With the large-scale metagenome sequencing data produced currently, alignment-free metagenomic profiling approaches have demonstrated the effectiveness of Markov models in addressing the limitations of alignment-based techniques, particularly in handling unclassified reads. The development of POSMM (Python Optimized Standard Markov Model), employing SMM (Standard Markov Model) algorithm, initially showcased competitive performance when compared to tools such as Kraken2. However, when subjected to simulated damages present in ancient metagenomics data, shortcomings emerged, leading to false positives or misclassified sequences that compromised overall classification accuracy. To address this problem, we developed a segmental genome model (SGM) algorithm based on the generation of the ensemble of models representing distinct classes of DNA segments in a genome. SGM incorporated a recursive segmentation and clustering approach to segregate regions of distinct composition in a microbial genome. An ensemble of higher-order Markov models is trained on DNA clusters generated for each genome. A database of models of genomes, with each genome represented by multiple Markov models are then queried to infer the origin of reads from a metagenome. SGM was benchmarked using diverse synthetic metagenome datasets of varying composition, read lengths, and error profiles. The comparative assessment showed that SGM consistently outperformed SMM. SGM brings in significant advances in alignment-free profiling, offering a new promising avenue for metagenomic exploration through its integration in the next version of POSMM. Furthermore, leveraging the power of integration of alignment-free and alignment-based approaches and highlighting the versatility and practicality of these methods in addressing critical public health challenges, we developed a statistical analysis and machine learning pipeline to identify candidate microbes associated with COVID-19. This involved a meta-analysis of the whole genome sequencing data of COVID-19 patients' samples and its predictive modeling to discern the distinctive microbial features. We improve and explore alignment-free metagenome profiling to raise the bar in metagenome profiling in complex real-world samples.

<p dir="ltr">Plant secondary metabolites (PSMs) are specialized compounds produced in response to a range of insect herbivores and microbes, making them important in shaping tri-trophic interactions. However, despite being well-studied in the context of plant-insect coevolution, it is unclear how PSMs impact microbial communities associated with plants and the insect herbivores that feed on them. The overarching goal of this dissertation was to better understand how variation in plant defensive responses, particularly expression of PSMs, influences the composition and functional potential of microbial communities associated with plant tissues (roots and leaves) and insect herbivores. Monarchs (<i>Danaus plexippus</i>) and their milkweed hosts (<i>Asclepias spp.)</i> are well-studied for mechanisms of plant defense and insect counter defense, but little is known about the role of associated microbial communities in this iconic system. To address this knowledge gap, a combination of metabarcoding and metagenomics was used to characterize the taxonomic composition and functional gene profiles of bacterial communities associated with plant tissues (i.e., phyllosphere and rhizosphere) and monarch caterpillars fed on multiple milkweed species (<i>A. curassavica</i>, <i>A. syriaca</i>, and <i>A. tuberosa</i>). Findings show the composition of phyllosphere, rhizosphere, and monarch microbiomes vary across milkweed species in terms of diversity and relative abundance of bacterial taxa. Furthermore, phyllosphere and rhizosphere microbiomes were shown to have distinct functional gene profiles and presence of potential PSM metabolism genes that also varied across milkweed species. Rhizosphere microbiomes had a greater overall capacity for PSM metabolism compared to the phyllosphere, having more genes, and associated metabolic pathways involved in degradation or detoxification of known classes of PSMs. However, plant associated microbiomes were not generally affected by monarch feeding, evidenced by few changes in taxonomic composition or abundance of genes predicted to be involved in PSM metabolism. Interestingly, monarch microbiomes shared >90% of their taxa with their host plants, but there was little evidence of PSM metabolism genes present in functional gene profiles. Overall, this dissertation lays the foundation for understanding how PSMs shape all the microbial communities associated with monarchs and their milkweed hosts. Findings suggest plant defensive responses affect the assembly, functional potential and ultimately the evolution of plant and insect microbiomes.</p>