Luisa Warchavchik Hugerth

This study uses multi-omics to uncover Gardnerella genomic features linked to biofilm formation and interspecies interactions in the vaginal microbiome, identifying potential therapeutic targets for bacterial vaginosis.

The paper introduces a Gaussian random field model for imputing missing data in longitudinal microbiome studies, achieving comparable accuracy to baselines while modeling temporal and multivariate correlations.

This paper benchmarks virus identification tools on vaginal metagenomes, finding geNomad excels in overall classification, VIBRANT minimizes false positives, ViraLM detects short contigs and eukaryotic viruses, and VirSorter2 performs well on VLP-enriched assemblies.

PLAGUE improves plasmid detection accuracy by reducing false positives in short-read assemblies while maintaining sensitivity.

The paper predicts food allergies and postpartum depression using microbiome time series data with imputation and logarithmic transformation, achieving balanced accuracies of 0.72 and 0.62 respectively.

The paper proposes a roadmap for fair reuse of public microbiome data by introducing a Data Reuse Information tag (DRI) to facilitate communication between data creators and consumers.

This paper compares methods for scaffolding multiple regions of the 16S rRNA gene to improve taxonomic resolution, finding that Sidle provides the least bias and most precise species-level resolution.

This paper evaluates best practices for sequencing-based characterization of the human vaginal microbiome, comparing 16S rRNA gene sequencing and shotgun metagenomic approaches.

The paper compares single-amplified genomes and metagenome-assembled genomes from uncultivated prokaryotes, finding high similarity between them and highlighting their complementary strengths.

The paper develops an automated pipeline for human microbiome sampling, 16S rRNA sequencing, and bioinformatics processing, providing guidelines for sample collection, storage, DNA extraction, PCR, and analysis.