Computational approaches for taxa-free microbiome-phenotype associations from metagenomic sequencing Anastasia Lucas

Format:

Book

Thesis/Dissertation

Author/Creator:

Lucas, Anastasia, author.

Contributor:

University of Pennsylvania. Genomics and Computational Biology., degree granting institution.

Language:

English

Subjects (All):

Bioinformatics.

Biostatistics.

Epidemiology.

0715.

0766.

0308.

Local Subjects:

Bioinformatics.

Biostatistics.

Epidemiology.

0715.

0766.

0308.

Genre:

Academic theses

Physical Description:

1 online resource (166 pages)

Contained In:

Dissertations Abstracts International 87-12B

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2026

Language Note:

English

Summary:

The gut microbiome has shown immense clinical potential for human disease as non-invasive biomarkers and new therapeutic interventions such as fecal microbiota transplants. The lack of replicable taxonomic associations across cohorts is currently limiting the clinical potential of the gut microbiome, resulting in the need for approaches that can explain the persistent relationships between different gut microbes and human health phenotypes. Given that the functional composition of the gut microbiome is more consistent across individuals and microbial proteins with the same function can originate in different species, microbial proteins may underlie the existing gut microbiome associations with human disease and health outcomes. However, there are currently no methods to quantify and efficiently map metagenomic short reads to microbial proteins in a taxa-agnostic way, a necessary first step in downstream biomarker prediction and clinical risk modeling. In this thesis, I provide an overview of existing computational approaches for functional metagenomic research, as well as three studies that utilize a new, taxa-free unit of measurement to quantify microbial proteins from shotgun metagenomics for sequence mapping, protein-level biomarker prediction, and integration of host multi-omic data in immune-mediated health outcomes. The first study uses five cohorts of melanoma patients prescribed anti-PD-1 to demonstrate the transferability of taxa-agnostic microbial protein biomarkers across cohorts in the United States. We additionally identify a multi-species 4Fe-4S operon associated with non-gastrointestinal immune-related adverse events to anti-PD-1. The second study then generalizes our protein quantification approach into kMermaid, a highly-resource efficient metagenomic read mapping tool, available to the broader research community. Using nested hashing of amino acid k-mer frequencies, kMermaid can map 40 million metagenomic reads to taxa-agnostic protein clusters using only six hours of compute time and 2GB of RAM. The last study demonstrates how kMermaid-derived features can be integrated with host metabolomic data to create tissue-specific models of biological aging. These studies demonstrate the clinical utility of taxa-agnostic approaches to metagenomic analyses and help clarify the functional roles of gut microbes in immune-related human health phenotypes

Notes:

Source: Dissertations Abstracts International, Volume: 87-12, Section: B.

Advisors: Auslander, Noam Committee members: Grice, Elizabeth; Long, Qi; Li, Bo; Davenport, Emily

Ph.D. University of Pennsylvania 2026

Vendor supplied data

Local Notes:

School code: 0175

ISBN:

9798247973997

Access Restriction:

Restricted for use by site license