{"title":"GRUMB:用于监测城市微生物组和诊断病原体风险的基因组解析宏基因组框架。","authors":"Suleiman Aminu, AbdulAziz Ascandari, Rachid Benhida, Rachid Daoud","doi":"10.1093/bioinformatics/btaf548","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>Urban infrastructure hosts dynamic microbial communities that complicate biosurveillance and AMR monitoring. Existing tools rarely combine genome-resolved reconstruction with ecological modeling and batch-aware analytics tailored to infrastructure-scale studies. We present GRUMB (Genome-Resolved Urban Microbiome Biosurveillance), an open-source, SLURM-compatible pipeline that reconstructs high-quality metagenome-assembled genomes (MAGs) from shotgun sequencing reads and integrates taxonomic/functional annotation (CARD, VFDB), batch-aware normalization, ecological diagnostics and machine learning classification of environment types with uncertainty and risk scoring. GRUMB accepts either SRA project accessions or paired-end FASTQ files with metadata, and produces assemblies, MAGs, taxonomic and functional profiles, ecological outputs and risk-informed classification. Its modular design enables reproducible, infrastructure-scale biosurveillance across diverse environments.</p><p><strong>Implementation and availability: </strong>.GRUMB is freely available under the MIT License at: https://github.com/SuleimanAminu/genome-resolved-urban-microbiome-biosurveillance; Zenodo DOI: https://doi.org/10.5281/zenodo.15505402. Requirements: Linux (Ubuntu 20.04+), Python 3.11, R 4.2+, SLURM. Issues and feature requests are tracked on GitHub.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GRUMB: A Genome-Resolved Metagenomic Framework for Monitoring Urban Microbiomes and Diagnosing Pathogen Risk.\",\"authors\":\"Suleiman Aminu, AbdulAziz Ascandari, Rachid Benhida, Rachid Daoud\",\"doi\":\"10.1093/bioinformatics/btaf548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Summary: </strong>Urban infrastructure hosts dynamic microbial communities that complicate biosurveillance and AMR monitoring. Existing tools rarely combine genome-resolved reconstruction with ecological modeling and batch-aware analytics tailored to infrastructure-scale studies. We present GRUMB (Genome-Resolved Urban Microbiome Biosurveillance), an open-source, SLURM-compatible pipeline that reconstructs high-quality metagenome-assembled genomes (MAGs) from shotgun sequencing reads and integrates taxonomic/functional annotation (CARD, VFDB), batch-aware normalization, ecological diagnostics and machine learning classification of environment types with uncertainty and risk scoring. GRUMB accepts either SRA project accessions or paired-end FASTQ files with metadata, and produces assemblies, MAGs, taxonomic and functional profiles, ecological outputs and risk-informed classification. Its modular design enables reproducible, infrastructure-scale biosurveillance across diverse environments.</p><p><strong>Implementation and availability: </strong>.GRUMB is freely available under the MIT License at: https://github.com/SuleimanAminu/genome-resolved-urban-microbiome-biosurveillance; Zenodo DOI: https://doi.org/10.5281/zenodo.15505402. Requirements: Linux (Ubuntu 20.04+), Python 3.11, R 4.2+, SLURM. Issues and feature requests are tracked on GitHub.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>\",\"PeriodicalId\":93899,\"journal\":{\"name\":\"Bioinformatics (Oxford, England)\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics (Oxford, England)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btaf548\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btaf548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
GRUMB: A Genome-Resolved Metagenomic Framework for Monitoring Urban Microbiomes and Diagnosing Pathogen Risk.
Summary: Urban infrastructure hosts dynamic microbial communities that complicate biosurveillance and AMR monitoring. Existing tools rarely combine genome-resolved reconstruction with ecological modeling and batch-aware analytics tailored to infrastructure-scale studies. We present GRUMB (Genome-Resolved Urban Microbiome Biosurveillance), an open-source, SLURM-compatible pipeline that reconstructs high-quality metagenome-assembled genomes (MAGs) from shotgun sequencing reads and integrates taxonomic/functional annotation (CARD, VFDB), batch-aware normalization, ecological diagnostics and machine learning classification of environment types with uncertainty and risk scoring. GRUMB accepts either SRA project accessions or paired-end FASTQ files with metadata, and produces assemblies, MAGs, taxonomic and functional profiles, ecological outputs and risk-informed classification. Its modular design enables reproducible, infrastructure-scale biosurveillance across diverse environments.
Implementation and availability: .GRUMB is freely available under the MIT License at: https://github.com/SuleimanAminu/genome-resolved-urban-microbiome-biosurveillance; Zenodo DOI: https://doi.org/10.5281/zenodo.15505402. Requirements: Linux (Ubuntu 20.04+), Python 3.11, R 4.2+, SLURM. Issues and feature requests are tracked on GitHub.
Supplementary information: Supplementary data are available at Bioinformatics online.