Hugging Face's logo

Datasets:
ChatterjeeLab
/
SNOOPPI

Modalities:
Text
Formats:
parquet
Size:
100K - 1M
DOI:
Libraries:
Datasets
Dask
Polars
Dataset card Data Studio Files
xet
 Community
1
SNOOPPI / README.md
svincoff's picture
svincoff
Update README.md with citation
45e4f02 verified
preview code
|
Raw History Blame Contribute Delete
10.8 kB
---
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},
}
```