Jacob T. Nearing

MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery

MaAsLin 3 fits hurdle models separating microbial prevalence (augmented logistic) and abundance (median-adjusted log-linear) associations, improving precision over existing differential abundance tools and showing 77% of IBD associations are prevalence-driven.

Evaluating metagenomic analyses for undercharacterized environments: what's needed to light up the microbial dark matter?

The paper evaluates metagenomic analysis methods in simulated and real environmental data, finding that newer marker-based methods perform better at lower taxonomic levels.

Implications of taxonomic bias for microbial differential-abundance analysis

The paper examines how taxonomic bias affects differential-abundance analysis in microbiome studies and proposes methods to mitigate its effects.

Microbiome differential abundance methods produce disturbingly different results across 38 datasets

In 38 two-group 16S rRNA datasets, 14 differential abundance tools disagreed sharply; ALDEx2 and ANCOM-II were conservative and consistent, while edgeR and LEfSe inflated false positives.