_tag` is used. role: target_identifier - name: n_sig_peaks dtype: float64 description: >- Number of peaks in the promoter region of the the target gene role: quantitative_measure - name: max_fc dtype: float64 description: >- If there are multiple peaks in the promoter region, then the maximum is reported. Otherwise, it is the fold change of the single peak in the promoter. role: quantitative_measure - name: min_pval dtype: float64 description: >- The most significant p-value among peaks for this interaction. role: quantitative_measure - config_name: reprocess_annotatedfeatures description: >- Annotated features reprocessed with updated peak calling methodology dataset_type: annotated_features data_files: - split: train path: reprocess_annotatedfeatures.parquet dataset_info: features: - 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: baseMean dtype: float64 description: Average of normalized count values, dividing by size factors, taken over all samples - name: log2FoldChange dtype: float64 description: Log2 fold change between comparison and control groups - name: lfcSE dtype: float64 description: Standard error estimate for the log2 fold change estimate - name: stat dtype: float64 description: Value of the test statistic for the gene - name: pvalue dtype: float64 description: P-value of the test for the gene - name: padj dtype: float64 description: Adjusted p-value for multiple testing for the gene - config_name: reprocess_annotatedfeatures_tagcounts description: Another version of the reprocessed data, quantified similarly to Calling Cards dataset_type: annotated_features data_files: - split: train path: reprocess_annotatedfeatures_tagcounts.parquet dataset_info: features: - name: regulator_locus_tag dtype: string description: Systematic gene name (ORF identifier) of the transcription factor role: regulator_identifier - name: target_locus_tag dtype: string description: Systematic gene name (ORF identifier) of the target gene role: target_identifier - name: rank dtype: int64 description: Rank (ties method min rank) of the peak based on pvalue with ties broken by enrichment. Largest rank is most significant. - name: control_count dtype: int64 description: Number of tags in the control condition - name: experimental_count dtype: int64 description: Number of tags in the experimental condition - name: mu dtype: float64 description: Expected count under the null hypothesis (control_count + 1) * (experimental_total_tags / control_total_tags) - name: enrichment dtype: float64 description: Enrichment ratio of experimental over control. (experimental_counts / experimental_total) / (control_counts + pseudocount) / control_total role: quantitative_measure - name: log2_enrichment dtype: float64 description: Log2-transformed enrichment ratio role: quantitative_measure - name: neg_log10_pvalue dtype: float64 description: Negative log10 of the p-value for binding significance role: quantitative_measure - name: neg_log10_qvalue dtype: float64 description: Negative log10 of the FDR-adjusted q-value role: quantitative_measure --- # Rossi 2021 This data is gathered from [yeastepigenome.org](https://yeastepigenome.org/). This work was published in [Rossi MJ, Kuntala PK, Lai WKM, Yamada N, Badjatia N, Mittal C, Kuzu G, Bocklund K, Farrell NP, Blanda TR, Mairose JD, Basting AV, Mistretta KS, Rocco DJ, Perkinson ES, Kellogg GD, Mahony S, Pugh BF. A high-resolution protein architecture of the budding yeast genome. Nature. 2021 Apr;592(7853):309-314. doi: 10.1038/s41586-021-03314-8. Epub 2021 Mar 10. PMID: 33692541; PMCID: PMC8035251.](https://doi.org/10.1038/s41586-021-03314-8) This repo provides 4 datasets: - **rossi_2021_metadata**: Metadata describing the tagged regulator in each experiment. - **genome_map**: ChIP-exo 5' tag coverage data partitioned by sample accession. - **reprocess_annotatedfeatures**: This data was reprocessed from the fastq files on GEO. See scripts/reprocessing_details.txt for more information. - **yeastepigenome_annotatedfeatures**: ChIP-exo regulator-target binding features with peak statistics. - **reprocess_annotatedfeatures_tagcounts**: Reprocessed using a similar method to the calling cards quantification ## 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/rossi_2021" dataset from Hugging Face Hub. ```python from huggingface_hub import ModelCard from pprint import pprint card = ModelCard.load("BrentLab/rossi_2021", 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/rossi_2021", repo_type="dataset", allow_patterns="rossi_2021_metadata.parquet" ) dataset_path = os.path.join(repo_path, "rossi_2021_metadata.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 accession SRR11466106: ```python # Download only a specific sample's genome coverage data repo_path = snapshot_download( repo_id="BrentLab/rossi_2021", repo_type="dataset", allow_patterns="genome_map/accession=SRR11466106/*.parquet" ) # Query the specific partition dataset_path = os.path.join(repo_path, "genome_map") 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/rossi_2021" 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 rossi_annotated_features parquet file parquet_path = os.path.join(repo_path, "yeastepigenome_annotatedfeatures.parquet") print(f"āœ“ Parquet file at: {parquet_path}") ```