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, Mycobacterium tuberculosis, Plasmodium falciparum, and Phytophthora palmivora. In silico 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 M. tuberculosis. 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.