Amy D. Willis

The paper introduces fastEmu, a scalable method for estimating differential abundance in microbial metagenomics data, achieving faster runtime with minimal impact on error rates.

happi tests gene enrichment hypotheses while accounting for genome quality in metagenome-assembled genomes.

The paper introduces an interactive method for analyzing gene-level phylogenies in microbial evolution, enabling visualization and comparison of gene histories across bacterial strains.

The paper compares gut metagenomes of dairy workers and community controls to assess antibiotic resistance and pathogen exposure risks.

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

The paper identifies five bacterial strains associated with Crohn's disease using a novel method combining machine learning and assembly graphs for metagenomic analysis.

The paper investigates how segment number thresholds influence germline cluster proliferation and gametogenesis in juvenile Platynereis dumerilii, finding a correlation between growth state and developmental stages.

The paper proposes a method to estimate absolute microbial abundances using both relative and absolute abundance data, improving accuracy by accounting for detection efficiencies.

The paper introduces a tool for gene-level metagenomic analysis to identify genomic islands linked to immunotherapy response in microbiome data.

This paper introduces a statistical model and method for estimating cell lineage trees using maximum-likelihood phylogenetics, showing improved accuracy over existing methods in simulations and zebrafish development.

DivNet estimates diversity in networked communities, improving alpha- and beta-diversity accuracy by incorporating ecological interactions, especially in high-diversity ecosystems.

The paper identifies and corrects systematic bias in metagenomic sequencing by modeling how bias distorts measurements and validating it across different protocols and datasets.

The paper introduces a computational method to cluster microbial genes by co-abundance, identifying reproducible gene groups linked to colorectal cancer and inflammatory bowel disease.