prokbert-mini

ProkBERT-mini Model

ProkBERT-mini (kmer=6, shift=1) is part of the ProkBERT family of genomic language models, specifically designed for microbiome applications. This model, optimized for DNA sequence analysis, can provide robust and high resolution solutions.

Simple Usage Example

The following example demonstrates how to use the ProkBERT-mini model for processing a DNA sequence:

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("neuralbioinfo/prokbert-mini", trust_remote_code=True)
model = AutoModel.from_pretrained("neuralbioinfo/prokbert-mini", trust_remote_code=True)

segment = "ATGTCCGCGGGACCT"

# Tokenize the input and return as PyTorch tensors
inputs = tokenizer(segment, return_tensors="pt")

# Pass the tokenized input to the model
outputs = model(**inputs)

Model Details

Developed by: Neural Bioinformatics Research Group

Architecture: ProkBERT-mini-k6s1 is based on the MegatronBert architecture, a variant of the BERT model optimized for large-scale training. The model employs a learnable relative key-value positional embedding, mapping input vectors into a 384-dimensional space.

Tokenizer: The model uses a 6-mer tokenizer with a shift of 1 (k6s1), specifically designed to handle DNA sequences efficiently.

Parameters:

Intended Use

Intended Use Cases: ProkBERT-mini-k6-s1 is intended for bioinformatics researchers and practitioners focusing on genomic sequence analysis, including:

• sequence classification tasks
• Exploration of genomic patterns and features

Segmentation and Tokenization in ProkBERT Models

Preprocessing Sequence Data

Transformer models, including ProkBERT, have a context size limitation. ProkBERT's design accommodates context sizes significantly larger than an average gene but smaller than the average bacterial genome. The initial stage of our pipeline involves two primary steps: segmentation and tokenization. For more details about tokenization, please see the following notebook: Tokenization Notebook in Google Colab.

For more details about segmentation, please see the following notebook: Segmentation Notebook in Google Colab.

Segmentation

Segmentation is crucial for Genomic Language Models (GLMs) as they process limited-size chunks of sequence data, typically ranging from 0 to 4kb. The sequence is divided into smaller parts through segmentation, which can be either contiguous, splitting the sequence into disjoint segments, or random, involving randomly sampling segments of length L.

The first practical step in segmentation involves loading the sequence from a FASTA file, often including the reverse complement of the sequence.

Segmentation process:

Tokenization Process

After segmentation, sequences are encoded into a vector format. The LCA method allows the model to use a broader context and reduce computational demands while maintaining the information-rich local context.

Basic Steps for Preprocessing:

1. Load Fasta Files: Begin by loading the raw sequence data from FASTA files.
2. Segment the Raw Sequences: Apply segmentation parameters to split the sequences into manageable segments.
3. Tokenize the Segmented Database: Use the defined tokenization parameters to convert the segments into tokenized forms.
4. Create a Padded/Truncated Array: Generate a uniform array structure, padding or truncating as necessary.
5. Save the Array to HDF: Store the processed data in an HDF (Hierarchical Data Format) file for efficient retrieval and use in training models.

Installation of ProkBERT (if needed)

For setting up ProkBERT in your environment, you can install it using the following command (if not already installed):

try:
    import prokbert
    print("ProkBERT is already installed.")
except ImportError:
    !pip install prokbert
    print("Installed ProkBERT.")

Training Data and Process

Overview: The model was pretrained on a comprehensive dataset of genomic sequences to ensure broad coverage and robust learning.

Training Process:

• Masked Language Modeling (MLM): The MLM objective was modified for genomic sequences for masking overlapping k-mers.
• Training Phases: The model underwent initial training with complete sequence restoration and selective masking, followed by a succeeding phase with variable-length datasets for increased complexity.

Evaluation Results for ProkBERT-mini

Masking performance of the ProkBERT family.

Evaluation of Promoter Prediction Tools on E-coli Sigma70 Dataset

The ProkBERT family models exhibit remarkably consistent performance across the metrics assessed. With respect to accuracy, all three tools achieve an impressive

Promoter prediction performance metrics on a diverse test set. A comparative analysis of various promoter prediction tools, showcasing their performance across key metrics including accuracy, F1 score, MCC, sensitivity, and specificity.

Evaluation on phage recognition benchmark

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

As with all models in the bioinformatics domain, ProkBERT-mini-k6-s1 should be used responsibly. 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
• Feedback and inquiries: obalasz@gmail.com

Reference

If you use ProkBERT-mini 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={...}
}