TY  - JOUR
TI  - Reproducible Emu-Based Workflow for High-Fidelity Soil and Plant Microbiome Profiling on HPC Clusters
AU  - Dias, Henrique M.
AU  - Jain, Riya 
AU  - Santos, Vinicius A.
AU  - Gonzalez-Hernandez, Jose L.
AU  - Solanki, Shyam 
AU  - Menendez III, Hector M.
AU  - Graham, Christopher 
VL  - 16
IS  - 2
PY  - 2026
DA  - 2026/01/20
SP  - e5577
C1  - Bio-protocol 2026;16:e5577
DO  - 10.21769/BioProtoc.5577
UR  - https://doi.org/10.21769/BioProtoc.5577
AB  - Accurate profiling of soil and root-associated bacterial communities is essential for understanding ecosystem functions and improving sustainable agricultural practices. Here, a comprehensive, modular workflow is presented for the analysis of full-length 16S rRNA gene amplicons generated with Oxford Nanopore long-read sequencing. The protocol integrates four standardized steps: (i) quality assessment and filtering of raw reads with NanoPlot and NanoFilt, (ii) removal of plant organelle contamination using a curated Viridiplantae Kraken2 database, (iii) species-level taxonomic assignment with Emu, and (iv) downstream ecological analyses, including rarefaction, diversity metrics, and functional inference. Leveraging high-performance computing resources, the workflow enables parallel processing of large datasets, rigorous contamination control, and reproducible execution across environments. The pipeline’s efficiency is demonstrated on full-length 16S rRNA gene datasets from yellow pea rhizosphere and root samples, with high post-filter read retention and high-resolution community profiles. Automated SLURM scripts and detailed documentation are provided in a public GitHub repository (https://github.com/henrimdias/emu-microbiome-HPC; release v1.0.2, emu-pipeline-revised) and archived on Zenodo (DOI: 10.5281/zenodo.17764933).
KW  - Metabarcoding pipeline
KW  - Soil–plant-microbiome
KW  - 16S rRNA
KW  - Full-length amplicon
KW  - High-performance computing
KW  - Bioinformatics reproducibility
JF  - Bio-protocol
SN  - 2331-8325
PB  - Bio-protocol LLC.
BIO101  - False