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license: cc-by-3.0
pretty_name: ProteomeLM gene-essentiality data (OGEE v3)
tags:
- biology
- proteomics
- gene-essentiality
- proteomelm
configs:
- config_name: proteins
data_files: data/proteins.parquet
default: true
- config_name: minimal_cells
data_files: data/minimal_cells.parquet
---
# ProteomeLM gene-essentiality data
The data used to train and evaluate the gene-essentiality classifier of
[ProteomeLM](https://github.com/Bitbol-Lab/ProteomeLM)
([PNAS 2026](https://www.pnas.org/doi/10.1073/pnas.2524201123), Fig. 5; trained head:
[Bitbol-Lab/ProteomeLM-ess](https://huggingface.co/Bitbol-Lab/ProteomeLM-ess)).
The essentiality calls come from **OGEE v3**, the Online GEne Essentiality database
([Gurumayum et al., NAR 2021](https://doi.org/10.1093/nar/gkaa884)). The OGEE website
(v3.ogee.info) is currently unavailable, so its tables are mirrored here unmodified under
its CC BY 3.0 license. Please cite OGEE, and the primary screens it compiles, when you
use these labels.
## Files
| file | content |
|---|---|
| `raw/ogee_v3/gene_essentiality.txt` | OGEE essentiality calls (`dataset`, `taxaID`, `locus`, `gene`, `score`, `essentiality`, `pmid`, `Ref_db`) |
| `raw/ogee_v3/genes.txt` | OGEE gene annotations |
| `raw/ogee_v3/datasets.txt` | OGEE datasets (source, technique, condition, definition of essential) |
| `data/proteins.parquet` | 486,681 proteins of 89 genomes, with labels and folds (config `proteins`) |
| `data/genomes.tsv` | one row per genome: organism, protein and label counts, role |
| `data/minimal_cells.parquet` | JCVI-Syn1.0 and JCVI-Syn3A proteomes and labels (config `minimal_cells`) |
| `data/info.json` | split parameters |
## `proteins`
Each OGEE genome's proteome (UniProt reference proteome, else UniProtKB, else NCBI or the
source named in OGEE) with exact duplicates (on the first 4,096 residues) removed. OGEE
calls were matched to proteins by gene name, locus tag and synonyms.
| column | |
|---|---|
| `taxid` | NCBI taxonomy id of the genome |
| `protein_id`, `header` | FASTA id and full header line |
| `sequence` | amino-acid sequence |
| `gene`, `synonyms` | gene name and synonyms used for matching |
| `ogee_calls` | all OGEE calls for the gene (`E` essential, `NE` non-essential, `C` conditional, ...) |
| `label` | training label: the last `E`/`NE` call, null if none |
| `fold` | cross-validation fold: 0 = test, 1 = validation, 2-4 = train |
| `fasta_file` | proteome file name in the original pipeline |
153,918 proteins have a label (42,077 E, 111,841 NE). 82 genomes form the cross-validation set; the others were not used for training:
| taxid | organism | role | proteins | E | NE |
|---|---|---|---|---|---|
| 83333 | Escherichia coli K-12 | held out (Fig. 5B) | 4,361 | 290 | 3,969 |
| 199310 | Escherichia coli CFT073 | excluded | 5,336 | 232 | 0 |
| 580240 | Saccharomyces cerevisiae S288C | held out (Fig. 5B) | 5,975 | 939 | 4,353 |
| 679895 | Escherichia coli BW25113 | excluded | 4,192 | 313 | 0 |
| 941322 | Escherichia coli O25b:H4-ST131 | excluded | 4,821 | 294 | 4,023 |
| 1124478 | Vibrio cholerae O1 str. C6706 | excluded | 3,532 | 0 | 0 |
| 1380365 | Dyella japonica UNC79MFTsu3.2 | excluded | 4,374 | 0 | 0 |
"Excluded" genomes have fewer than 10 labelled proteins or problematic labels.
**Split.** All 486,681 sequences were clustered with MMseqs2 (`easy-cluster --min-seq-id 0.40`), and whole clusters were assigned at random to 5 folds (seed 0), so proteins sharing ≥40% identity are always in the same fold. Fold sizes: 0: 97,336, 1: 97,339, 2: 97,339, 3: 97,337, 4: 97,330. The released head
[ProteomeLM-ess](https://huggingface.co/Bitbol-Lab/ProteomeLM-ess) was trained with an earlier fold
assignment, so evaluate it on the held-out genomes and the minimal cells, not on fold 0.
## `minimal_cells`
The minimal cells of Fig. 5B (NCBI proteomes, locus tags as ids). Labels: `E`, `NE`, `QE`
(quasi-essential), uncertain calls (`E?`, `NE?`), `Not_a_Gene` and `No_label`.
| organism | taxid | proteins | E | NE |
|---|---|---|---|---|
| JCVI-Syn1.0 | 766747 | 820 | 202 | 386 |
| JCVI-Syn3A | 2144189 | 455 | 199 | 64 |
Sources: Hutchison et al. 2016, *Science* 351:aad6253, Database S1 (JCVI-Syn1.0) and
[SynWiki](https://synwiki.uni-goettingen.de) (JCVI-Syn3A).
## Usage
```python
import pandas as pd
proteins = pd.read_parquet("hf://datasets/Bitbol-Lab/ProteomeLM-ess-data/data/proteins.parquet")
```
or `datasets.load_dataset("Bitbol-Lab/ProteomeLM-ess-data", "proteins")`. To rebuild the input files of the
ProteomeLM pipeline (`experiments/essentiality` in the GitHub repository):
```bash
python -m experiments.essentiality.data fetch
```
## Citation
```bibtex
@article{malbranke2026proteomelm,
title={ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa},
author={Malbranke, Cyril and Zalaffi, Gionata Paolo and Bitbol, Anne-Florence},
journal={Proceedings of the National Academy of Sciences},
volume={123},
number={21},
pages={e2524201123},
year={2026},
doi={10.1073/pnas.2524201123}
}
@article{gurumayum2021ogee,
title={OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines},
author={Gurumayum, Sanathoi and Jiang, Puzi and Hao, Xiaowen and Campos, Tulio L and Young, Neil D and Korhonen, Pasi K and Gasser, Robin B and Bork, Peer and Zhao, Xing-Ming and He, Li-jie and Chen, Wei-Hua},
journal={Nucleic Acids Research},
volume={49},
number={D1},
pages={D998--D1003},
year={2021},
doi={10.1093/nar/gkaa884}
}
```
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