VIDHOP, viral host prediction with Deep Learning

Mock, Florian; Viehweger, Adrian; Barth, Emanuel; Marz, Manja

MOTIVATION: Zoonosis, the natural transmission of infections from animals to humans, is a far-reaching global problem. The recent outbreaks of Zikavirus, Ebolavirus, and Coronavirus are examples of viral zoonosis, which occur more frequently due to globalization. In case of a virus outbreak, it is helpful to know which host organism was the original carrier of the virus to prevent further spreading of viral infection. Recent approaches aim to predict a viral host based on the viral genome, often in combination with the potential host genome and arbitrarily selected features. These methods are limited in the number of different hosts they can predict or the accuracy of the prediction. RESULTS: Here, we present a fast and accurate deep learning approach for viral host prediction, which is based on the viral genome sequence only. We tested our deep neural network (DNN) on three different virus species (influenza A virus, rabies lyssavirus, rotavirus A). We achieved for each virus species an AUC between 0.93 and 0.98, allowing highly accurate predictions while using only fractions (100-400 bp) of the viral genome sequences. We show that deep neural networks are suitable to predict the host of a virus, even with a limited amount of sequences and highly unbalanced available data. The trained DNNs are the core of our virus-host prediction tool VIDHOP (VIrus Deep learning HOst Prediction). VIDHOP also allows the user to train and use models for other viruses. AVAILABILITY: VIDHOP is freely available under https://github.com/flomock/vidhop SUPPLEMENTARY INFORMATION: Available at DOI 10.17605/OSF.IO/UXT7