Hybrid expert system for robust detection of rare sequence signals: a computational proof-of-concept in host-dominated backgrounds.

Deciphering microbial symbiosis in robust environmental and host-associated systems increasingly depends on the reliable recovery of weak biological signals from sequencing data dominated by non-target background. In practice, low-abundance symbiont-derived signatures are often obscured by background DNA, recurrent technical artifacts, and context-dependent false-positive calls, limiting the accuracy of downstream ecological and functional inference. Here, we present SymbioFilter, a hybrid expert system designed to improve the specificity and stability of rare-signal detection in host-associated microbiome sequencing data with substantial background noise. (i.e., overwhelming host-derived DNA, recurrent alignment errors, and sequencing artifacts). Specifically, this framework is intended for specialized usage cases where standard host-depletion strategies fail, such as capturing minor microbe-induced host somatic mutations or rare microbial homologs, which reflect subtle and intimate interactions between microbes and the host. SymbioFilter integrates three coordinated layers: (i)ensemble-based candidate detection, (ii)machine-learned background/noise discrimination using an XGBoost classifier, and (iii) rule-guided suppression of artifact-prone genomic regions using curated repetitive and blacklist annotations. Rather than relying on a single decision rule, the framework combines probabilistic classification with expert-defined constraints to preserve weak true signals while reducing recurrent false positives. This design specifically targets analytical failure modes common to host-dominated and low-input datasets, where precision is frequently compromised by rare-event noise. As a stringent proof-of-concept benchmark, we evaluated SymbioFilter in synthetic gradient spike-in datasets. Crucially, to ensure rigorous computational evaluation with an absolute ground truth-a standard that is currently unattainable in complex, real-world microbiome samples at ultra-low abundances-we utilized plasma cfDNA-like low-frequency conditions as a highly controlled, demanding proxy. Across gradient abundance levels, the framework consistently reduced false-positive inflation, improved agreement with the known ground truth, and maintained stable classification performance. Compared with a baseline pipeline and a widely used variant-calling workflow, SymbioFilter achieved lower mean squared error, stronger true-positive/true-negative balance, and consistently high precision-recall behavior, particularly under the most challenging low-abundance settings. Although validated here in a proxy benchmark environment, the computational principles of SymbioFilter address a broader class of sequencing problems central to microbial symbiosis research: identifying rare, functionally relevant biological signals in complex, noise-prone backgrounds. By providing a transferable, modular, and open computational strategy for robust signal recovery, SymbioFilter offers a useful methodological foundation for future studies of host-microbe interactions, resilient community assembly, and symbiosis-associated ecosystem stability. The code is freely available for academic use at https://github.com/hello-json/SymbioFilter.