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Related Experiment Video

Updated: Jan 30, 2026

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere
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Reproducible Emu-Based Workflow for High-Fidelity Soil and Plant Microbiome Profiling on HPC Clusters.

Henrique M Dias1,2, Riya Jain1, Vinicius A Santos3

  • 1Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.

Bio-Protocol
|January 29, 2026
PubMed
Summary

This study presents a new workflow for analyzing soil and root bacterial communities using long-read sequencing. It enhances accuracy in profiling microbial ecosystems for sustainable agriculture.

Keywords:
16S rRNABioinformatics reproducibilityFull-length ampliconHigh-performance computingMetabarcoding pipelineSoil–plant-microbiome

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate profiling of soil and root-associated bacterial communities is crucial for understanding ecosystem functions.
  • Sustainable agricultural practices rely on insights from microbial community analysis.
  • Existing methods may lack comprehensive quality control and high-resolution taxonomic assignment for long-read sequencing data.

Purpose of the Study:

  • To present a comprehensive, modular workflow for analyzing full-length 16S rRNA gene amplicons from Oxford Nanopore sequencing.
  • To enable accurate and high-resolution profiling of bacterial communities in soil and root environments.
  • To provide a reproducible and scalable pipeline for microbiome research.

Main Methods:

  • Integration of NanoPlot and NanoFilt for quality assessment and filtering of raw reads.
  • Utilizing a curated Viridiplantae Kraken2 database for removal of plant organelle contamination.
  • Species-level taxonomic assignment using Emu, followed by downstream ecological analyses (rarefaction, diversity metrics, functional inference).
  • Leveraging high-performance computing for parallel processing and automated SLURM scripts.

Main Results:

  • The workflow demonstrated high post-filter read retention and high-resolution community profiles on yellow pea rhizosphere and root samples.
  • Rigorous contamination control and reproducible execution were achieved.
  • The pipeline enables scalable microbiome diversity and functional analyses.

Conclusions:

  • The presented workflow offers a robust and efficient solution for analyzing full-length 16S rRNA gene data from soil and root microbiomes.
  • This tool supports advancements in sustainable agriculture and ecosystem function research.
  • The modular design and provided automation scripts facilitate widespread adoption and reproducibility.