SiftSeq

A state-of-the-art CNN+LSTM neural network model that predicts whether short sequences of DNA are viral, human, or bacterial in origin

Performance

SiftSeq significantly outperforms benchmarks set by the recent classifiers ViraMiner and VirNet, while being able to handle more classes and shorter sequences.

Contents

• data: contains small fasta files used for checking that things run properly

• saved_models: saved models produced during training

• sift_seq: scripts used in training and prediction

• Dockerfile: the file from which the docker image for this project is built

Installation

1. If you do not yet have it, download Docker.

2. Pull the docker image for SiftSeq from Docker Hub. On the command line, type

docker pull elanstop/sift-seq:latest

3. Run a container from this image. On the command line, type

docker run -it elanstop/sift-seq:latest

Usage

To examine predictions, e.g., for the input file all_raw_reads.fasta contained in the data folder, cd to the sift_seq directory within the container and run

python make_prediction.py all_raw_reads.fasta output.csv

where output.csv is the chosen name of the output file that will hold the predictions. More generally, input files stored on your machine may be copied to the container using docker cp.

To train the model using the example data, cd to the sift_seq directory within the container and run:

python train.py

Trained models are saved in the saved_models folder after each epoch and labelled with their validation accuracy. To supply your own training data, simply use docker cp to transfer files to the container, and modify the paths within train.py to point to your chosen files.

Contact

Elan Stopnitzky, e.stopnitzky@gmail.com