Sequence UNET 1.0.3

Sequence UNET is a fully convolutional neural network variant effect predictor, able to predict the pathogenicity of protein coding variants and the frequency they occur across large multiple sequence alignments. A description and discussion of the model is available on bioRxiv (Dunham et al. 2022). It uses a U-shaped architecture inspired by the U-NET medical image segmentation network (Ronneberger et al. 2015), with an optional Graph CNN section to incorporate information from protein structure:

This repo contains a python package for downloading and using the trained models as well as code used to train, explore and analyse the models. The package can download, load and predict with 8 variant trained models, which are also available for manual download on BioStudies. The python package is contained in the sequence_unet directory, with documentation in the docs directory. It is also possible to use the modules and scripts used for training and exploring different models, found in analysis, bin and source. The python scripts and modules require adding the src directory to your python path.

Installation

In most cases pip should be able to handle all the dependancies, meaning installation is simple:

pip install sequence_unet

Or the latest development version from GitHub:

pip install git+https://github.com/allydunham/sequence_unet

Manual installation

If pip can't resolve the correct dependancies the requirements might be able to be installed manually:

1. Install Tensorflow (or tensorflow-macos on M1 Macs): pip install tensorflow or pip install tensorflow-macos
2. Install other PyPi requirements: pip install numpy pandas biopython tqdm proteinnetpy
3. Install Sequence UNET: pip install --no-deps sequence_unet

This was needed on M1 Macs before I updated pyproject.toml to use tensorflow-macos for those systems since no versions of Tensorflow were available which matched the requirements. It might help similar compatibility issues too.

Requirements

The python package requires:

• Python 3+
• Tensorflow 2.6+
• Numpy
• Pandas
• Biopython
• TQDM
• ProteinNetPy

I have tried to maintain as broad requirements as possible but future changes, particularly in Tensorflow, may result in more restrictive version support.

Figure generation and performance analysis was performed in R 4.0, largely using Tidyverse packages. All R packages used are loaded in src/config.R or the top of the respective script.

Basic Usage

Detailed documentation of the modules functions and scripts are available on ReadTheDocs.

Loading Models

Trained models are downloaded and loaded using the sequence_unet.models module, which also provides functions to initialise untrained models. The loaded model is a TensorFlow Keras model, so easily interfaces with other code.

from sequence_unet import models

# Download a trained model
model_path = models.download_trained_model("freq_classifier", root="models", model_format="tf")

# Load the downloaded model via its path. This function can also download the model if not found
model = models.load_trained_model(model=model_path)

# or via name and root
model = models.load_trained_model(model="freq_classifier", root="models")
model.summary()

Prediction

The sequence_unet.predict module provides functions to predict from sequences or ProteinNet files. Other input data can easily be parsed into one of these formats or transformed in Python to fit the models input shapes (described in the models.sequence_unet function).

from pandas import concat
from Bio import SeqIO
from proteinnetpy.data import ProteinNetDataset

from sequence_unet.models import load_trained_model
from sequence_unet.predict import predict_sequence, predict_proteinnet

# load a model
model = load_trained_model(model="freq_classifier", download=True)

# Predict from a fasta file
fasta = SeqIO.parse("path/to/fasta.fa", format="fasta")
preds = concat([p for p in predict_sequence(model, sequences=fasta, wide=True)])

# Predict from a ProteinNet file
data = ProteinNetDataset(path="path/to/proteinnet", preload=False)
preds = concat([p for p in predict_proteinnet(model, data, wide=True)])

The package also binds the sequence_unet command, which allows users to make predictions from the command line. It supports predicting scores for proteins in a fasta or ProteinNet file. A full description is available in the documentation and can be accessed using -h.

Training

The sequence_unet package does not include specific code for training new models, as the best way to do this will be specific to the users needs. However, the loaded models are TensorFlow Keras models and inherently support straightforward and extensible training. I used the training.py script to train the models, based on a saved model and data loading function saved by the make_experiment_dir function in src/utils.py. Usage of the script is documented in it's docstring and can be accessed using -h. The training scripts in subdirectories of models give examples of the model training procedures I used to train the various forms of the model. This setup can be adapted for other users needs reasonably easily.