Datasets:

ChatterjeeLab
/

SNOOPPI

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:

pip install -U datasets

Load the complete dataset

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:

snooppi_positive = snooppi["positive"]
snooppi_negative = snooppi["negative"]
snooppi_unknown = snooppi["unknown"]

Load one split directly

Load only the positive interactions:

from datasets import load_dataset

snooppi_positive = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="positive",
)

Load only the negative interactions:

snooppi_negative = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="negative",
)

Load only the unknown interactions:

snooppi_unknown = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="unknown",
)

Convert a split to pandas

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.

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

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.