Aug 2026· Science Advances· Vol 12· 0 citations· 72 references
Medicine
TL;DR
FEDKEA, an enzyme annotation tool leveraging protein language models, and a user-friendly, FEDKEA-based metagenomic pipeline, MEnzMap, which encompasses the entire analysis workflow—from raw data quality control to function prediction and downstream analyses are designed.
Abstract
Metagenomic data have notable biological potential, but their functional interpretation is frequently impeded by incomplete protein function annotations. Accurate enzyme annotation is essential for elucidating the metabolic capabilities of microbial communities within metagenomic datasets. To address this challenge, we developed FEDKEA, an enzyme annotation tool leveraging protein language models, and provided a web platform for its use. In addition, we designed a user-friendly, FEDKEA-based metagenomic pipeline, MEnzMap, which encompasses the entire analysis workflow—from raw data quality control to function prediction and downstream analyses. Applying MEnzMap to human gut metagenomic data from the iHMP2 project, we generated a comprehensive enzyme profile landscape for both healthy individuals and patients with inflammatory bowel diseases. These tools provide an efficient method for the functional annotation of microbial dark matter and facilitate the identification of disease-associated enzymes.
This review presents a practical, workflow-oriented guide to microbiome data analysis, from raw DNA sequence processing to statistical interpretation and biological insight, and highlights emerging technologies, including machine learning methods that are beginning to reshape the field.
Jenna Poelzer, D. Wishart· Frontiers in Microbiology· 0 citations
GEOMeta provides a scalable resource and reproducible framework for metadata curation in the Gene Expression Omnibus, and benchmarked transcriptome representation models for predicting sex, age, tissue and disease from transcriptome embeddings.
Xiaodan Zhang, S. Paithankar, Jing Pu et al.· bioRxiv· 0 citations
WASP highlights how structural homology can systematically discover annotations missed by sequence-based approaches, predicting protein functions from AlphaFold structures using network-based structural homology and filling metabolic model gaps by mapping 75-100% of orphan reactions.
Abstract Motivation Functional characterization of microbiomes often relies on the sequencing of metagenomic DNA extracted from environmental samples, with current approaches using metagenome-assembled genomes (MAGs). Although glycoside hydrolases (GHs) are central to carbon cycling, accurate annotation of GHs in metag...
N. Griffin, Alison E Hughes, D. S. Erdody et al.· Bioinformatics Advances· 0 citations
ManiFasta is presented, a tool enabling users to generate standardized, reproducible, and robustly documented protein reference sets from diverse input sources and datatypes, and the value of maniFasta is highlighted in the context of salivary metaproteomics, addressing the need for a taxonomically comprehensive refere...
Christopher Handelmann, Ashley K. Miles, Yin-Yin Ye et al.· bioRxiv· 0 citations
This work optimized LC–MS/MS acquisition parameters for both DDA and DIA using a model microbiome, demonstrating how DIA enables increased sample throughput without compromising quantitative performance and establishing a scalable and cost-effective pipeline for metaproteomics of complex microbial communities.
Samantha Obermiller, Mary S. Lipton, P. Piehowski et al.· bioRxiv· 0 citations
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