The human microbiome, a complex ecosystem of microorganisms inhabiting various body sites, plays a crucial role in the immune system and overall human health. A comprehensive understanding of the microbiome and its interactions with the host is essential for advancing scientific knowledge and potential therapeutic interventions. This work focuses on two aspects of the microbiome space: taxonomic profiling for the identification of resident microbes, and longitudinal analysis to unravel the dynamics of microbial communities over time.
A fundamental step in microbiome analysis is taxonomic profiling: the identification of resident microbes in samples. Numerous tools have been developed to cater to different sequencing types (e.g., 16S versus WGS) and contexts. However, despite significant advances in the profiling field, further work is needed to establish optimal methods for metagenomic classification. To address this gap, we introduce MetaScope, a comprehensive R-based package for accurate microbial composition identification at a strain-level resolution within a sample. We have performed benchmarking against mock microbial communities to validate MetaScope's performance against popular competitors using 16S datasets.
Microbial time-series data presents unique challenges, including intricate covariate dependencies and diverse longitudinal study designs. Existing methods often fall short in addressing these challenges, lacking versatility, data type specificity, or the ability to account for the compositional nature of the data. In response, this work introduces LegATo, an open-source suite comprising modeling, visualization, and statistical tools tailored for analyzing microbiome dynamics. LegATo, with its user-friendly interface, accommodates various study structures and incorporates Generalized Estimating Equation (GEE) models, Hotelling’s T-squared tests, and several visualization functions. This toolkit enables researchers to identify microbial taxa affected by perturbations over time, such as the onset of disease or lifestyle changes, and predict their effects on the composition or stability of commensal bacteria. To illustrate the practical application of LegATo, we present two case studies focusing on the nasopharyngeal microbiomes of Zambian infants exposed to HIV and experiencing fatal acute febrile illness. These applications showcase the efficacy of LegATo for unraveling the complex dynamics of microbial communities, providing insight into the impact of specific perturbations on the microbiome.
In conclusion, this research contributes to the advancement of microbiome analysis by enhancing taxonomic profiling methodologies and addressing the challenges posed by longitudinal data. The presented tools, MetaScope and LegATo, provide valuable resources for researchers exploring the intricate interactions between the microbiome and host over time, paving the way for a deeper understanding of microbial dynamics and their implications for human health.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/48722 |
| Date | 08 May 2024 |
| Creators | Odom, Aubrey R. |
| Contributors | Johnson, W. Evan |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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