When technology meets technology: Retrained ‘Inception V3’ classifier for NGS based pathogen detection

Accurate characterization of pathogenic microbes that may be present in food or clinical samples is essential in the design of appropriate intervention strategies. Inherent genomic patterns (codon-biases and rate of evolution) do simplify the classification of microbes at most taxonomic levels (genus and above), but mostly blur classification at Species/Strain levels. Hence, their classification at these finer taxonomic levels requires high-resolution genomic-data that provide SNP (Single Nucleotide Polymorphism) level precision. Existing classification methods involve either targeted amplification of sero-specific genes (serotyping and MLST) or sequencing of the entire microbial genome, both of which require extra time and resources. We present a computational approach, which harnesses the power of the metagenomic NGS-data and object-detection abilities of Convolutional Neural Networks (CNN)(Inception V3), for precise classification of pathogens by converting genomic-data (NGS-reads) into images (nucleotide-by-color). A small scale retraining (<;50 images/class) of `Inception V3' resulted in a classifier with 100% and 96% validation and test accuracies, respectively, when classifying pathogens such as Campylobacter coli/jejuni and Escherichia coli (O157:H7 and Non O157-STECs). We aim to extend this protocol to the detection of several microbes (multiple-objects) in a metagenomic image (genomic image of an entire microbial community).