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◆ Journal of bioinformatics and computational biology2026-08-01· Computational biology

A scalable pan-genomic pipeline for annotation-free discovery of species-specific markers: Application to Staphylococcus aureus.

Yuyang Zhou, Jiayi Wang, Junhua Xiao, Yuxun Zhou, Kai Li

原始摘要(英文原文)· Original abstract
Current bioinformatics approaches for bacterial diagnostic target discovery remain constrained by their reliance on gene annotations and fixed-boundary genome segmentation, which overlook unannotated intergenic regions and introduce sequence-truncation artifacts. Here, we developed an open-source, annotation-independent pan-genomic pipeline featuring an overlapping sliding-window algorithm (500-bp window, 100-bp step) and a three-tier subtractive screening funnel. Using Staphylococcus aureus as a model, the pipeline screened 1629 genomes against 852 non-S. aureus Staphylococcus genomes and >20,000 background bacterial genomes. Seven highly conserved, unannotated targets (SA-1-SA-7) were identified, with all seven translated into qPCR primer sets (SAP-1-SAP-7), among which three (SAP-1-SAP-3) were further characterized by in vitro experiments. Multi-layer in silico evaluation demonstrated 100% intraspecific sensitivity and zero cross-reactivity against background genomes, including the S. aureus complex. In vitro testing using crude cell lysates confirmed specific amplification of S. aureus DNA without non-target cross-reactivity, establishing a qualitative limit of detection (LOD) of 105 CFU/mL. Additional computational validation on draft genomes, raw sequencing reads, near-neighbor species, and a clinical truth set corroborated marker robustness under realistic conditions. This framework successfully circumvents conventional gene-centric limitations, providing a generalizable computational strategy for target discovery across other high-priority bacterial pathogens.
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A scalable pan-genomic pipeline for annotation-free discovery of species-specific markers: Application to Staphylococcus aureus. — 科研速览 Science Skim