Chenhao Li

This study identifies and characterizes the IsmB enzyme, which catalyzes a key step in coprostanol formation by the human gut microbiome, and links its presence to lower serum cholesterol levels.

CAZyLingua uses protein language models to annotate carbohydrate-active enzymes in metagenomic data, outperforming traditional methods and revealing new annotations in disease-related datasets.

The paper identifies genetic variants affecting innate lymphoid cells in the gut using single-cell RNA-seq and validates their regulatory roles across cell types.

The paper analyzes gut metagenomes of Asian octogenarians to identify age-related microbial shifts and their associations with aging phenotypes.

StrainPanDA links strain composition and gene content in metagenomic data to understand microbial functions and phenotypes.

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

BEEM-Static infers ecological interactions from metagenomic data, outperforming existing methods in detecting interaction directionality and robustness to non-steady-state samples.

This paper introduces a hybrid metagenomic assembler that improves assembly quality and enables high-resolution analysis of resistance determinants and mobile elements in the human gut microbiome.

BEEM improves ecological modeling of microbial communities using longitudinal metagenome data by integrating biomass estimation and model inference without requiring additional experimental data.

The paper identifies specific bacterial species associated with gut microbiome recovery after antibiotic use and shows their role in promoting microbial diversity and preventing pathogen colonization.

ConsensusDriver improves driver alteration prediction by combining multiple methods, increasing sensitivity by 20% across cancer types and individual patients.

INC-Seq improves nanopore sequencing accuracy for bacterial profiling using intramolecular-ligated consensus sequencing, achieving >97% median accuracy for 16S rRNA amplicons.