J. T. Morton

Decoupling diet from microbiome dynamics results in model mis-specification that implicitly annuls potential associations between the microbiome and disease phenotypes-ruling out any role of the microbiome in autism (Yap et al. 2021) likely a premature conclusion

The paper critiques Yap et al.'s analysis of diet and microbiome associations with autism, showing their model's assumption of independence between diet and microbiome leads to incorrect conclusions about microbiome's role in autism.

Multi-omic analysis along the gut-brain axis points to a functional architecture of autism

The paper identifies a functional architecture of autism along the gut-brain axis using multi-omic data, revealing microbial and metabolic profiles linked to autism symptoms.

Multi-omics profiling of Earth's biomes reveals patterns of diversity and co-occurrence in microbial and metabolite composition across environments

The paper performs a multi-omics meta-analysis of microbial and metabolite data from diverse environments, revealing patterns of diversity and co-occurrence across biomes.

Microbe-metabolite associations linked to the rebounding murine gut microbiome post-colonization with vancomycin resistant Enterococcus faecium

This study investigates how the gut microbiome changes in mice after colonization with vancomycin-resistant Enterococcus faecium, identifying metabolite associations linked to bacterial shifts.

Deep metagenomics examines the oral microbiome during dental caries, revealing novel taxa and co-occurrences with host molecules

This study uses deep metagenomics to analyze the oral microbiome in children with and without dental caries, identifying novel taxa and host-microbe interactions.

American Gut: an Open Platform for Citizen-Science Microbiome Research

The paper uses citizen-science data to analyze human stool microbiomes, revealing new insights into microbial diversity, metabolome composition, and associations with diet and health.

Phylofactorization - a graph partitioning algorithm to identify phylogenetic scales of ecological data

This paper introduces Phylofactorization, a graph-partitioning algorithm to identify phylogenetic scales in ecological data, and demonstrates its application to mammalian body mass and gut microbiome studies.