Update README.md

README.md

@@ -158,48 +158,67 @@ configs:
         dtype: float64
         description: Adjusted p-value for multiple testing for the gene
 ---
-
 # Rossi 2021
 
-This data is gathered from [yeastepigenome.org](https://yeastepigenome.org/). This work was published in
+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)
 
-## Dataset details
+This repo provides 4 datasets:
 
-`genome_map` is fully reprocessed data from the sequence files. I used the nf-core/chipseq pipeline, details for which can be found in `scripts/`. With
-those bams, I filtered the reads using `samtools` and the same settings specified in Rossi et al 2021, and then counted 5' ends using bedtools. See
-`scripts/count_tags.sh`.
+- 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.
 
-## Data Structure
+## Usage
 
-### Metadata
+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.
 
-| Field                 | Description                                                       |
-|-----------------------|-------------------------------------------------------------------|
-| `regulator_locus_tag` | Systematic gene name (ORF identifier) of the transcription factor |
-| `regulator_symbol`    | Standard gene symbol of the transcription factor                  |
-| `run_accession`       | GEO run accession identifier for the sample                       |
-| `yeastepigenome_id`   | Sample identifier used by yeastepigenome.org                      |
+### `tfbpapi`
 
-### Genome Map
+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.
 
-| Field    | Description                                                    |
-|----------|----------------------------------------------------------------|
-| `chr`    | Chromosome name, ucsc (e.g., chrI, chrII, etc.)                |
-| `pos`    | Genomic position of the 5' tag                                 |
-| `pileup` | Depth of coverage (number of 5' tags) at this genomic position |
+### huggingface_cli/duckdb
 
-## Usage
+You can retrieves and displays the file paths for each configuration of0
+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")
 
-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.
+# 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)
+```
+
+### explore metadata
+
+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",
@@ -229,3 +248,28 @@ 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"\n✓ 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"✓ Parquet file at: {parquet_path}")
+```