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98bd880 f6063ec 98bd880 f6063ec 98bd880 f6063ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | # 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
Citation information will be added upon publication of the accompanying manuscript.
|