Gene Catalog

Overview

This module is designed to function as both a standalone gene catalog pipeline as well as a component of the larger CAMP metagenome analysis pipeline. As such, it is both self-contained (ex. instructions included for the setup of a versioned environment, etc.), and seamlessly compatible with other CAMP modules (ex. ingests and spawns standardized input/output config files, etc.).

This module generates and functionally annotates a gene catalog from assembled contigs. It is both self-contained (ex. instructions included for the setup of a versioned environment, etc.), and compatible with other CAMP modules (ex. ingests and spawns standardized input/output config files, etc.).

Installation

Tip

All databases used in CAMP modules will also be available for download on Zenodo (link TBD).

Install conda

If you don't already have conda handy, we recommend installing miniforge, which is a minimal conda installer that, by default, installs packages from open-source community-driven channels such as conda-forge.

# If you don't already have conda on your system...
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh

Run the following command to initialize Conda for your shell. This will configure your shell to recognize conda activate.

conda init

Restart your terminal or run:

source ~/.bashrc  # For bash users
source ~/.zshrc   # For zsh users

Setting up the Gene Cataloguing Module

1. Clone repo from Github.

git clone https://github.com/Meta-CAMP/camp_gene-catalog

2. Set up the rest of the module interactively by running setup.sh. This will install the necessary conda environments (if they have not been installed already) and generate parameters.yaml as well as set up the paths in test_data/samples.csv for testing.

cd camp_gene-catalog/
source setup.sh

# If you encounter issues where conda activate is not recognized, follow these steps to properly initialize Conda
conda init
source ~/.bashrc # or source ~/.zshrc

3. Make sure the installed pipeline works correctly. With 10 threads and a maximum of 40 GB allocated, the test dataset should finish in approximately 80 minutes.

# Run tests on the included sample dataset
conda activate camp
python /path/to/camp_gene-catalog/workflow/gene-catalog.py test

Using the Module

Input: /path/to/samples.csv provided by the user.

Output: TODO

Module Structure

└── workflow
    ├── Snakefile
    ├── gene-catalog.py
    ├── utils.py
    ├── __init__.py
    └── ext/
        └── scripts/

• workflow/gene-catalog.py: Click-based CLI that wraps the snakemake and other commands for clean management of parameters, resources, and environment variables.
• workflow/Snakefile: The snakemake pipeline.
• workflow/utils.py: Sample ingestion and work directory setup functions, and other utility functions used in the pipeline and the CLI.
• ext/: External programs, scripts, and small auxiliary files that are not conda-compatible but used in the workflow.

Running the Workflow

1. Make your own samples.csv based on the template in configs/samples.csv. Sample test data can be found in test_data/.

   • samples.csv requires either absolute paths or paths relative to the directory that the module is being run in

2. Update the relevant parameters in configs/parameters.yaml.

3. Update the computational resources available to the pipeline in configs/resources.yaml.

Command Line Deployment

To run CAMP on the command line, use the following, where /path/to/work/dir is replaced with the absolute path of your chosen working directory, and /path/to/samples.csv is replaced with your copy of samples.csv. - The default number of cores available to Snakemake is 1 which is enough for test data, but should probably be adjusted to 10+ for a real dataset. - Relative or absolute paths to the Snakefile and/or the working directory (if you're running elsewhere) are accepted! - The parameters and resource config YAMLs can also be customized.

conda activate camp
python /path/to/camp_gene-catalog/workflow/gene-catalog.py \
    (-c number_of_cores_allocated) \
    (-p /path/to/parameters.yaml) \
    (-r /path/to/resources.yaml) \
    -d /path/to/work/dir \
    -s /path/to/samples.csv

Slurm Cluster Deployment

To run CAMP on a job submission cluster (for now, only Slurm is supported), use the following. - --slurm is an optional flag that submits all rules in the Snakemake pipeline as sbatch jobs. - In Slurm mode, the -c flag refers to the maximum number of sbatch jobs submitted in parallel, not the pool of cores available to run the jobs. Each job will request the number of cores specified by threads in configs/resources/slurm.yaml.

conda activate camp
sbatch -J jobname -o jobname.log << "EOF"
#!/bin/bash
python /path/to/camp_gene-catalog/workflow/gene-catalog.py --slurm \
    (-c max_number_of_parallel_jobs_submitted) \
    (-p /path/to/parameters.yaml) \
    (-r /path/to/resources.yaml) \
    -d /path/to/work/dir \
    -s /path/to/samples.csv
EOF

Credits

• This package was created with Cookiecutter as a simplified version of the project template.
• Free software: MIT