Leo Lahti

The paper introduces BioSIFTR, a tool that uses shallow shotgun sequencing and biome-specific databases to generate accurate taxonomic and functional profiles, validated against deep sequencing data.

This study links gut transit time, inferred from stool consistency, to microbial metabolites and microbiota in infants, showing associations with breastfeeding, bile acid metabolism, and short-chain fatty acids.

This study analyzes gut microbiome and resistome diversity in Indigenous Orang Asli communities in Malaysia, finding that urbanization reduces microbial diversity but increases antibiotic resistance gene diversity.

NMFBatch reduces technical variation and preserves biological structure in proteomics data through batch correction and imputation.

This study investigates the effects of xylo-oligosaccharide (XOS) prebiotic on hepatic steatosis in overweight individuals, finding that it reduces harmful gut metabolites and liver fat in responders.

The paper introduces an open-source ecosystem for microbiome data science using Bioconductor, supporting modular workflows for analyzing complex, multi-modal datasets.

This study transfers exuberant toddler gut microbiota to rats, increasing their exploratory behavior without affecting locomotion or social behavior.

LimROTS improves differential expression analysis in proteomics by combining empirical Bayes and reproducibility-optimized statistics for better accuracy and reproducibility.

This paper reviews data science methods for longitudinal multi-omics in microbiome research, highlighting the need for integrated approaches and the lack of widely adopted methods.

This study examines skin microbiota variation among Indian families, finding that diet and geography influence microbiota composition, while family membership shows significant association.

This study integrates polygenic risk scores and gut metagenomic data to improve prediction of common diseases, showing enhanced predictive performance over conventional risk factors.

The paper finds a negative association between infant left amygdala volume and fecal microbiota alpha diversity in early life.

Greengenes2 aligns 16S rRNA and shotgun metagenomic data using a whole-genome phylogeny, showing agreement in principal coordinates, taxonomy, and phenotype effect size.

The paper compares missing data handling methods for variant pathogenicity predictors and finds that simpler imputation methods like mean imputation perform best.

This paper quantifies how ecological memory affects community dynamics by incorporating it into models and shows its impact on resilience, stability, and state shifts.

This study uses gut microbiome data and machine learning to improve liver disease risk prediction, showing that combining microbiome features with conventional risk factors enhances model performance.

In 5,959 Finns with genotypes and stool metagenomes, GWAS found 583 independent SNP-taxon associations; dairy intake modified LCT effects on Bifidobacterium, and ABO-linked Faecalicatena lactaris tracked secretor status.

This paper introduces OGUs, a metagenome analysis method that uses sequence alignment hits to reference genomes for community analysis, showing better performance in predicting biological traits than taxonomic or 16S rRNA methods.

This study identifies 118 genetic loci associated with 2,319 metabolite features and finds 46 causal effects of metabolites on common diseases using genome-wide association and Mendelian randomization.