MetaCAT outperforms existing methods in both clustering accuracy and computational efficiency across diverse datasets and provides a scalable framework for microbial community profiling and advances the understanding of host-microbe interactions.
This broad evaluation demonstrated that Metax resolved bacterial and viral signatures of peri-implantitis in oral microbiomes and revealed signals suggestive of reagent-borne contaminants and reference misassemblies in plasma-cell-free DNA.
Zhi-Luo Deng, Nasim Safaei, A. McHardy· Cell· 0 citations
This work presents a novel, integrative framework that connects strategic omics and sequencing technologies to combine sophisticated metagenomics studies with customized next-generation sequencing techniques to lay the groundwork for next-generation ecological predictions and precise microbiome engineering with proper...
P. Kumari, Gaurav Sharma, M. Gupta et al.· Chemical biology letters· 0 citations
ABSTRACT Functional profiling of meta-omics is essential for understanding microbial communities, yet support for custom genome-resolved reference databases is limited. We introduce Leviathan for integrated taxonomic and functional profiling at both genome and pangenome resolution. Leviathan combines Sylph for ultrafas...
Josh L. Espinoza, Allan J. Phillips, C. Dupont· mSystems· 0 citations
Metaproteomic analyses commonly rely on protein sequence databases, yet database construction remains one of the most variable steps in metaproteomic workflows. Here, I present MetaproDB, a flexible and reproducible framework for biome-informed protein-sequence-database construction in metaproteomics. MetaproDB integ...
M. Arıkan· Journal of Proteome Research· 0 citations
Public sequencing repositories contain large amounts of gut microbiome data that could support cross-study comparison, reproducibility analysis, and microbiome foundation model development. However, the extent to which these data are structured, harmonized, and reusable at archive scale remains unclear. Here, we charac...
Kiersten Oderman, T. Nandi, J. Malas et al.· bioRxiv· 0 citations
This Review presents microbiome foundation models as a hierarchy spanning biological scales, and proposes a transition from static representation learning toward intervention‐aware microbiome world models capable of simulation, digital twinning, and generative microbiome engineering.
Hao-Hong Zhang, Zi-Xin Kang, K. Ning· Advanced genetics· 0 citations
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