Genome mining algorithm for identifying identical repeat sequences to enhance DNA-based diagnostic assays

A pair of primers that can bind at multiple loci across the genome and randomly amplify multiple copies increases the analytical sensitivity of the currently used diagnostic assays. We developed a novel genome mining algorithm to identify short identical repeat sequences (IRSs) dispersed across the genome. The genome mining algorithm for IRS identification can be accessed from the GitHub portal (https://github.com/BPaul-bioinfoLAB/IRS-Finder). Using this algorithm, we have identified the IRS from five pathogens, namely, human gammaherpesvirus, vaccinia virus, <i>Mycobacterium tuberculosis, Plasmodium falciparum</i>, and <i>Phytophthora palmivora</i>. <i>In silico</i> PCR revealed that these IRSs can amplify multiple nonhomologous regions of variable amplicon sizes via three priming combinations. We further performed a polymerase chain reaction (PCR) assay with an IRS pair identified in <i>M. tuberculosis</i>. Interestingly, the PCR with single IRS amplified multiple nonhomologous copies and even more variable-sized copies in pair. These results indicate that the IRS-based diagnostic assays can detect pathogens in case of low-concentration DNA during disease progression. The genome mining algorithm can be used as a translation technology platform for developing highly sensitive varieties of PCR, microarray, loop-mediated isothermal amplification, and fluorescence in situ hybridization-based diagnostic assays.