Nicholas D. Youngblut

This study shows that gut microbiome diversity correlates with infection by multiple soil-transmitted helminths in African populations, using metagenomic sequencing for parasite detection.

The paper reports shotgun gut metagenomes and assembled microbial genomes from an urban Colombian cohort, complementing existing 16S rRNA gene and host data.

This study shows that gut microbiome flagellin affects vaccine reactogenicity and can be influenced by diet.

This study links medication use to distinct gut microbial features in anxiety and depression, showing antidepressants have a larger effect on microbiota than diagnoses.

ResMiCo improves metagenome assembly quality by accurately identifying misassembled contigs using deep learning, achieving better performance than existing methods.

endoR interprets tree ensemble models in microbiome data by extracting feature importance and interactions, confirming known associations and revealing new microbial network insights.

The paper evaluates tree-based and tree-agnostic diversity measures in human gut metagenomes, finding tree-based measures better explain phenotypic variation and improve predictive models.

Struo2 constructs and updates metagenome reference databases efficiently, improving speed and scalability for large microbial genome datasets.

DeepMAsED evaluates metagenomic assembly quality using deep learning, identifying misassembled contigs without references and showing high accuracy on large assemblies.

Struo builds custom databases for metagenome profilers by integrating new genomes, improving read mappability in synthetic and real datasets.

The paper shows that human AMY1 gene copy number affects oral and gut microbiome composition and function, with low-AMY1 associated with enhanced complex carbohydrate breakdown and high-AMY1 linked to resistant starch fermentation and higher adiposity.

The paper introduces HTSSIP, an R package for analyzing HTS-SIP data, addressing the lack of accessible and well-documented tools for this task.

This paper evaluates the accuracy of DNA-SIP methods using a simulation tool (SIPSim) and compares two analytical approaches, finding HR-SIP more specific and robust than q-SIP.