Microbiology

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).

Plant roots associate with a wide diversity of bacteria and archaea across the root-soil spectrum. The rhizosphere microbiota, the communities of microbes in the soil adjacent to the root, can contain up to 10 billion bacterial cells per gram of soil (Raynaud and Nunan, 2014) and can play important roles for the fitness of the host plant. Subsets of the rhizospheric microbiota can colonize the root surface (rhizoplane) and the root interior (endosphere), forming an intimate relationship with the host plant. Compositional analysis of these communities is important to develop tools in order to manipulate root-associated microbiota for increased crop productivity. Due to the reduced cost and increasing throughput of next-generation sequencing, major advances in deciphering these communities have recently been achieved, mainly through the use of amplicon sequencing of the 16S rRNA gene. Here we first present a protocol for dissecting the microbiota from various root compartments, developed using rice as a model. We next present a method for amplifying fragments of the 16S rRNA gene using a dual index approach. Finally, we present a simple workflow for analyzing the resulting sequencing data to make ecological inferences.

Next-generation sequencing (NGS) offers unparalleled resolution for untargeted organism detection and characterization. However, the majority of NGS analysis programs require users to be proficient in programming and command-line interfaces. EDGE bioinformatics was developed to offer scientists with little to no bioinformatics expertise a point-and-click platform for analyzing sequencing data in a rapid and reproducible manner. EDGE (Empowering the Development of Genomics Expertise) v1.0 released in January 2017, is an intuitive web-based bioinformatics platform engineered for the analysis of microbial and metagenomic NGS-based data (Li et al., 2017). The EDGE bioinformatics suite combines vetted publicly available tools, and tracks settings to ensure reliable and reproducible analysis workflows. To execute the EDGE workflow, only raw sequencing reads and a project ID are necessary. Users can access in-house data, or run analyses on samples deposited in Sequence Read Archive. Default settings offer a robust first-glance and are often sufficient for novice users. All analyses are modular; users can easily turn workflows on/off, and modify parameters to cater to project needs. Results are compiled and available for download in a PDF-formatted report containing publication quality figures. We caution that interpreting results still requires in-depth scientific understanding, however report visuals are often informative, even to novice users.