Computational Framework for High Copy-Number Probe Selection and Cross-Binding Reduction

Abstract

DNA probe design plays a critical role in biosensor-based disease diagnostics, gene expression analysis and environmental monitoring. Traditional probe designs primarily target lower-copy genetic sequences, often leading to low detection sensitivity due to limited hybridization events. This study introduces a novel probe design strategy that leverages highly repetitive DNA sequences as target sites to amplify biosensor signals without requiring PCR-based amplification. The computational selection process is conducted using a custom-developed bioinformatics tool to identify repetitive sequences across the entire Mycobacterium tuberculosis genome, independent of gene boundaries. The identified sequences are then cross-referenced against the Homo sapiens genome using BLAST to minimize host cross-reactivity. The analysis revealed that a 23 bp sequence repeated 39 times in M. tuberculosis exhibits only 78% sequence identity with human DNA and is present in just two copies within the human genome. This suggests that the selected probe may yield substantially stronger hybridization signals for M. tuberculosis relative to human cfDNA, thereby enhancing biosensor sensitivity. The computational methodology introduced in this study provides a robust framework for designing high-sensitivity biosensors, enabling more effective infectious disease diagnostics, environmental monitoring and clinical point-of-care testing.