Daniel T. McDonald

This paper integrates microbiomic and metabolomic data to identify microbial and metabolic drivers of CNS autoimmunity in a mouse model of MS.

This study identifies distinct microbial and metabolic features in non-lesional acne skin using multi-omics analyses, showing molecular dysbiosis extends beyond visible lesions.

The paper presents a large-scale gut microbiome dataset across 1,553 host species and identifies major compositional shifts in gut microbiomes during animal evolution.

This study shows that FXR deficiency prevents IHC-induced atherosclerosis by disrupting microbiome-bile acid signaling in ApoE-/- mice.

The paper introduces MOPP, a pipeline for processing metaRibo-Seq data to improve taxonomic and functional analysis by reducing false positives through coverage breadth filtering.

DartUniFrac is a fast, scalable algorithm for microbiome analysis that matches the statistical performance of exact UniFrac methods.

The paper introduces microbiomeMASST, a metadata network graph linking metabolites across datasets, enabling cross-study analysis of microbial metabolites and their biological context.

The paper introduces a collaborative platform for curating microbial metabolite knowledge to enhance metabolomics data interpretation.

The paper uses whole-body metabolic models to link gut microbiota to metabolic changes in Alzheimer's disease, identifying key microbial drivers and metabolites involved.

The paper examines how environmental and maternal factors influence infant gut metabolism through microbiome and metabolome profiling in Bangladeshi mother-infant pairs.

The paper develops a drug exposure resource from untargeted metabolomics data to identify medications without clinical records.

This paper introduces C-DEPP, a method that scales deep phylogenetic embedding to large reference trees by using an ensemble approach, enabling efficient placement of 20 million 16S fragments on the GG2 tree in 41 hours.

Greengenes2 aligns 16S rRNA and shotgun metagenomic data using a whole-genome phylogeny, showing agreement in principal coordinates, taxonomy, and phenotype effect size.

Evident calculates effect sizes and performs power analysis for microbiome community differences, scaling to large datasets with many metadata covariates.

This paper introduces OGUs, a metagenome analysis method that uses sequence alignment hits to reference genomes for community analysis, showing better performance in predicting biological traits than taxonomic or 16S rRNA methods.

EMPress provides an interactive tool for visualizing phylogenetic trees alongside multi-omic data, enabling exploratory analyses of microbiome and metabolome datasets.

Dual patterned unique molecular identifiers (UMIs) turn Nanopore or PacBio CCS reads of ~4400 bp rRNA operons into single-molecule consensus sequences with 0.0049% (ONT) or 0.0006% (PacBio) mean error.

The paper introduces Qemistree, a method for analyzing metabolomics mass spectrometry data using hierarchical molecular fingerprints and chemical ontologies to explore chemical composition.

This paper expands the interpretability of human metabolomes by using food-based annotations, increasing the fraction of molecular features understood 7-fold.

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.

This paper shows that incorporating species abundance data improves taxonomy classification accuracy for amplicon sequences.

This paper engineers non-pathogenic E. coli to detect ETEC using quorum sensing and cellulose encapsulation for environmental monitoring.