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---
license: cc-by-nc-sa-4.0
---
# ProkBERT PhaStyle
**Model Name**: neuralbioinfo/PhaStyle-mini
**Model Type**: Genomic Language Model (ProkBERT-based)
**Model Description**:
ProkBERT PhaStyle is a fine-tuned genomic language model designed for phage lifestyle prediction. It classifies phages as either **virulent** or **temperate** directly from nucleotide sequences. The model is based on ProkBERT architecture and was trained on the **BACPHLIP dataset**, excluding *E. coli* sequences
By leveraging transfer learning, ProkBERT PhaStyle is optimized for handling **fragmented sequences**, commonly encountered in metagenomic and metavirome datasets. The model provides a fast, efficient alternative to traditional methods without requiring complex preprocessing pipelines or curated databases.
### Key Points:
- **Trained on BACPHLIP** dataset excluding *E. coli* sequences.
- **Segment Length** for training: 512 base pairs.
- **Output**: Binary classification (virulent or temperate).
- **Model Parameters**: ~25 million parameters.
---
## Intended Use
ProkBERT PhaStyle is designed for phage lifestyle prediction tasks, suitable for:
- **Phage Therapy**: Identifying virulent phages for bacterial infection treatment.
- **Microbiome Engineering**: Understanding the interaction between temperate and virulent phages in various microbiomes.
- **Metagenomic Studies**: Classifying fragmented phage sequences from environmental or clinical samples.
### Inference Code
ProkBERT PhaStyle requires the **ProkBERT tokenizer** and a **custom classification model** (`BertForBinaryClassificationWithPooling`). Below is a high-level overview of how to use the model in inference mode:
```python
aaa
```
```bash
python bin/PhaStyle.py \
--fastain data/EXTREMOPHILE/extremophiles.fasta \
--out output_predictions.tsv \
--ftmodel neuralbioinfo/PhaStyle-mini \
--modelclass BertForBinaryClassificationWithPooling \
--per_device_eval_batch_size 196
```
### Datasets Used:
- **BACPHLIP (without E. coli)**: 1,868 training sequences and 246 validation sequences.
- **Guelin Collection**: 394 *Escherichia* phages (temperate and virulent types).
- **EXTREMOPHILE Phages**: 16 phages isolated from extreme environments, including deep-sea, acidic, and arsenic-rich habitats.
Each dataset was processed using **512bp segment lengths** to simulate fragmented metagenomic assemblies.
---
## Performance Results
The performance of ProkBERT PhaStyle was evaluated on various datasets, including *Escherichia* and EXTREMOPHILE phages, using segment lengths of 512bp and 1022bp. The results are summarized below:
### Performance on *Escherichia* Dataset (512bp and 1022bp segments)
| Method | Balanced Accuracy | MCC | Sensitivity | Specificity |
|--------------------------|-------------------|-------|-------------|-------------|
| **ProkBERT-mini (512bp)** | 0.91 | 0.83 | 0.94 | 0.89 |
| ProkBERT-mini-long (512bp)| 0.90 | 0.82 | 0.96 | 0.85 |
| ProkBERT-mini-c (512bp) | 0.89 | 0.80 | 0.95 | 0.84 |
| DNABERT-2-117M (512bp) | 0.84 | 0.72 | 0.95 | 0.74 |
| Nuc. Trans.-50m (512bp) | 0.85 | 0.72 | 0.92 | 0.78 |
| **ProkBERT-mini (1022bp)**| **0.94** | **0.88** | **0.97** | **0.91** |
| ProkBERT-mini-long (1022bp)| 0.94 | 0.89 | 0.97 | 0.91 |
### Performance on EXTREMOPHILE Dataset (512bp and 1022bp segments)
| Method | Balanced Accuracy | MCC | Sensitivity | Specificity |
|--------------------------|-------------------|-------|-------------|-------------|
| **ProkBERT-mini (512bp)** | 0.93 | 0.83 | 0.99 | 0.87 |
| ProkBERT-mini-long (512bp)| 0.93 | 0.82 | **1.00** | 0.86 |
| ProkBERT-mini-c (512bp) | 0.92 | 0.80 | 0.99 | 0.84 |
| DNABERT-2-117M (512bp) | 0.89 | 0.74 | 0.99 | 0.79 |
| **ProkBERT-mini (1022bp)**| **0.96** | **0.91** | **1.00** | **0.93** |
| ProkBERT-mini-long (1022bp)| 0.96 | 0.90 | 1.00 | 0.92 |
These tables highlight the high accuracy, MCC, and generalization capability of ProkBERT models, particularly on challenging datasets like *Escherichia* and extremophile phages. The ProkBERT-mini and ProkBERT-mini-long models consistently performed well on both datasets.
For more detailed results, including additional metrics, please refer to the original research paper.
---
## Inference Speed and Running Times
The computational performance of ProkBERT PhaStyle was evaluated using 1,000 randomly selected sequences from the BACPHLIP dataset. The evaluation was performed on a consistent hardware setup with NVIDIA Tesla A100 GPUs. The execution times and inference speeds of various models are summarized below:
### Execution Times (in seconds)
| Model | Execution Time (seconds) | Inference Speed (MB/sec) |
|--------------------------|--------------------------|--------------------------|
| **ProkBERT-mini-long** | **132** | **0.52** |
| ProkBERT-mini | 141 | 0.49 |
| ProkBERT-mini-c | 146 | 0.47 |
| DNABERT-2-117M | 248 | 0.25 |
| Nucleotide Transformer-50m| 342 | 0.18 |
| Nucleotide Transformer-500m| 502 | 0.12 |
| DeePhage | 159 | 0.43 |
| PhaTYP | 2,718 | 0.03 |
| BACPHLIP | 7,125 | 0.01 |
## Limitations
ProkBERT PhaStyle is specifically designed for **binary classification** of phage lifestyles (virulent vs. temperate) and does not handle non-phage sequences. It is recommended to use this model in conjunction with upstream pipelines that identify phage sequences. For large-scale inference, **GPU support** is strongly advised.
# Citing this work
If you use the data in this package, please cite:
```bibtex
@Article{ProkBERT2024,
author = {Ligeti, Balázs and Szepesi-Nagy, István and Bodnár, Babett and Ligeti-Nagy, Noémi and Juhász, János},
journal = {Frontiers in Microbiology},
title = {{ProkBERT} family: genomic language models for microbiome applications},
year = {2024},
volume = {14},
URL={https://www.frontiersin.org/articles/10.3389/fmicb.2023.1331233},
DOI={10.3389/fmicb.2023.1331233},
ISSN={1664-302X}
}
```
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