Curtis Huttenhower

The paper identifies N-acyl putrescines, particularly NOP, as immunomodulatory metabolites produced by gut microbiota that suppress inflammation in IBD models.

The paper investigates the link between gut microbiota composition and item recognition in healthy humans, finding that alpha diversity and beta diversity are associated with memory performance.

BAQLaVa improves viral profiling from metagenomes and metatranscriptomes, revealing reduced virome diversity in inflammatory bowel diseases and novel phage-host relationships.

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.

This study evaluates LLMs for microbial phenotype annotation, finding varied performance across models and traits, with confidence scores correlating with accuracy.

The paper presents a global respiratory pan-microbiome atlas integrating over 4,000 metagenomes to reveal ecological patterns in health and disease.

The paper benchmarks differential abundance methods using microbiome datasets with known biological ground truth, finding that compositional methods are less effective than commonly used non-compositional approaches.

The paper investigates shared and distinct functional pathogenicity of gut microbiomes in inflammatory diseases using germ-free mice colonized with human fecal samples.

The paper develops a model to estimate infant gut microbiome age using metagenomic data, finding conserved microbial succession patterns across diverse human populations.

Anpan quantifies strain-level associations in microbiomes, identifying genetic elements and phylogenetic effects linked to colorectal cancer, with improved accuracy over existing methods.

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

This paper introduces CompDA, a new method for health-microbiome association analysis that accounts for compositional data and ecological interactions, and demonstrates its effectiveness through simulations and real-data applications.

The paper presents a method to discover new human gut viruses using enriched virome samples and reports over 162,000 novel viral sequences.

The paper develops and validates a method to detect novel lateral gene transfer events in human microbiomes, identifying over 100K putative LGT events across >2K metagenomes.

Marker genes for 26,970 species-level genome bins, 4,992 defined only by metagenome-assembled genomes, let MetaPhlAn 4 quantify uncharacterized species and beat MetaPhlAn 3, mOTUs and Bracken on synthetic benchmarks.

This paper introduces HAllA, a method for discovering associations in multi-omic datasets, showing it outperforms existing approaches and identifies new biological relationships.

GeneGrouper clusters gene clusters across taxa to identify functional or evolutionary patterns, revealing a novel pduN pseudogene in Salmonella enterica.

The paper introduces a statistical model to simulate microbial community profiles and evaluates its performance in benchmarking analysis methods.

The paper develops MMUPHin for meta-analysis of microbial communities in IBD, identifying consistent microbial associations and showing no discrete microbiome types.

The paper introduces STORMS, a reporting guideline for human microbiome studies, providing a 17-item checklist to standardize and improve the organization and reporting of microbiome research.

PICRUSt2 places 16S rRNA variants into a 20,000-gene reference tree, predicting gene families, pathways and phenotypes; gene-family predictions matched or beat four tools against shotgun metagenomes.

HMP16SData provides integrated 16S rRNA sequencing data from the Human Microbiome Project, enabling easier analysis for researchers with basic R skills.

The paper introduces curatedMetagenomicData, a resource providing curated metagenomic profiles for analysis in R with minimal bioinformatic expertise.