Qiyun Zhu

TM-Vec 2s improves protein remote homology detection by being faster and more accurate than previous methods, enabling efficient large-scale database searches.

This paper introduces a parallel and scalable implementation of the balanced minimum evolution algorithm for large-scale phylogenetic inference, achieving significant speedup and memory efficiency.

The paper presents a Python implementation of ANCOM-BC for differential abundance testing, achieving 100x faster runtime and 87% less memory use compared to the R version.

The paper improves microbial phylogenetic analysis by selecting tailored marker genes from functional databases, enhancing tree accuracy across genomes and metagenomes.

PhyloMix improves microbiome-trait association prediction by generating synthetic samples using phylogenetic relationships, outperforming existing augmentation methods.

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

MIOSTONE models microbiome-trait associations using taxonomy-adaptive neural networks to predict and interpret microbial impacts on host traits.

BinaRena is a web-based tool that assists researchers in exploring and binning metagenomic contigs through interactive visualization and human-guided analysis.

The authors reanalyze their pan-cancer microbiome findings using decontaminated data to address concerns about contamination, batch effects, and machine learning approaches.

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

DEPP extends species trees using single genes by learning to place them without pre-specified models, enabling integration of 16S and metagenomic data.

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 APPLES-2, a distance-based phylogenetic placement method that is more accurate and scalable than existing methods, enabling efficient placement of microbial genomes on large trees.

This paper highlights challenges in benchmarking metagenomic profilers due to confusion between sequence abundance and taxonomic abundance.

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.

EMPress provides an interactive tool for visualizing phylogenetic trees alongside multi-omic data, enabling exploratory analyses of microbiome and metabolome datasets.

Dual patterned unique molecular identifiers (UMIs) turn Nanopore or PacBio CCS reads of ~4400 bp rRNA operons into single-molecule consensus sequences with 0.0049% (ONT) or 0.0006% (PacBio) mean error.

The paper introduces Qemistree, a method for analyzing metabolomics mass spectrometry data using hierarchical molecular fingerprints and chemical ontologies to explore chemical composition.

This paper expands the interpretability of human metabolomes by using food-based annotations, increasing the fraction of molecular features understood 7-fold.

This study evaluates shallow shotgun sequencing as an alternative to 16S for affordable, high-resolution human microbiome analysis, finding it provides accurate taxonomic and functional profiles.