A Framework for Microbial Dark Matter Taxonomic Inference by Genomic Context

Thesis Abstract

The study addresses the persistent challenge of characterizing microbial dark matter—unclassified microbes that evade conventional taxonomic assignment—by leveraging genomic context as a robust inference framework. The aim is to develop and validate a framework that integrates synteny, gene neighborhood conservation, pan-genome dynamics, and phylogenomic signals to improve taxonomic inferences for uncultured and poorly characterized microbes. Specific objectives include (i) constructing a genomic-context feature set from curated metagenome-assembled genomes (MAGs) across diverse environments, (ii) developing an inference model that combines Bayesian hierarchical reinforcement with machine learning classifiers to assign provisional taxonomy, (iii) evaluating framework performance against conventional sequence-based approaches using benchmark datasets, and (iv) identifying contextual genomic signatures predictive of taxonomic status at the family and genus levels. The methodology adopts a mixed-methods, theory-driven approach anchored in the frameworks of phylogenomics and genomic neighborhood theory. The population comprises publicly available MAGs and single-amplified genomes (SAGs from marine, soil, and human-associated microbiomes, totaling approximately 38,000 contigs and 1,250 high-quality bins with completeness >70% and contamination <5%). Sampled datasets include representative lineages spanning multiple phyla (Bacteroidota, Proteobacteria, Firmicutes, Acidobacteriota) to maximize diversity of genomic contexts. Data collection instruments involve a standardized pipeline for gene annotation (Prokka), conserved marker extraction (PhyloRank markers), synteny mapping (SibeliaZ), orthogroup clustering ( OrthoFinder), and genomic-context feature extraction (gene neighborhood topology, intergenic distances, operon structure, and mobile genetic element associations). An ensemble inference engine combines a Bayesian hierarchical model with gradient-boosted trees to integrate context features with sequence-based signals (rRNA operon structure, conserved marker presence/absence). Validation employs 10-fold cross-validation, holdout testing on an external MAG set, and comparison with state-of-the-art taxonomic classifiers (GTDB-Tk, sourmash MinHash workflows). Statistical analysis uses precision-recall, F1-scores, and area under the precision-recall curve, with ablation studies to quantify the contribution of each genomic-context feature. Robustness checks include sensitivity analyses across completeness thresholds (70–90%) and varying contamination levels. Key expected findings include (i) genomic-context features significantly improve taxonomic resolution for uncultured lineages, increasing genus-level assignment accuracy by 12–18 percentage points relative to sequence-only methods; (ii) synteny conservation and operon architectures exhibit strong predictive power for higher-ridelity placement within underrepresented taxa, (iii) a probabilistic scoring schema enables confident provisional assignments with calibrated uncertainty intervals, guiding subsequent experimental validation, and (iv) the framework reveals previously unrecognized clade associations consistent with emergent phylogenomic signals. The study anticipates identifying robust genomic-context signatures—such as conserved co-localization of ribosomal operons with unique regulatory genes and distinctive mobile element associations—that correlate with taxonomic boundaries across diverse environments. The contribution to knowledge lies in providing a formalized, data-driven framework that operationalizes genomic context as a primary taxonomic cue for microbial dark matter, bridging gaps between content-based and context-based taxonomic inference, and delivering a replicable pipeline applicable to large-scale metagenomic projects. The framework offers a transparent, probabilistic mechanism to quantify uncertainty in taxonomic assignment, facilitating more accurate ecological and evolutionary inferences from MAGs and SAGs. The study concludes that incorporating genomic-context information markedly enhances taxonomic inference for uncultured taxa and argues for its integration into standard taxonomic workflows. Recommendations include extending the model to incorporate functional-context signals (metabolic pathway co-occurrence), expanding to plasmid-encoded gene neighborhoods, and establishing a community benchmark with standardized MAG/SAG datasets to foster continual improvement and comparability across methods.