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.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc2356162 |
| Date | 07 1900 |
| Creators | Pusadkar, Vaidehi |
| Contributors | Azad, Rajeev K., Lund, Amie, Antunes, Mauricio S., Allen, Michael S., Padilla, Pamela A., Shulaev, Vladimir |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Pusadkar, Vaidehi, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
Page generated in 0.0014 seconds