Upload README.md with huggingface_hub

README.md

@@ -1,23 +1,13 @@
 ---
-License: Apache-2.0
-
-tags:
-  - biology
-  - protein
-  - structure
-  - PDB
-  - PISCES
-  - CullPDB
-  - sequence
-  - curation
-language: en
-
+# Dataset Viewer: configs first so the viewer shows the table and subset dropdown
+viewer: true
+# Dataset Viewer dropdown; default = small subset so preview loads (main has 4.5M rows)
 configs:
-- config_name: main
-  data_files: curated_csv/cullpdb_compiled.csv
-  default: true
 - config_name: cullpdb_pc15.0_res0.0-1.0_len40-10000_R0.2_Xray_d2026_01_26_chains281
   data_files: curated_csv/subsets/cullpdb_pc15.0_res0.0-1.0_len40-10000_R0.2_Xray_d2026_01_26_chains281.csv
+  default: true
+- config_name: main
+  data_files: curated_csv/cullpdb_combined_chains.csv
 - config_name: cullpdb_pc15.0_res0.0-1.0_noBrks_len40-10000_R0.2_Xray_d2026_01_26_chains267
   data_files: curated_csv/subsets/cullpdb_pc15.0_res0.0-1.0_noBrks_len40-10000_R0.2_Xray_d2026_01_26_chains267.csv
 - config_name: cullpdb_pc15.0_res0.0-1.2_len40-10000_R0.2_Xray_d2026_01_26_chains785
@@ -500,6 +490,21 @@ configs:
   data_files: curated_csv/subsets/cullpdb_pc95.0_res0.0-5.0_len40-10000_R1.0_Xray+Nmr+EM_d2026_01_26_chains79005.csv
 - config_name: cullpdb_pc95.0_res0.0-5.0_noBrks_len40-10000_R1.0_Xray+Nmr+EM_d2026_01_26_chains62732
   data_files: curated_csv/subsets/cullpdb_pc95.0_res0.0-5.0_noBrks_len40-10000_R1.0_Xray+Nmr+EM_d2026_01_26_chains62732.csv
+license: cc-by-4.0
+task_categories:
+  - other
+tags:
+  - biology
+  - protein
+  - structure
+  - PDB
+  - PISCES
+  - CullPDB
+  - sequence
+  - curation
+language: en
+size_categories:
+  - n>1M
 dataset_info:
   config_name: main
   features:
@@ -533,19 +538,13 @@ dataset_info:
   num_rows: 4540884
 # Main and index paths; full list of subset paths is in dataset_metadata.json
 data_paths:
-  main_csv: curated_csv/cullpdb_compiled.csv
+  main_csv: curated_csv/cullpdb_combined_chains.csv
   index_csv: curated_csv/cullpdb_list_fasta_index.csv
   full_list_csv: curated_csv/cullpdb_full_compiled_list.csv
   subsets_dir: curated_csv/subsets
   metadata: curated_csv/dataset_metadata.json
 ---
 
-# Dataset Description
-
-The PISCES dataset provides curated sets of protein sequences from the Protein Data Bank (PDB) based on user-defined sequence identity and structural quality criteria. PISCES allows users to obtain non-redundant subsets of protein chains by applying filters such as sequence identity, resolution, R-value, chain length, and experiment type (e.g., X-ray, NMR).
-
-Unlike other culling tools that rely on BLAST or global alignments, PISCES uses PSI-BLAST to create position-specific scoring matrices for more accurate detection of homologous relationships, especially below 40% sequence identity. The server supports culling of:
-
 # PISCES-CulledPDB — Curated chain CSVs
 
 Curated protein chain tables derived from PISCES/CullPDB-style lists and FASTA files. One row per chain with sequence, metadata, and curation parameters.
@@ -554,46 +553,18 @@ Curated protein chain tables derived from PISCES/CullPDB-style lists and FASTA f
 
 On the dataset page, use the **subset dropdown** above the table to switch between the main combined table and any of the **242** curation subsets (same columns, different filters).
 
-
-PISCES was created to provide high-quality, non-redundant sets of protein chains for computational analysis. The goal is to maximize structural reliability while minimizing sequence redundancy.
-
-**Source Data**
-
-Protein sequences from the RCSB PDB (mmCIF format, Uniformity Project).
-
-Sequence identity matrices generated using PSI-BLAST against the non-redundant protein database.
-
-PISCES: A Protein Sequence Culling Server https://dunbrack.fccc.edu/pisces/
-
-@article{wang2003pisces,
-  title={PISCES: a protein sequence culling server},
-  author={Wang, Guoying and Dunbrack, Roland L. Jr.},
-  journal={Bioinformatics},
-  volume={19},
-  number={12},
-  pages={1589--1591},
-  year={2003},
-  publisher={Oxford University Press}
-}
-
-Authors contact
-Qifang Xu: Qifang.Xu@fccc.edu
-Roland Dunbrack: Roland.Dunbrack@fccc.edu
-
-
-Recurated in Hugging Face by: Akshaya Narayanasamy (akshayanarayanasamy[at]gmail.com)
 ## Dataset Summary
 
 | Item | Description |
 |------|-------------|
-| **Main CSV** | `curated_csv/cullpdb_compiled.csv` — single table with **4,540,884** chains (one row per chain). |
+| **Main CSV** | `curated_csv/cullpdb_combined_chains.csv` — single table with **4,540,884** chains (one row per chain). |
 | **Subset CSVs** | `curated_csv/subsets/*.csv` — **242** files, one per curation subset (same columns as main). |
 | **Index** | `curated_csv/cullpdb_list_fasta_index.csv` — **242** rows; maps subset basenames to list/FASTA paths and parameters. |
 
 ## Data paths (metadata)
 
 **Main and index files:**
-- `curated_csv/cullpdb_compiled.csv` — master chain table
+- `curated_csv/cullpdb_combined_chains.csv` — master chain table
 - `curated_csv/cullpdb_list_fasta_index.csv` — subset index
 - `curated_csv/cullpdb_full_compiled_list.csv` — full compiled list
 - `curated_csv/subsets/` — directory containing one CSV per subset (**242** files)
@@ -604,21 +575,21 @@ Recurated in Hugging Face by: Akshaya Narayanasamy (akshayanarayanasamy[at]gmail
 
 Each chain row has the following columns:
 
-| Column |Description |
-|--------|-------------|
-| **pdb_chain** |PDB chain ID (e.g. 1ABC_A) |
-| **pdb** | PDB ID (first 4 chars) |
-| **chain** |Chain ID |
-| **sequence** |Amino acid sequence (one-letter) |
-| **len** |Sequence length |
-| **method** |Experimental method (e.g. XRAY, NMR) |
-| **resolution** |Resolution in Å (per structure) |
-| **rfac** |R-factor |
-| **freerfac** |Free R-factor |
-| **pc** |Sequence identity cutoff % used for this subset |
-| **no_breaks** | Whether chain has no breaks (yes/no) |
-| **R** |R-factor cutoff used for this subset |
-| **source_list** |Subset list basename (identifies curation parameters) |
+| Column | Type | Description |
+|--------|------|-------------|
+| **pdb_chain** | string/number | PDB chain ID (e.g. 1ABC_A) |
+| **pdb** | string/number | PDB ID (first 4 chars) |
+| **chain** | string/number | Chain ID |
+| **sequence** | string/number | Amino acid sequence (one-letter) |
+| **len** | string/number | Sequence length |
+| **method** | string/number | Experimental method (e.g. XRAY, NMR) |
+| **resolution** | string/number | Resolution in Å (per structure) |
+| **rfac** | string/number | R-factor |
+| **freerfac** | string/number | Free R-factor |
+| **pc** | string/number | Sequence identity cutoff % used for this subset |
+| **no_breaks** | string/number | Whether chain has no breaks (yes/no) |
+| **R** | string/number | R-factor cutoff used for this subset |
+| **source_list** | string/number | Subset list basename (identifies curation parameters) |
 
 Subset CSVs use the same schema; each file corresponds to one row in the index (one set of parameters: pc, resolution range, no_breaks, R, Nmethods).
 
@@ -652,12 +623,14 @@ Subset list basenames follow:
 
 ## Usage
 
+### Load in Python
+
 ```python
 from huggingface_hub import hf_hub_download
 import pandas as pd
 
 # Download main CSV (large)
-path = hf_hub_download(repo_id="PRMegathon26/PISCES-CulledPDB", filename="curated_csv/cullpdb_compiled.csv", repo_type="dataset")
+path = hf_hub_download(repo_id="PRMegathon26/PISCES-CulledPDB", filename="curated_csv/cullpdb_combined_chains.csv", repo_type="dataset")
 df = pd.read_csv(path)
 
 # Or load a single subset
@@ -665,9 +638,28 @@ path = hf_hub_download(repo_id="PRMegathon26/PISCES-CulledPDB", filename="curate
 df = pd.read_csv(path)
 ```
 
+### Filter chains (filter_chains_csv.py)
+
+After downloading the repo or the main/subset CSVs, run the filter script to build a custom CSV by pc, resolution, no_breaks, R, method, and length:
+
+```bash
+# From the repo root (e.g. after git clone or having curated_csv/ locally)
+python src/filter_chains_csv.py --input curated_csv/cullpdb_combined_chains.csv --output my_filtered.csv \
+  --pc 20 --resolution-max 2.0 --no-breaks yes --R 0.25
+
+# Or filter a subset directory (merges all subset CSVs then filters)
+python src/filter_chains_csv.py --input curated_csv/subsets --output high_res.csv \
+  --resolution-max 1.5 --len-min 100
+```
+
+Options: `--pc`, `--pc-min`, `--pc-max`, `--resolution-min`, `--resolution-max`, `--no-breaks` (yes/no), `--R`, `--method`, `--len-min`, `--len-max`. Omit a filter to leave that dimension unrestricted.
+
 ## Curation workflow
 
 1. **Index**: List/FASTA pairs scanned → `cullpdb_list_fasta_index.csv`
 2. **Build**: For each index row, list and FASTA aligned 1:1 → one chain CSV per subset + combined master CSV
 3. **Filter/visualize**: Optional filtering and summary figures from the master CSV
 
+## License
+
+CC BY 4.0 (or as specified in the repo).