pgptracker 0.1.3

Integration of soil metagenomic data for correlation of microbial markers with plant biochemical indicators

PGPTracker: A Bioinformatics Pipeline for Functional Prediction and Analysis

PGPTracker is a command-line interface (CLI) tool designed to automate the complete workflow from 16S rRNA sequencing data to in-depth functional and statistical analysis.

It connects Amplicon Sequence Variants (ASVs) to predicted functions (KEGG Orthologs) and maps them to Plant Growth-Promoting Traits (PGPTs).

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Core Workflow

The pipeline is split into two main stages:

• Stage 1 (process): Handles data processing (QIIME 2, PICRUSt2) to generate unstratified (Function x Sample) and stratified (Taxon x Function x Sample) abundance tables.
• Stage 2 (analysis): Takes the tables from Stage 1 and performs normalization (CLR), statistical analysis (Kruskal-Wallis, PERMANOVA), machine learning (Random Forest, Lasso), and generates publication-quality visualizations (PCA, Heatmaps, Volcano Plots).

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Installation

PGPTracker is a pip-installable package that requires Conda to manage its bioinformatics dependencies (QIIME 2 and PICRUSt2).

Step 1: Create and Activate Base Environment

Create and activate a clean Conda environment (Python 3.10+ recommended).

conda create -n pgptracker python=3.13
conda activate pgptracker

Step 2: Install PGPTracker

Install the package and its core dependencies from PyPI.

pip install pgptracker

Step 3: Run Internal Setup (Mandatory)

This command is mandatory. It automatically creates and configures the separate qiime2 and picrust2 Conda environments that PGPTracker needs to run external tools.

pgptracker setup

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Quick Start: A Full Example

This example demonstrates the full process and subsequent analysis.

Note: You can also run the command pgptracker -i to enter the interactive mode, which is much more user-friendly.

Step 1: Run Stage 1 (process)

Process your raw sequence data (.qza, .fna, or .biom) into PGPT abundance tables. This example generates a table stratified by Genus.

pgptracker process \
    --rep-seqs path/to/dna-sequences.fasta \
    --feature-table path/to/feature-table.biom \
    -o my_project_output \
    --stratified \
    --tax-level Genus

This command will create the file my_project_output/genus_stratified_pgpt.tsv.

Step 2: Run Stage 2 (analysis)

Analyze the stratified output against your metadata to find which Genus/Function pairs differ by Treatment.

pgptracker analysis \
    -i my_project_output/genus_stratified_pgpt.tsv \
    -m path/to/my_metadata.tsv \
    -o my_project_output/analysis_by_treatment \
    --input-format long \
    --group-col Treatment \
    --target-col Treatment \
    --ml-type classification

This will create the analysis_by_treatment directory containing plots and machine learning results.

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Command Reference

Main Commands

Command	Description
pgptracker process	(Stage 1) Runs the full bioinformatics pipeline (QIIME2, PICRUSt2, PGPTs).
pgptracker analysis	(Stage 2) Runs statistical tests, ML, and plotting on a Stage 1 output table.
pgptracker setup	Installs and configures internal Conda environments. Must be run once after install.
pgptracker -i	Runs the tool in a guided, interactive menu-driven mode.

pgptracker process (Stage 1) Arguments

Argument	Description
--rep-seqs	Path to representative sequences (.qza or .fna).
--feature-table	Path to feature table (.qza or .biom).
-o, --output	Output directory to store results.
--stratified	Flag to generate stratified (Taxon x Function x Sample) output.
--tax-level	Taxonomic level for stratification (default: Genus).
--max-nsti	NSTI threshold for PICRUSt2 filtering (default: 1.7).
-t, --threads	Number of threads to use (default: auto-detect).
--classifier-qza	Path to a custom QIIME 2 classifier (default: Greengenes 2024.09).

pgptracker analysis (Stage 2) Arguments

Argument	Description
-i, --input-table	Path to the input table (output from process).
-m, --metadata	Path to the sample metadata file (TSV format).
-o, --output-dir	Directory to save analysis results.
--group-col	Metadata column for grouping in plots and statistics (e.g., 'Treatment').
--target-col	Metadata column to predict in machine learning (e.g., 'pH' or 'Treatment').
--ml-type	Type of ML task: classification or regression.
--input-format	Format of the input table: wide (unstratified) or long (stratified).
--no-stats	Flag to skip statistical tests (Kruskal-Wallis/Mann-Whitney).
--no-ml	Flag to skip machine learning models.

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Example Workflows (Stage 2 Analysis Cookbook)

A. Classification: Predict Environmental Biome

Question: "Can the functional profile distinguish between biomes (e.g., forest vs. desert)?"

pgptracker analysis \
    -i path/to/unstratified_pgpt_Lv3_abundances.tsv \
    -m path/to/emp_metadata.tsv \
    -o results/analysis_biome \
    --feature-col-name Lv3 \
    --group-col env_biome \
    --target-col env_biome \
    --ml-type classification

B. Regression: Correlate with Chemistry (pH)

Question: "Which bacterial functions (PGPTs) are most associated with soil pH?"

pgptracker analysis \
    -i path/to/unstratified_pgpt_Lv3_abundances.tsv \
    -m path/to/emp_metadata.tsv \
    -o results/analysis_ph \
    --feature-col-name Lv3 \
    --group-col env_feature \
    --target-col ph \
    --ml-type regression

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Outputs

PGPTracker generates publication-ready outputs in your results folder:

Directory	Content
normalization/	Raw and CLR-normalized abundance tables.
diversity/	Alpha Diversity plots (Shannon, Simpson), Beta Diversity plots (PCA, t-SNE), and PERMANOVA results.
statistics/	Differential Abundance results (Kruskal-Wallis), Volcano Plots, and Clustered Heatmaps.
machine_learning/	Feature Importance bar plots (Random Forest / Lasso) and Boruta selection results.

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Citing

PGPTracker is built upon the work of many others. Please cite the core tools and databases it uses:

PGPTracker & PLaBAse

• Atz, S., Rauh, M., Gautam, A., Huson, D.H. mgPGPT: Metagenomic analysis of plant growth-promoting traits. (submitted, 2024, preprint)
• Patz, S., Gautam, A., Becker, M., Ruppel, S., Rodríguez-Palenzuela, P., Huson, D.H. PLaBAse: A comprehensive web resource for analyzing the plant growth-promoting potential of plant-associated bacteria. (submitted 2021, preprint)

Core Dependencies

• QIIME 2: Bolyen E, Rideout JR, Dillon MR, et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37: 852–857.
• PICRUSt2: Douglas, G.M., Maffei, V.J., Zaneveld, J.R. et al. (2020). PICRUSt2 for prediction of metagenome functions. Nature Biotechnology 38, 685–688.
• Greengenes2: McDonald, D., et al. (2024). Greengenes2 unifies microbial data in a single reference tree. Nature Biotechnology.