HSeeker: an algorithm for systematic H-DNA sequence identification

Abstract

H-DNA is a naturally occurring intramolecular DNA triplex structure formed by Hoogsteen hydrogen bonds at homopurine-homopyrimidine mirror repeats and has functional roles in gene regulation, genome instability, and human disease. The existing H-DNA detection tools capture only a subset of H-DNA sequences, often missing relevant sequence features or failing to assess key aspects of structural stability. To address this gap, we present "HSeeker", a state-of-the-art computational tool that compiles a three-part algorithm to identify and score potential H-DNA-forming sequences. Using a center-outward search algorithm approach, HSeeker evaluates candidate hinge positions and spacer lengths while allowing configurable mirror mismatches and purine-pyrimidine composition thresholds. The greedy overlap removal phase resolves overlapping candidates by retaining the longest and most compact motif within each overlapping region. Finally, the thermodynamic stability scoring algorithm evaluates the candidate motifs using an experimentally informed scoring model that incorporates Hoogsteen G-G and A-A bonds, consecutive-pair stacking, and imposes penalties for mismatches and disrupted stacks. The scoring procedure also optimizes motif boundaries by trimming weak terminal positions and reassigning unstable arm positions to the spacer. HSeeker reports genomic coordinates, sequence information, pairing and stacking components, and an overall stability score. HSeeker is also user-friendly, available as a Python package and as a web application, supporting configurable, high-throughput analysis and exportability of predicted H-DNA motifs. HSeeker provides an accessible and reproducible framework for investigating the distribution and potential stability of H-DNA-forming sequences across genomic datasets.