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Genome mining algorithm for identifying identical repeat sequences to enhance DNA-based diagnostic assays.
Kalepu Rajeswari1, Raksha Poojary2, Padival Shruptha3
1Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Genome
|January 6, 2026
Summary
Researchers developed a novel genome mining algorithm to identify identical repeat sequences (IRSs). This innovation enhances diagnostic sensitivity for detecting pathogens, even with low DNA concentrations, improving disease diagnosis.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Diagnostics
Background:
- Current diagnostic assays lack sufficient analytical sensitivity for detecting low pathogen loads.
- Identifying repetitive DNA elements across genomes is challenging with existing methods.
Purpose of the Study:
- To develop a novel genome mining algorithm for identifying short identical repeat sequences (IRSs).
- To assess the potential of IRSs for enhancing the sensitivity of diagnostic assays.
Main Methods:
- Developed a genome mining algorithm (IRS-Finder) to identify dispersed IRSs.
- Identified IRSs in five pathogens: gammaherpesvirus, vaccinia virus, Mycobacterium tuberculosis, Plasmodium falciparum, and Phytophthora palmivora.
- Performed in-silico PCR and experimental PCR assays to evaluate amplification potential.
Main Results:
- The algorithm successfully identified IRSs across multiple pathogen genomes.
- In-silico and experimental PCR demonstrated that IRSs can amplify multiple non-homologous regions with variable amplicon sizes.
- A single IRS pair from M. tuberculosis amplified multiple non-homologous copies, increasing assay sensitivity.
Conclusions:
- Identified IRSs offer a promising strategy for developing highly sensitive diagnostic assays.
- The genome mining algorithm serves as a versatile platform for creating advanced diagnostic tools.
- IRS-based assays can improve pathogen detection in low-concentration DNA samples, aiding disease progression monitoring.
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