Reproducible Emu-Based Workflow for High-Fidelity Soil and Plant Microbiome Profiling on HPC Clusters

Software and datasets

The complete set of databases, software tools, and custom scripts required to reproduce this workflow is listed in Table 1, along with version numbers, access information, and licensing details. All custom scripts are available in the public GitHub repository and archived on Zenodo (DOI: 10.5281/zenodo.17764933; release v1.0.2).

Table 1. Databases, software, and custom scripts required to run the full Emu-based 16S microbiome workflow

Procedure

Part I. Before you begin

Before initiating the four steps, it is advisable to organize the computational environment, define analysis parameters, and outline the overall workflow (see GitHub repository: https://github.com/henrimdias/emu-microbiome-HPC). The graphical overview presents a flowchart of the Emu pipeline, tracing the path from demultiplexed raw Oxford Nanopore FASTQ files through quality control, organelle filtering, expectation-maximization taxonomic assignment, and downstream ecological analyses. The following sections detail the principal considerations and options required for smooth and reproducible execution of the protocol.

Note: This protocol focuses exclusively on the computational analysis of Oxford Nanopore 16S reads and begins at the stage of base-called, demultiplexed FASTQ files. Procedures for base calling and demultiplexing (e.g., using Dorado, https://github.com/nanoporetech/dorado), as well as wet-lab steps for DNA extraction, library preparation, and sequencing, are outside the scope of this protocol.

A. Set up a directory structure

1. Create the project in a high-performance working space (often named scratch or work) for large, input/output-heavy runs, and keep code, configs, and finalized outputs in persistent project storage (often named project, groups, or shared). The layout may be created manually or, preferably, cloned from the GitHub template with paths adjusted as needed.

git clone --depth 1 --filter=blob:none --sparse https://github.com/henrimdias/emu-microbiome-HPC.git

cd emu-microbiome-HPC

git sparse-checkout set emu_pipeline

Note: Retention and backup policies for the working space vary by cluster; check the cluster site docs.

B. Create and manage Conda environments

1. Maintain separate environments (nanotools_env, preemu_env, emu_env, faprotax_env) to avoid software conflicts.

2. Create each environment from YAML files provided in the repository (on the HPC login node).

nanotools_env:

cd yml_files

conda env create -f  nanotools_env.yml

conda activate nanotools_env

preemu_env:

cd yml_files

conda env create -f preemu_env.yml

conda activate preemu_env

emu_env:

cd yml_files

conda env create -f emu_env.yml

conda activate emu_env

faprotax_env:

cd yml_files

conda env create -f faprotax_env.yml

conda activate faprotax_env

Note: YAML files ensure convenience and reproducibility. Alternatively, each protocol step also includes a one-liner for installing only the required tools without using YAMLs. Both approaches are valid.

Note: If Miniconda is not available on the cluster, install a local copy in your home directory and use it in all subsequent SLURM jobs. For example, on the login node:

cd $HOME

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda3

source $HOME/miniconda3/etc/profile.d/conda.sh

conda init bash   # optional

After installation, make sure all SLURM scripts load this installation, e.g.:

source $HOME/miniconda3/etc/profile.d/conda.sh

conda activate <env_name> 

C. Configure SLURM array jobs

Configure these settings in all SLURM files for each module, as illustrated in step A3 from Part II.

1. Use array indexing (e.g., --array=1-N) to parallelize per-sample steps.

2. Adjust memory (e.g., --mem=4G-24G) and CPU cores (e.g., --cpus-per-task=1-8) according to the demands of each tool (see Part I, section E).

3. Choose the recommended partition following the cluster policies (e.g., --partition=compute).

4. Change the directories following the recommended paths.

5. If Miniconda is not available, install it (e.g., in $HOME/miniconda3) and use this path as the source directory in all SLURM job scripts.

D. Reference databases

1. Select a reference database and fix its version to ensure stable and reproducible taxonomic assignments.

2. Record the database name, version, download date, source/DOI, and checksum.

3. By default, run the protocol with the Emu prebuilt database (OSF project 56uf7).

4. When customization is necessary, rebuild an Emu database from a pinned NCBI taxonomy following the Emu documentation: https://github.com/treangenlab/emu.

Note: A summary of widely used 16S rRNA gene reference databases, their most recent releases, and Emu-specific considerations to guide selection and version pinning is available in Table 2.

Caution: As new releases add sequences or rename taxa, identical reads may be classified differently. Version pinning avoids inconsistencies across runs.

Table 2. Common 16S rRNA gene reference options for Emu workflows

E. Hardware preparation

The amount of RAM required depends on the dataset size and the complexity of the analysis. For the workflow described, which involves moderately complex steps, 16 GB of RAM is sufficient (Table 3).

Table 3. Recommended hardware and scheduler settings for each module of the workflow

Note: Partition names (e.g., --partition=moderate) are cluster-specific and may differ across HPC systems. The settings shown here correspond to the compute infrastructure of the SDSU HPC and should be adapted to the partitions and resource limits available on other clusters.

For reference, check https://help.sdstate.edu/TDClient/2744/Portal/KB/ArticleDet?ID=159685.

F. Controls

Controls must span the entire workflow, not only the experimental design. Include extraction blanks and no-template PCR controls and process them through filtering, organelle depletion, taxonomic assignment, and downstream analyses; use at least one positive control (mock community) to benchmark length/quality thresholds and expected taxonomic recovery. Use blanks to identify contaminant taxa (e.g., blank-prevalent features) and to set acceptance ranges; report control outcomes with the main results and retain raw control tables for auditing. These practices are essential for low-biomass contexts and to avoid reagent/kit contaminants driving patterns (for a review, see [24]).

Part II. Preprocessing full-length 16S rRNA reads

This workflow performs automated quality control and filtering of full-length 16S rRNA Nanopore reads using a single SLURM pipeline that integrates NanoPlot, NanoFilt, and QC summary consolidation. The unified design minimizes manual editing by requiring only one configuration update at the top of the SLURM file before execution.

Timing: ~30 min (based on 18 .fastq files ranging between 150 and 220 MB).

Minimum disk space: ~8 GB.

A. Launch the unified nanotools pipeline

1. Ensure that all base-called, demultiplexed FASTQ files are placed in the raw-reads directory (e.g., emu_pipeline/raw_data) and follow a consistent naming pattern, such as barcodeXX.fastq.gz (for example, barcode01.fastq.gz, barcode02.fastq.gz) or user-defined sampleID.fastq.gz names that match the sample identifiers in the metadata table. The SLURM array scripts parse these filenames to derive sample IDs and map them to metadata.

2. Activate the Conda environment.

conda activate nanotools_env

3. Open the SLURM file run_nanotools_pipeline.slurm and edit only the configuration section at the top of the file. Specify desired directories for raw reads, filtered reads, QC reports, and summary outputs:

# Example configuration block

RAW_DIR="/scratch/project/raw"

FILTERED_DIR="/scratch/project/filtered"

QC_DIR="/scratch/project/qc"

SUMMARY_DIR="/scratch/project/summary"

SCRIPT_QC_SUMMARY="/home/user/scripts/run_QC_summary.py"

# Filtering parameters

MIN_LEN=1000

MAX_LEN=1850

MIN_QUAL=10

Note: The script automatically detects files with extensions .fastq or .fastq.gz. There is no need to modify the file search pattern manually.

4. Submit the SLURM job to run the entire workflow.

sbatch run_nanotools_pipeline.slurm

Note: This single command executes the following steps sequentially:

(i) NanoPlot: generates per-sample quality control (QC) reports for raw reads.

(ii) NanoFilt: filter reads by quality and length thresholds.

(iii) NanoPlot (filtered): regenerates QC reports after filtering.

(iv) QC summary: consolidates NanoStat metrics across samples and stages.

B. Filtering logic and parameters

Reads are filtered to 1,000–1,850 bp (Q ≥ 10) to enrich for full-length 16S rRNA gene amplicons (~1,450–1,600 bp) and exclude truncated (≤1,000 bp) and concatemeric or anomalously long (≥1,850 bp) reads, improving classification accuracy and comparability [25].

Expected outputs:

(i) Filtered FASTQ files (*_filtered.fastq.gz)

(ii) NanoPlot QC directories (/nanoplot_reports/raw and /nanoplot_reports/filtered)

(iii) Intermediate NanoStat summaries

C. Consolidating and visualizing QC reports

After the pipeline completes, the QC summary script automatically collects all NanoStat reports (raw and filtered) and merges them into an integrated summary.

If needed, the script can be run independently:

python run_QC_summary.py –-inpunt /put_the_directory –output /put_the_directory

Expected outputs:

(i) sequencing_summary.csv: Tables of all key NanoStat metrics (mean read length, quality, read count, N50, etc.).

(ii) QC_summary_plots.pdf: Multi-page PDF showing side-by-side comparisons (raw vs. filtered) per sample.

D. Quality verification and acceptance criteria

1. Confirm that read-length N50 values fall within the expected amplicon range (1,450–1,550 bp).

2. Ensure that read retention after filtering is between 60% and 90%.

3. Examine QC plots to confirm that the filtering step reduces truncated reads without excessive loss of valid reads.

Note: If results deviate (e.g., severe loss or unexpected quality shifts), adjust the parameters (MIN_LEN, MAX_LEN, or MIN_QUAL) in the configuration section of run_nanotools_pipeline.slurm and resubmit the job. See Table 2 for troubleshooting guidance if acceptance criteria are not met.

Caution: After filtering, the QC reports should show a controlled reduction in total read count and a reshaped quality/length distribution that reflects the chosen thresholds. If results deviate (e.g., abrupt read loss), adjust the parameters in run_nanofilt_barcodes.slurm.

Part III. Organelle contamination removal

Detect and remove organelle (chloroplast and mitochondrial) contamination from full-length Nanopore 16S rRNA reads. This step preserves accurate bacterial and archaeal community profiles for downstream Emu-based taxonomic analysis by eliminating host-derived organelle sequences. Reads are classified with Kraken2 [26] against a curated organelle reference database [27], and organelle-assigned reads are discarded. The result is a high-confidence, organelle-free read set for Emu, improving accuracy and reproducibility in plant and soil microbiome studies.

Timing: ~30–60 min (based on 18 .fastq files ranging between 150 and 220 MB).

Minimum disk space: ~12 GB.

A. Download curated RefSeq organelle genomes

1. Download curated chloroplast and mitochondrial FASTA files to construct the Kraken2 database. Run the organelle genome download script. Update and check pathways on the script before running the code.

bash download_plant_organelle_RefSeq_fastas.sh

2. Verify downloaded and decompressed files.

ls -lh /$USER/scratch/emu_pipeline/kraken_db/

Note: These files are multi-genome FASTAs curated by NCBI containing a wide range of plastid and mitochondrial sequences from green plants (May 2025 release).

B. Build the organelle database with Kraken2

1. Activate the Conda environment.

conda activate preemu_env

2. Download taxonomy files.

kraken2-build --download-taxonomy –-db \/$USER/scratch/kraken_env/db/kraken_organelle_db

3. Build the database with curated references using SLURM. Construct a Kraken2 organelle database from the curated RefSeq chloroplast and mitochondrial FASTA files downloaded in the previous step.

sbatch kraken2_add_build_db.slurm

Expected database files: hash.k2d; opts.k2d; taxo.k2d; seqid2taxid.map.

C. Identify and filter organelle contamination

1. Use this database to classify reads and remove organelle-derived sequences from environmental full-length 16S rRNA gene datasets. The provided script applies Kraken2 using the default nucleotide parameters (k-mer length k = 35, minimizer length ℓ = 31, minimizer spaces s = 7), with a conservative confidence threshold of --confidence 0.6 [28]. Run the provided SLURM script:

sbatch kraken2_classify_filter.slurm

2. Summarize contamination levels.

python summarize_kraken2_reports.py \

  --input_dir /path/to/reports/ \

  --output_file /path/to/output/kraken_contamination_summary.tsv

Expected outputs: Per-sample classification outputs: *.kraken, *.report.txt; Organelle-depleted reads: *.fastq.gz; kraken_contamination_summary.tsv.

3. After depletion, confirm that bacterial reads dominate root libraries [29]. Check that organelle contamination does not exceed 15%–20% of classified reads.

4. Verify that the reads loss after filtering does not exceed 30%. Check code description in:

remove_kraken2_organelle_reads.py.

5. Confirm that database provenance checks complete without errors, checking that report files are not empty or abnormal.

Note: Empty or abnormal report.txt files, unusually high organelle reads, or sharp bacterial losses (>30%) indicate potential database mismatch, over-filtering, or contamination. See Table 6 (Troubleshooting) for likely causes and corrective actions.

Part IV. Taxonomic assignment using Emu

Timing: ~30–60 min (based on 18 .fastq files ranging between 150 and 220 MB).

Minimum disk space: ~2 GB.

A. Prepare the Emu database

1. Download the prebuilt Emu database from OSF (https://osf.io/56uf7/) and set the directory path.

Note: The prebuilt Emu database requires approximately 90 MB of disk space.

2. Alternatively, build a custom database following the Emu GitHub instructions (https://github.com/treangenlab/emu; see Table 1) [30].

B. Run Emu with SLURM batch arrays

1. Inside the run_emu.slurm script, each array task calls Emu using a template command such as:

# -----------------------------------

# 6. Run EMU abundance profiling

# -----------------------------------

echo "Running EMU on: $FASTA_FILE"

emu abundance \

  --db "$EMU_DB_DIR" \

  --output-dir "$BARCODE_OUTPUT" \

  --keep-counts \

  --keep-read-assignments \

  "$FASTA_FILE"

Note: Here, EMU_DB_DIR points to the prebuilt Emu database (see Part IV, section A), FASTA_FILE is the organelle-depleted full-length 16S rRNA gene file for a given barcode, and BARCODE_OUTPUT is a per-sample directory where Emu writes relative abundance tables (*_rel-abundance.tsv) and read assignment distributions (*_read-assignment-distributions.tsv). It does not apply an additional minimum read-length filter inside Emu because length filtering to 1,000–1,850 bp (Q ≥ 10) is already enforced upstream with NanoFilt.

2. Activate the Conda environment.

conda activate emu_env

3. Before running the job, confirm that the input directory contains organelle-depleted FASTQ files from the previous step, the Emu database is downloaded and indexed, and all directory paths in the SLURM script match the HPC environment.

4. Submit the SLURM job script:

sbatch run_emu.slurm

5. Monitor the job array to confirm that each sample (barcode) runs as an independent task.

6. Typically, >95% of mapped reads should be assigned to a bacteria taxon.

Expected outputs: *_rel-abundance.tsv (taxa-level relative abundances); *_read-assignment-distributions.tsv (read-level mapping details).

C. Combine per-sample taxonomic tables

1. Merge all per-sample outputs into a single wide-format abundance table using the combine-outputs command:

emu combine-outputs <directory_path> <rank>   

Expected outputs: Combined abundance table (e.g., species_rel-abundance.tsv)

Note: This function generates a single table that contains the relative abundances of all Emu output files within a specified directory. It automatically selects all .tsv files in the directory that contain “rel-abundance” in their filename. It is also possible to combine tables at a specific taxonomic rank by specifying one of the following: tax_id, species, genus, family, order, class, phylum, or superkingdom. For more details, visit the GitHub repository: https://github.com/treangenlab/emu.

Caution: Regarding Emu, the quality of taxonomic assignment is evaluated based on the proportion of classified reads and the performance of controls. For example, in complex soils, low read assignment may indicate high error rates or gaps in the database rather than true biological absence [31]. If Emu fails to complete, expected output tables are missing, assignment rates are unexpectedly low at the genus or species level, array indexing skips or mislabels samples, or database paths/provenance are incorrect, consult the troubleshooting guidance for likely causes and corrective actions (see Table 6).

D. Aggregate Emu read-count statistics

1. After Emu completes, collect .out files generated per barcode (e.g., barcode01.out) that report three fields: Unmapped_Read_Count, Mapped_Read_Count, and Unclassified_Mapped_Read_Count. Place all .out files in a single directory.

2. Aggregate these counts across barcodes into one table using the provided script:

python collect_counts.py /path/to/directory

3. Convert relative abundances into estimated raw read counts per barcode using the following script:

python relab_to_counts.py summary_counts.tsv relative_abundance.tsv raw_counts.tsv --strict-sum

Note: Diversity indices that require integer counts (e.g., Chao1, rarefaction, DESeq2) must use these derived counts, not proportions.

Part V. Microbiome downstream analyses

Timing: ~60–120 min (based on 18 samples).

Minimum disk space: ~4 GB.

In the final stage of the workflow, downstream microbial community analysis is carried out in R, drawing on established ecological tools such as the vegan package [32]. Abundance tables and metadata provide the foundation for exploring sequencing depth, diversity, and community structure. Analyses typically include rarefaction curves, alpha diversity indices (e.g., Shannon, Simpson, Pielou’s evenness, Chao1), and beta diversity ordinations such as PCoA on Bray–Curtis dissimilarities. Differential abundance analysis can be performed with DESeq2 [33] using its internal size-factor normalization and false discovery rate (FDR)-adjusted P-values to identify taxa that change significantly across experimental groups. These are complemented by visualizations of taxonomic composition across selected ranks, while functional insights can be inferred with FAPROTAX [34]. Together, these approaches offer a flexible framework that can be readily adapted to a wide range of experimental designs. Table 4 summarizes the R version and core packages, together with their version numbers and roles in the workflow.

Table 4. R environment and packages used for microbiome downstream analyses

Note: The downstream R script expects two main input tables:

(i) Metadata table (metadata.tsv): tab-delimited, with one row per sample and at least the following columns:

• SampleID (unique sample identifier; must match column names in the count table).

• Group (experimental group or treatment).

(ii) Count table (counts.tsv): tab-delimited matrix with the following:

• One row per taxon (e.g., species or genus).

• One column per sample (SampleID).

• An initial column with Taxon (or Species) storing the taxonomic label.

(iii) Relative abundance table (relative_counts.tsv): same structure as the count table, but with remaining cells containing relative abundances (e.g., output from Emu pipeline) instead of raw counts.

An example dataset (example_metadata.tsv, example_counts.tsv) that satisfies these requirements is provided in the GitHub repository (under emu_pipeline/example_data/) and in the Zenodo archive. Users can run the full downstream script on this example dataset to verify that their installation and configuration are working before analyzing their own data.

A. Run the downstream analysis workflow in R

1. Download downstream_microbiome.R from the repository.

2. Open and edit the configuration block at the top of the script: set metadata_path, raw_counts_path, rel_abund_path, and EXPORT_DIR. Confirm sample_id_col, group_col, and tax_level match the metadata table.

# ============================================================

# 1) User configuration (edit for a given dataset)

# ============================================================

# Path to metadata table (one row per sample)

metadata_path   <- "metadata.tsv"

# Path to raw count table (features in rows, samples in columns)

raw_counts_path <- "raw_counts.tsv"

# Path to relative abundance table

rel_abund_path  <- "raw_counts.tsv"

# Column name in metadata containing sample IDs

sample_id_col   <- "SampleID"

# Column name used as the main grouping factor (for colors / statistics)

group_col       <- "Group"

# Target taxonomic rank for composition plots (case-insensitive).

# Typical choices: "kingdom", "phylum", "class", "order", "family", "genus", "species".

tax_level       <- "genus"

# Number of most abundant taxa to display explicitly in composition plots

topN_taxa       <- 16

# Number of interpolation steps for rarefaction curves

raref_steps     <- 100

# Output directory for all plots and tables

EXPORT_DIR <- "downstream_microbiome_output"

dir.create(EXPORT_DIR, showWarnings = FALSE, recursive = TRUE)

# Optional: preprocessing for alpha diversity (filters / rarefaction on raw counts)

filter_for_alpha <- FALSE   # if TRUE, apply filters below (must be used for Chaos1)

min_lib_alpha    <- 1000    # minimum reads per sample for alpha analyses

min_prevalence   <- 2       # minimum number of samples in which a feature must be present

min_total_count  <- 10      # minimum total counts per feature (across all samples)

rarefy_for_alpha <- FALSE   # if TRUE, perform rarefaction for alpha analyses

rare_depth_alpha <- NA      # if NA, uses minimum library size after filtering

# Optional: minimum library size for inclusion in beta diversity

min_lib_beta     <- 1       # 1 removes only zero-library samples

3. Run the script locally in RStudio or from the command line:

Rscript downstream_microbiome.R

Expected outputs: PNG/SVG figures (rarefaction, composition bar charts, alpha diversity, PCoA/NMDS) and CSV tables (alpha metrics, PERMANOVA/betadisper results, DESeq2 outputs).

Note: The script is fully commented to support customization and guides users through a complete downstream workflow, including rarefaction curves for sequencing depth assessment, taxonomic composition plots at chosen ranks, alpha diversity metrics (S_obs, Chao1, Shannon, Simpson, Pielou’s evenness), beta diversity ordinations (Bray, Jaccard, Aitchison with PCoA/NMDS), PERMANOVA and dispersion testing (betadisper), and differential abundance analysis with DESeq2 using size-factor normalization and FDR-adjusted results.

B. Assign putative functions with FAPROTAX

1. Download the FAPROTAX database (v1.2.12, last updated May 2025; [34]) from: http://www.loucalab.com/archive/FAPROTAX/lib/php/index.php?section=Download

2. Activate the Conda environment.

conda activate faprotax_env

3. Prepare the input table with taxonomy, sample IDs, and counts/abundances. Convert Emu outputs using:

python emu-to-faprotax.py relative_abundance.tsv faprotax_relative_abundance.tsv

4. Run FAPROTAX in tabular mode to generate function-by-sample profiles:

python collapse_table.py \

-i faprotax_relative_abundance.tsv \

-o func_faprotax_relative_abundance.tsv \

-g FAPROTAX.txt -d "taxonomy" -c "#" -v

Expected outputs: func_faprotax_relative_abundance.tsv (function-by-sample matrix).

Note: FAPROTAX accuracy depends on the quality of taxonomic assignments. Misclassifications at the genus/species level may propagate into functional predictions.

Validation of protocol

This protocol has been used and validated in Dias et al. [35]. The study applied the workflow to full-length 16S rRNA Nanopore datasets from seasonal yellow pea varieties, demonstrating organelle read removal, Emu-based taxonomic assignment, and downstream ecological analyses. Detailed results are presented in Figure 2 (alpha diversity metrics) and Figure 4 (community composition profiles) of [35].

General notes and troubleshooting

Before inspecting module-specific issues, it is recommended that users perform a few high-level checks on read retention, organelle removal, taxonomic assignment rates, and diversity-ready sampling depth. Table 5 summarizes indicative sanity-check thresholds based on the validation dataset.

Table 5. Quick sanity-check thresholds across the workflow

Common problems, their likely causes, and practical solutions are summarized in Table 6. This table covers issues that arise across the pipeline, including key references.

Table 6. Common pitfalls in microbiome analysis: quick diagnostics and solutions

Supplementary information

Supplementary materials are available with the online version of this article and in the associated GitHub repository (https://github.com/henrimdias/emu-microbiome-HPC). Supplementary data have been deposited in Zenodo (DOI: 10.5281/zenodo.17195104).

Acknowledgments

Authors’ contribution

Conceptualization, H.M.D. and C.G.; Investigation, H.M.D., R.J., and C.G.; Writing—Original Draft, H.M.D.; Writing—Review & Editing, H.M.D., R.J., V.A.S., J.L.G.H., S.S., H.M.M., and C.G.; Funding acquisition, S.S. and C.G.; Supervision, C.G.

Startup funding to the S.S. lab from the Agricultural Experiment Station (AES), SDSU. We thank Mehmet Karakaya for his unwavering support during late-night work sessions, generously providing food, humor, and care throughout the development of this protocol.

This protocol has been used and validated in Dias et al. [35].

Competing interests

The authors declare no conflict of interest.

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