Code for "Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy"

Overview

aav_clean contains Python sourcecode for training the supervised machine learning (ML) models in our manuscript and for using these models to design AAV5 insertion libraries. We include a small demo file demonstrating the format of the raw experimental results for an insertion library, as well as the tools required for performing all pre-processing and filtering steps. We also include Python scripts for the evaluations and visualizations included in the paper.

System Requirements

aav_clean can be run on a standard computer, and supports both GPU and CPU-only model training. The code has been tested on a Linux (Ubuntu 18.04.6 LTS) system with Python 3.7.9 and 3.9.5. Relevant Python dependencies include:

• BioPython (1.76+)
• matplotlib (3.3.2+)
• numpy (1.19.4+)
• pandas (1.1.4+)
• scikit-learn (0.22.1+)
• scipy (1.7.0+)
• seaborn (0.11.0+)
• tensorflow (2.0.0+)

Installation

To install from GitHub:

git clone https://github.com/dhbrookes/aav_clean.git
cd aav_clean

Usage

Data Processing: Demo

To pre-precess the demo sequencing data, run:

cd data
python ../src/pre_process.py demo pre -r -c -p 2
python ../src/pre_process.py demo post -r -c -p 2

Pre-processing the 'pre-' and 'post-' selection demo data takes ~0.8 sec each on a standard computer. The expected outputs of these two steps the following four Pandas DataFrames in CSV file format:

• reads/demo_pre_reads.csv
• reads/demo_post_reads.csv
• counts/demo_pre_counts.csv
• counts/demo_post_counts.csv

Modeling

Given a pre-processed library, a supervised model can be trained using a command of the form:

python src/keras_models_run.py library_name model_type

where model_type specifies whether a linear ('linear') or neural network ('ann') model is trained. Information about additional arguments that may be specified (such as model hyperparameters) can be obtained by running

python src/keras_models_run.py --help

On a standard computer, it takes ~10 minute to train a standard linear model (CPU-only) on an AAV5 insertion library, and around ~30 minutes to train a reasonable-sized neural network model (CPU-only) on the same library, although train times for the latter are highly hyperparameter-dependent. The expected outputs of a training run are:

• a saved Keras model
• {modelname}_test_pred.npy containing a numpy array of model test predictions

Entropy Optimization

Given a trained Keras model, entropy optimization can be performed using a command of the form:

python src/entropy_opt.py model_path save_path --min_lambda 1 --max_lambda 2.5 --num_lambda 150 --num_iter 3000 --learning_rate 0.01 --num_samples 1000

Information about additional arguments can be obtained by running

python src/entropy_opt.py --help

On a standard computer, it takes ~30 minutes to perform the optimization (CPU-only) for a single lambda value. The expected output of an entropy_opt.py run is a {save_path}.npy file which contains a dictionary containing metadata about the optimization run and, for each lambda value, a tuple (entropy, mean_predict_log_enrichment, optimized_distribution).

Extracting designed library probabilities

The position-wise nucleotide probabilities required to synthesize any of the libraries designed in our manuscript are stored in results and can be easily extracted using the utility provided in opt_analysis. For example,

libraries, metadata = opt_analysis.load_designed_library_probabilities('../results/opt_results_nuc_1.npy')
libraries[0.25]

outputs the position-wise nucleotide probabilities for the library designed using our ML-guided approach with the diversity trade-off parameter lambda=0.25.