--- configs: - config_name: default data_files: - split: positive path: data/positive-* - split: negative path: data/negative-* - split: unknown path: data/unknown-* 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: 4633269465 num_examples: 835083 download_size: 1457344515 dataset_size: 4821388032 --- # SNOOPPI: Sequence-Normalized Database of On- and Off-Target Protein-Protein Interactions SNOOPPI is a curated protein–protein interaction (PPI) dataset containing positive, negative, and unknown interaction annotations. * SNOOPPI is *sequence-indexed*, meaning one unique SNOOPPI PPI or non-PPI indicates a unique combination of protein sequences. * The dataset preserves protein sequences, identifiers, species information, PSI-MI interaction terms, experimental evidence, publications, and feature relationships 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 IntAct's 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 If you find SNOOPPI useful in your work, please consider citing our paper. ``` @article{10.1093/bioinformatics/btag736, author = {Vincoff, Sophia and Chatterjee, Pranam}, title = {SNOOPPI: a sequence-normalized database of on- and off-target protein–protein interactions}, journal = {Bioinformatics}, volume = {42}, number = {10}, pages = {btag736}, year = {2026}, month = {10}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btag736}, url = {https://doi.org/10.1093/bioinformatics/btag736}, eprint = {https://academic.oup.com/bioinformatics/article-pdf/42/10/btag736/71611875/btag736.pdf}, } ```