Hidden Markov Model: a shortest unique representative approach to detect the protein toxins, virulence factors and antibiotic resistance genes

Abstract Objective Currently, next generation sequencing (NGS) is widely used to decode potential novel or variant pathogens both in emergent outbreaks and routine clinical practice. However, the efficient identification of diverged pathogenomic compositions remains a big challenge. It especially true for short DNA sequence fragments from NGS, since similarity searching vulnerable false negatives positives, as mismatching matching with unrelated proteins. Therefore, this study aimed establish bioinformatics approach that can generate unique motif sequences profiling searching, resulting high specificity sensitivity. Results In study, we introduced Shortest Unique Representative Hidden Markov Model (HMM) identify bacterial toxin, virulence factor (VF), antimicrobial resistance (AR) reads. We first construct representative domain toxin genes, VFs, ARs avoid then use HMM models accurately VF, AR fragments. The benchmark shows achieve relatively sensitivity if appropriate cutoff value applied. Our be recognize protein known toxins pathogens, identifies their common characteristics searches similar other organisms.