Addressing geometric abnormalities and algorithmic shortcomings in metagenomic analysis.

Our work aims to identify shortcomings in microbiome analysis methods, making maximal use of sequencing data output to accurately represent sample relationships in a microbiome dataset. We introduce LMdist, a dimensionality reduction method for adjusting pairwise distances to more accurately represent distances along a sampling gradient. This method can be applied to a range of microbiome applications from soil to human gut microbiome studies. Applications beyond microbiome may benefit as well considering the ubiquity of dimensionality reduction in high dimensional datasets. We then implement the MAGEnTa pipeline, tracking engraftment of microbes following fecal microbiome transplants in the human gut. Making use of the whole genome reads produced by shotgun sequencing combined with Bayesian estimation, we can more accurately estimate strain level engraftment following microbiome transplants. This pipeline could prove useful in expanding personalized medicine for microbiome therapies, allowing researchers and clinicians to more accurately predict successful pairs of donor and recipient microbiomes prior to transplant. Finally, we apply longitudinal analysis approaches, including LMdist, to a large infant microbiome dataset. The infant microbiome is well known to diversify over the first two years of life, but the influence of intrapartum and pediatric antibiotics on the infant microbiome and growth is still largely unknown. We explore the interactions between microbiome, growth, and antibiotics over time to determine how clinical choices may impact infant development.

Author dc:creator

• Hoops, Susan

• Data Science
• Dimensionality Reduction
• Manifold Learning
• Metagenomics
• Microbiome
• Ordination

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/11299/270565

OAI identifier oai:identifier

oai:conservancy.umn.edu:11299/270565