OrochiPipeline

Orochi is a Snakemake-based metagenomics pipeline designed for comprehensive analysis of metagenomic data, including preprocessing, assembly, binning, taxonomic and functional annotation, and Biosynthetic Gene Cluster (BGC) prediction.

The Orochi project is a collaboration between the Microbial Ecology group at the Netherlands Institute of Ecology (NIOO-KNAW) and the Bioinformatics Group at Wageningen University & Research (WUR).

Overview

The Orochi pipeline integrates several state-of-the-art bioinformatics tools to process raw metagenomic reads and produce high-quality insights into microbial communities. It supports both single-sample assembly and co-assembly approaches.

Key Features:

• Preprocessing: Quality control with fastp and host/contaminant removal with bbmap.
• Assembly: Supports MEGAHIT (for co-assembly) and SPAdes (for single-sample assembly).
• Binning: Multi-tool binning using MetaBAT2 and MaxBin2, refined with DAS Tool and dereplicated with dRep.
• Annotation:
  • Taxonomic profiling with MetaPhlAn4 and CAT.
  • Functional annotation with eggNOG-mapper.
  • Eukaryotic gene prediction with Augustus.
• BGC Prediction: Identification of secondary metabolite clusters using antiSMASH.
• MAG Linkage: Linking MAGs using markerMAG.
• Reporting: Interactive HTML reports and visualizations.

Requirements

• Conda or Mamba (recommended)
• Snakemake (>= 7.0.0)
• Python (>= 3.8)
• R (for reporting)

Setup

1. Clone the repository:

   git clone https://github.com/your-repo/orochi_nioo.git
   cd orochi_nioo

2. Install Snakemake and Mamba: It is highly recommended to use Mamba for environment management.

   conda install -c conda-forge mamba
   mamba create -c conda-forge -c bioconda -n snakemake snakemake

3. Configure Databases: The pipeline requires several external databases. Update the paths in config/configfile.yaml:

   • phyloflash_db
   • emapper_database
   • antismash_db
   • CAT_database & CAT_taxonomy
   • checkm_db

   For antiSMASH, you can set download_antismash_db: true to let the pipeline handle the installation.

Usage

1. Prepare the Sample Sheet: Edit config/samples.tsv. It should be a tab-separated file with the following columns:

   • sample: Unique sample identifier.
   • treatment1, treatment2: Metadata columns.
   • fq1, fq2: Paths to raw paired-end FastQ files.
   • sample_pool: Grouping for co-assembly.

2. Configure the Pipeline: Edit config/configfile.yaml to set your output directory, assembly method, and tool parameters.

3. Run the Pipeline: Activate your Snakemake environment and run:

   snakemake --use-conda --cores <number_of_threads>

Project Structure

.
├── config/                 # Configuration files (configfile.yaml, samples.tsv)
├── resources/              # Reference genomes, contaminant sequences, and static data
├── workflow/
│   ├── envs/               # Conda environment definitions (.yaml)
│   ├── rules/              # Snakemake rule modules (.smk)
│   ├── scripts/            # Custom Python, R, and Bash scripts
│   └── Snakefile           # Main workflow entry point
├── README.md               # This file
└── LICENSE.md              # Project license

Scripts and Workflow Modules

Workflow Rules (workflow/rules/)

• preprocessing.smk: QC and filtering.
• coassembly.smk / single_sample_assembly.smk: Assembly logic.
• binning.smk: Binning, refinement, and dereplication.
• prokaryote-gene_prediction_and_annotation.smk: Taxonomic and functional profiling.
• BGC_prediction.smk: antiSMASH analysis.
• plots.smk: Report generation and visualization.

Custom Scripts (workflow/scripts/)

• render_report.R: Renders the final HTML report.
• augustify.py: Wrapper for Augustus eukaryotic gene prediction.
• summarize_antismash.py: Aggregates antiSMASH results.
• regenerate_antismash_html.py: Utility for antiSMASH report formatting.

Environment Variables

The pipeline primarily relies on the configfile.yaml for configuration. Ensure that any system-specific paths are correctly set there.