Update README.md

README.md

@@ -1,187 +1,184 @@
----
-dataset_info:
-  features:
-  - name: SNOOPPI_final_label
-    dtype: string
-  - name: MI0915_final_label
-    dtype: string
-  - name: SNOOPPI_Partner_A_ID
-    dtype: string
-  - name: SNOOPPI_Partner_B_ID
-    dtype: string
-  - name: SNOOPPI_Pair_ID
-    dtype: string
-  - name: SNOOPPI_Labeled_Pair_ID
-    dtype: string
-  - name: seq_sort
-    dtype: string
-  - name: unique_interaction_mi_terms
-    dtype: string
-  - name: partner_A_sequence
-    dtype: string
-  - name: partner_B_sequence
-    dtype: string
-  - name: partner_A_length
-    dtype: string
-  - name: partner_B_length
-    dtype: string
-  - name: partner_A_mol_type
-    dtype: string
-  - name: partner_B_mol_type
-    dtype: string
-  - name: intact_interaction_ids
-    dtype: string
-  - name: n_intact_interaction_ids
-    dtype: string
-  - name: partner_A_intact_interactor_ids
-    dtype: string
-  - name: partner_B_intact_interactor_ids
-    dtype: string
-  - name: partner_A_n_intact_interactor_ids
-    dtype: string
-  - name: partner_B_n_intact_interactor_ids
-    dtype: string
-  - name: partner_A_uniprot_ids
-    dtype: string
-  - name: partner_B_uniprot_ids
-    dtype: string
-  - name: partner_A_gene_names
-    dtype: string
-  - name: partner_B_gene_names
-    dtype: string
-  - name: partner_A_species
-    dtype: string
-  - name: partner_B_species
-    dtype: string
-  - name: mi_scores_all
-    dtype: string
-  - name: mi_score_mean
-    dtype: string
-  - name: mi_score_min
-    dtype: string
-  - name: mi_score_max
-    dtype: string
-  - name: assays_unique
-    dtype: string
-  - name: n_assays_unique
-    dtype: string
-  - name: n_papers
-    dtype: string
-  - name: pubmed_ids
-    dtype: string
-  - name: publication_years_all
-    dtype: string
-  - name: publication_year_min
-    dtype: string
-  - name: publication_year_max
-    dtype: string
-  - name: feature_relationship_types
-    dtype: string
-  - name: feature_relationship_summary
-    dtype: string
-  - name: has_mutation_relationship
-    dtype: string
-  - name: has_ptm_relationship
-    dtype: string
-  - name: has_binding_site_relationship
-    dtype: string
-  - name: partner_A_wildtype_sequence_if_feature_modified
-    dtype: string
-  - name: partner_B_wildtype_sequence_if_feature_modified
-    dtype: string
-  - name: wildtype_seq_sort_if_feature_modified
-    dtype: string
-  - name: wildtype_SNOOPPI_final_label
-    dtype: string
-  - name: wildtype_MI0915_final_label
-    dtype: string
-  - name: exact_sequence_pair_intact_label_summary
-    dtype: string
-  - name: snooppi_label_transition_from_intact
-    dtype: string
-  - name: seq_sort_origin_categories
-    dtype: string
-  - name: unique_expansions
-    dtype: string
-  - name: partner_A_go_terms
-    dtype: string
-  - name: partner_B_go_terms
-    dtype: string
-  - name: partner_A_dip
-    dtype: string
-  - name: partner_B_dip
-    dtype: string
-  - name: partner_A_ensg
-    dtype: string
-  - name: partner_B_ensg
-    dtype: string
-  - name: partner_A_ense
-    dtype: string
-  - name: partner_B_ense
-    dtype: string
-  - name: partner_A_enst
-    dtype: string
-  - name: partner_B_enst
-    dtype: string
-  - name: partner_A_interpro
-    dtype: string
-  - name: partner_B_interpro
-    dtype: string
-  - name: partner_A_rcsb_pdb
-    dtype: string
-  - name: partner_B_rcsb_pdb
-    dtype: string
-  - name: partner_A_reactome
-    dtype: string
-  - name: partner_B_reactome
-    dtype: string
-  - name: partner_A_host_context
-    dtype: string
-  - name: partner_B_host_context
-    dtype: string
-  - name: partner_A_sequence_snooppi_degree_by_label
-    dtype: string
-  - name: partner_B_sequence_snooppi_degree_by_label
-    dtype: string
-  - name: partner_A_sequence_modified_role_counts
-    dtype: string
-  - name: partner_B_sequence_modified_role_counts
-    dtype: string
-  - name: partner_A_sequence_partner_modified_role_counts
-    dtype: string
-  - name: partner_B_sequence_partner_modified_role_counts
-    dtype: string
-  - name: partner_A_gene_snooppi_degree_by_label
-    dtype: string
-  - name: partner_B_gene_snooppi_degree_by_label
-    dtype: string
-  - name: partner_A_gene_modified_role_counts
-    dtype: string
-  - name: partner_B_gene_modified_role_counts
-    dtype: string
-  - name: partner_A_gene_partner_modified_role_counts
-    dtype: string
-  - name: partner_B_gene_partner_modified_role_counts
-    dtype: string
-  splits:
-  - name: positive
-    num_bytes: 153392772
-    num_examples: 33625
-  - name: negative
-    num_bytes: 34725795
-    num_examples: 5378
-  - name: unknown
-    num_bytes: 4648675404
-    num_examples: 835084
-  download_size: 1460575565
-  dataset_size: 4836793971
-configs:
-- config_name: default
-  data_files:
-  - split: positive
-    path: data/positive-*
-  - split: negative
-    path: data/negative-*
-  - split: unknown
-    path: data/unknown-*
----
+# SNOOPPI
+
+SNOOPPI is a curated protein–protein interaction dataset containing positive, negative, and unknown interaction annotations.
+
+The dataset preserves protein sequences, identifiers, species information, PSI-MI interaction terms, experimental evidence, publications, feature relationships, and annotation provenance where available.
+
+## Dataset splits
+
+SNOOPPI is distributed in three splits:
+
+| Split      | Description                                                                                                                                 |
+| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
+| `positive` | Protein pairs retained as positive interactions under the SNOOPPI annotation criteria                                                       |
+| `negative` | Protein pairs retained as negative interactions under the SNOOPPI annotation criteria                                                       |
+| `unknown`  | Protein pairs present in the source interaction records but not assigned a definitive positive or negative label under the SNOOPPI criteria |
+
+The `unknown` split should not automatically be interpreted as a negative or as a set of experimentally tested non-interactions. These entries represent interactions for which the available evidence was insufficient, conflicting, or otherwise unresolved under the annotation framework.
+
+## Installation
+
+Install the Hugging Face `datasets` package:
+
+```bash
+pip install -U datasets
+```
+
+## Load the complete dataset
+
+```python
+from datasets import load_dataset
+
+snooppi = load_dataset("ChatterjeeLab/SNOOPPI")
+
+print(snooppi)
+```
+
+This returns a `DatasetDict` containing the `positive`, `negative`, and `unknown` splits.
+
+Access an individual split from the resulting object:
+
+```python
+snooppi_positive = snooppi["positive"]
+snooppi_negative = snooppi["negative"]
+snooppi_unknown = snooppi["unknown"]
+```
+
+## Load one split directly
+
+Load only the positive interactions:
+
+```python
+from datasets import load_dataset
+
+snooppi_positive = load_dataset(
+    "ChatterjeeLab/SNOOPPI",
+    split="positive",
+)
+```
+
+Load only the negative interactions:
+
+```python
+snooppi_negative = load_dataset(
+    "ChatterjeeLab/SNOOPPI",
+    split="negative",
+)
+```
+
+Load only the unknown interactions:
+
+```python
+snooppi_unknown = load_dataset(
+    "ChatterjeeLab/SNOOPPI",
+    split="unknown",
+)
+```
+
+## Convert a split to pandas
+
+```python
+from datasets import load_dataset
+
+snooppi_pos = load_dataset(
+    "ChatterjeeLab/SNOOPPI",
+    split="positive",
+).to_pandas()
+```
+
+Converting a complete split to pandas loads that split into memory. Users working in memory-constrained environments may prefer to filter the Hugging Face `Dataset` before calling `.to_pandas()`.
+
+## Example queries
+
+### Retrieve direct human positive interactions
+
+The PSI-MI term `MI:0407` denotes a direct interaction. The NCBI taxonomy identifier for humans is `9606`.
+
+```python
+from datasets import load_dataset
+
+snooppi_pos = load_dataset(
+    "ChatterjeeLab/SNOOPPI",
+    split="positive",
+).to_pandas()
+
+direct_human_positive = snooppi_pos.loc[
+    snooppi_pos[
+        "unique_interaction_mi_terms"
+    ].fillna("").str.contains(
+        "MI:0407",
+        regex=False,
+    )
+    & snooppi_pos[
+        "partner_A_species"
+    ].fillna("").str.contains(
+        "9606",
+        regex=False,
+    )
+    & snooppi_pos[
+        "partner_B_species"
+    ].fillna("").str.contains(
+        "9606",
+        regex=False,
+    )
+].copy()
+
+print(
+    f"Direct human positive interactions: "
+    f"{len(direct_human_positive):,}"
+)
+```
+
+### Find all positive interactions involving a UniProt accession
+
+```python
+from datasets import load_dataset
+
+snooppi_pos = load_dataset(
+    "ChatterjeeLab/SNOOPPI",
+    split="positive",
+).to_pandas()
+
+uniprot_accession = "P00734"
+
+uniprot_ppis = snooppi_pos.loc[
+    snooppi_pos[
+        "partner_A_uniprot_ids"
+    ].fillna("").str.contains(
+        uniprot_accession,
+        regex=False,
+    )
+    | snooppi_pos[
+        "partner_B_uniprot_ids"
+    ].fillna("").str.contains(
+        uniprot_accession,
+        regex=False,
+    )
+].copy()
+
+print(
+    f"Positive interactions involving {uniprot_accession}: "
+    f"{len(uniprot_ppis):,}"
+)
+```
+
+## Notes on identifier searches
+
+Identifier fields may contain multiple source identifiers or annotations within one string. The examples therefore use literal substring matching with `regex=False`.
+
+For analyses requiring exact identifier membership, users should parse the relevant identifier field according to its delimiter structure before testing membership.
+
+## Intended use
+
+SNOOPPI may support:
+
+* Evaluation of protein–protein interaction prediction methods
+* Analysis of positive and negative interaction evidence
+* Study of sequence and feature relationships between interacting proteins
+* Retrieval of interactions by UniProt accession, species, or PSI-MI term
+
+Users should preserve the distinction among positive, negative, and unknown annotations when constructing evaluation or training datasets.
+
+## Citation
+
+Citation information will be added upon publication of the accompanying manuscript.