GRaDL: A Framework for Animal Genome Sequence Classification with Graph Representations and Deep Learning

Bovine Respiratory Disease Complex (BRDC) is a complex respiratory disease in cattle with multiple etiologies, including bacterial and viral. It is estimated that mortality, morbidity, therapy, and quarantine resulting from BRDC account for significant losses in the cattle industry. Early detection and management of BRDC are crucial in mitigating economic losses. Current animal disease diagnostics is based on traditional tests such as bacterial culture, serolog, and Polymerase Chain Reaction (PCR) tests. Advancements of data analytics and machine learning are setting trends in several metagenome sequencing applications. In this work, we demonstrate a machine learning approach to identify pathogen signatures present in bovine metagenome sequences using k-mer-based network embedding followed by a deep learning-based classification task. With experiments conducted on two different simulated datasets, we show that networks-based deep learning approaches can detect pathogen signatures with up to 89.7% accuracy. We will make the simulated data used in our experiments available publicly upon request to tackle this important problem with more innovative approaches.