File size: 10,022 Bytes
a25312e b045f46 a25312e b045f46 a25312e e9b646e a25312e b045f46 a25312e b045f46 a25312e e9b646e a25312e b045f46 a25312e b045f46 a25312e b045f46 a25312e |
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 |
---
license: cc-by-nc-4.0
tags:
- prokbert
- bioinformatics
- genomics
- sequence embedding
- genomic language models
- nucleotide
- dna-sequence
- promoter-prediction
- phage
---
## ProkBERT-mini-long-phage Model
This finetuned model is specifically designed for promoter identification and is based on the [ProkBERT-mini-long model](https://huggingface.co/neuralbioinfo/prokbert-mini-long).
For more details, refer to the [phage dataset description](https://huggingface.co/datasets/neuralbioinfo/phage-test-10k) used for training and evaluating this model.
### Example Usage
For practical examples on how to use this model, see the following Jupyter notebooks:
- [Training Notebook](https://colab.research.google.com/github/nbrg-ppcu/prokbert/blob/main/examples/Finetuning.ipynb): A guide to fine-tuning the ProkBERT-mini model for promoter identification tasks.
- [Evaluation Notebook](https://colab.research.google.com/github/nbrg-ppcu/prokbert/blob/main/examples/Inference.ipynb): Demonstrates how to evaluate the finetuned ProkBERT-mini-promoter model on test datasets.
### Model Application
The model was trained for binary classification to distinguish between pahge and non-phage (bacteria) sequences. The non-phage sequences were sampled form the phage's host genome randomly.
## Simple Usage Example
The following example demonstrates how to use the ProkBERT-mini-promoter model for processing a DNA sequence:
```python
from prokbert.prokbert_tokenizer import ProkBERTTokenizer
from transformers import MegatronBertForSequenceClassification
finetuned_model = "neuralbioinfo/prokbert-mini-long-phage"
kmer = 6
shift= 2
tok_params = {'kmer' : kmer,
'shift' : shift}
tokenizer = ProkBERTTokenizer(tokenization_params=tok_params)
model = MegatronBertForSequenceClassification.from_pretrained(finetuned_model)
sequence = 'CACCGCATGGAGATCGGCACCTACTTCGACAAGCTGGAGGCGCTGCTGAAGGAGTGGTACGAGGCGCGCGGGGGTGAGGCATGACGGACTGGCAAGAGGAGCAGCGTCAGCGC'
inputs = tokenizer(sequence, return_tensors="pt")
# Ensure that inputs have a batch dimension
inputs = {key: value.unsqueeze(0) for key, value in inputs.items()}
# Generate outputs from the model
outputs = model(**inputs)
print(outputs)
```
### Model Details
**Developed by:** Neural Bioinformatics Research Group
**Architecture:**
...
**Tokenizer:** The model uses a 6-mer tokenizer with a shift of 2 (k6s2), specifically designed to handle DNA sequences efficiently.
**Parameters:**
| Parameter | Description |
|----------------------|--------------------------------------|
| Model Size | 26.6 million parameters |
| Max. Context Size | 4096 bp |
| Training Data | 206.65 billion nucleotides |
| Layers | 6 |
| Attention Heads | 6 |
### Intended Use
**Intended Use Cases:** ProkBERT-mini-phage is intended for bioinformatics researchers and practitioners focusing on genomic sequence analysis, including:
- sequence classification tasks
- Exploration of genomic patterns and features
### Installation of ProkBERT (if needed)
For setting up ProkBERT in your environment, you can install it using the following command (if not already installed):
```python
try:
import prokbert
print("ProkBERT is already installed.")
except ImportError:
!pip install prokbert
print("Installed ProkBERT.")
```
### Evaluation on phage recognition benchmark dataset
| method | L | auc_class1 | acc | f1 | mcc | recall | sensitivity | specificity | tn | fp | fn | tp | Np | Nn | eval_time |
|:--------------|-----:|-------------:|---------:|---------:|---------:|---------:|--------------:|--------------:|-----:|-----:|-----:|-----:|------:|------:|------------:|
| DeepVirFinder | 256 | 0.734914 | 0.627163 | 0.481213 | 0.309049 | 0.345317 | 0.345317 | 0.909856 | 4542 | 450 | 3278 | 1729 | 5007 | 4992 | 7580 |
| DeepVirFinder | 512 | 0.791423 | 0.708 | 0.637717 | 0.443065 | 0.521192 | 0.521192 | 0.889722 | 4510 | 559 | 2361 | 2570 | 4931 | 5069 | 2637 |
| DeepVirFinder | 1024 | 0.826255 | 0.7424 | 0.702678 | 0.505333 | 0.605651 | 0.605651 | 0.880579 | 4380 | 594 | 1982 | 3044 | 5026 | 4974 | 1294 |
| DeepVirFinder | 2048 | 0.853098 | 0.7717 | 0.743339 | 0.557177 | 0.6612 | 0.6612 | 0.8822 | 4411 | 589 | 1694 | 3306 | 5000 | 5000 | 1351 |
| INHERIT | 256 | 0.75982 | 0.6943 | 0.67012 | 0.393179 | 0.620008 | 0.620008 | 0.76883 | 3838 | 1154 | 1903 | 3105 | 5008 | 4992 | 2131 |
| INHERIT | 512 | 0.816326 | 0.7228 | 0.651408 | 0.479323 | 0.525248 | 0.525248 | 0.914973 | 4638 | 431 | 2341 | 2590 | 4931 | 5069 | 2920 |
| INHERIT | 1024 | 0.846547 | 0.7264 | 0.659447 | 0.495935 | 0.527059 | 0.527059 | 0.927825 | 4615 | 359 | 2377 | 2649 | 5026 | 4974 | 3055 |
| INHERIT | 2048 | 0.864122 | 0.7365 | 0.668595 | 0.518541 | 0.5316 | 0.5316 | 0.9414 | 4707 | 293 | 2342 | 2658 | 5000 | 5000 | 3225 |
| MINI | 256 | 0.846745 | 0.7755 | 0.766462 | 0.552855 | 0.735623 | 0.735623 | 0.815505 | 4071 | 921 | 1324 | 3684 | 5008 | 4992 | 6.68888 |
| MINI | 512 | 0.924973 | 0.8657 | 0.859121 | 0.732696 | 0.83046 | 0.83046 | 0.89998 | 4562 | 507 | 836 | 4095 | 4931 | 5069 | 16.3681 |
| MINI | 1024 | 0.956432 | 0.9138 | 0.911189 | 0.829645 | 0.879825 | 0.879825 | 0.94813 | 4716 | 258 | 604 | 4422 | 5026 | 4974 | 51.3319 |
| MINI-C | 256 | 0.827635 | 0.7512 | 0.7207 | 0.51538 | 0.640974 | 0.640974 | 0.861779 | 4302 | 690 | 1798 | 3210 | 5008 | 4992 | 7.33697 |
| MINI-C | 512 | 0.913378 | 0.8466 | 0.834876 | 0.69725 | 0.786453 | 0.786453 | 0.905109 | 4588 | 481 | 1053 | 3878 | 4931 | 5069 | 17.6749 |
| MINI-C | 1024 | 0.94644 | 0.8937 | 0.891564 | 0.788427 | 0.869479 | 0.869479 | 0.918175 | 4567 | 407 | 656 | 4370 | 5026 | 4974 | 54.204 |
| MINI-LONG | 256 | 0.777697 | 0.71495 | 0.686224 | 0.437727 | 0.622404 | 0.622404 | 0.807792 | 8065 | 1919 | 3782 | 6234 | 10016 | 9984 | 6.10304 |
| MINI-LONG | 512 | 0.880831 | 0.81405 | 0.798001 | 0.632855 | 0.744879 | 0.744879 | 0.881338 | 8935 | 1203 | 2516 | 7346 | 9862 | 10138 | 12.1307 |
| MINI-LONG | 1024 | 0.9413 | 0.88925 | 0.884917 | 0.781465 | 0.847195 | 0.847195 | 0.931745 | 9269 | 679 | 1536 | 8516 | 10052 | 9948 | 30.5088 |
| MINI-LONG | 2048 | 0.964551 | 0.929 | 0.927455 | 0.85878 | 0.9077 | 0.9077 | 0.9503 | 9503 | 497 | 923 | 9077 | 10000 | 10000 | 94.404 |
| Virsorter2 | 512 | 0.620782 | 0.6259 | 0.394954 | 0.364831 | 0.247617 | 0.247617 | 0.993884 | 5038 | 31 | 3710 | 1221 | 4931 | 5069 | 2057 |
| Virsorter2 | 1024 | 0.719898 | 0.7178 | 0.621919 | 0.51036 | 0.461799 | 0.461799 | 0.976478 | 4857 | 117 | 2705 | 2321 | 5026 | 4974 | 3258 |
| Virsorter2 | 2048 | 0.816142 | 0.8103 | 0.778724 | 0.647532 | 0.6676 | 0.6676 | 0.953 | 4765 | 235 | 1662 | 3338 | 5000 | 5000 | 5737 |
### Column Descriptions
- **method**: The algorithm or method used for prediction (e.g., DeepVirFinder, INHERIT).
- **L**: Length of the genomic segment.
- **auc_class1**: Area under the ROC curve for class 1, indicating the model's ability to distinguish between classes.
- **acc**: Accuracy of the prediction, representing the proportion of true results (both true positives and true negatives) among the total number of cases examined.
- **f1**: The F1 score, a measure of a test's accuracy that considers both the precision and the recall.
- **mcc**: Matthews correlation coefficient, a quality measure for binary (two-class) classifications.
- **recall**: The recall, or true positive rate, measures the proportion of actual positives that are correctly identified.
- **sensitivity**: Sensitivity or true positive rate; identical to recall.
- **specificity**: The specificity, or true negative rate, measures the proportion of actual negatives that are correctly identified.
- **fp**: The number of false positives, indicating how many negative class samples were incorrectly identified as positive.
- **tp**: The number of true positives, indicating how many positive class samples were correctly identified.
- **eval_time**: The time taken to evaluate the model or method, usually in seconds.
### Ethical Considerations and Limitations
Testing and evaluation have been conducted within specific genomic contexts, and the model's outputs in other scenarios are not guaranteed. Users should exercise caution and perform additional testing as necessary for their specific use cases.
### Reporting Issues
Please report any issues with the model or its outputs to the Neural Bioinformatics Research Group through the following means:
- **Model issues:** [GitHub repository link](https://github.com/nbrg-ppcu/prokbert)
- **Feedback and inquiries:** [obalasz@gmail.com](mailto:obalasz@gmail.com)
## Reference
If you use ProkBERT in your research, please cite the following paper:
```
@ARTICLE{10.3389/fmicb.2023.1331233,
AUTHOR={Ligeti, Balázs and Szepesi-Nagy, István and Bodnár, Babett and Ligeti-Nagy, Noémi and Juhász, János},
TITLE={ProkBERT family: genomic language models for microbiome applications},
JOURNAL={Frontiers in Microbiology},
VOLUME={14},
YEAR={2024},
URL={https://www.frontiersin.org/articles/10.3389/fmicb.2023.1331233},
DOI={10.3389/fmicb.2023.1331233},
ISSN={1664-302X},
ABSTRACT={...}
}
``` |