|
--- |
|
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} |
|
} |
|
``` |
|
|
|
|
