Promoters

Implementation of PromoterLCNN: A Light CNN-based Promoter Prediction and Classification model.

For convenience, this repository also contains the wrapper classes developed in order to compare with other models: IPromoter-BnCNN and pcPromoter-CNN in particular.

Requirements

Just run (ideally inside a virtual environment):

# Necessary ones
$ pip install tensorflow==2.6.0 tensorflow-addons==0.14.0 biopython==1.79 scikit-learn==1.0 numpy==1.19.5 pandas==1.3.4
# For Jupyter and table making
$ pip install jupyterlab tabulate

Weights

Get them from either

• Lightweight: PromoterLCNN only.

• Full: PromoterLCNN plus mirrors of both IPromoter-BnCNN and pcPromoter-CNN.

  Then unzip inside weights folder.

How to use

Running inference on DNA strings

Each class is defined inside the module custom_models, in order to use them, all you have to do is something like this:

from custom_models import PromoterLCNN, PromoterType
from pathlib import Path

# First load the model
model = PromoterLCNN("PromoterLCNN", # Name for the instance
                [
                    Path("/path/to/first/classifier"), # Promoter/Non-promoter
                    Path("/path/to/second/classifier") # Promoter class
                ]
)
# Sample data
data = [
    "TTACTCATGGTTTTCTCCTGTCAGGAACGTTCGGATGAAAATTGATCCTTTCCAAGCTTAGACCAGGATGGCGGGATGGGC",
    "ATGCCTGATAATGAGAACTGCTTTAGTAAACTACTTTGTATGCTGTCTGTCTTTCAAACCGACGCAGCTATTAACGCATGA"
]
# Then call the predict method
results = model.predict(data)
# Results is a numpy array of PromoterType

Running tests on DNA strings

See the examples inside Overview

Training

See the examples inside Train