MAG Binning

Overview

This module is designed to function as both a standalone MAG binning 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.).

As far as the binning procedure goes, the design philosophy is just to replicate the functionality of MetaWRAP (one of the original ensemble methods) with i) better dependency conflict management and ii) improved integration with new binning algorithms.

Currently, the binning algorithms MetaBAT2, CONCOCT, SemiBin, MaxBin2, MetaBinner, and VAMB are wrapped along with the bin refinement tool DAS Tool.

Installation

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 MAG Binning Module

1. Clone repo from Github.

git clone https://github.com/Meta-CAMP/camp_binning

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_binning/
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

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

# Run tests on the included sample dataset
conda activate camp
python /path/to/camp_binning/workflow/binning.py test

Using the Module

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

Output: 1) An output config file summarizing the locations of 2) the MAGs generated by each algorithm.

• /path/to/work/dir/binning/final_reports/samples.csv for ingestion by the next module (ex. quality-checking)
• /path/to/work/dir/binning/*/sample_name/bins/, where * refers to a binning or refinement tool, which contains FastAs of MAGs inferred by that tool

Module Structure

└── workflow
    ├── Snakefile
    ├── binning.py
    ├── utils.py
    ├── __init__.py
    └── ext/
        └── scripts/

• workflow/binning.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.

   • ingest_samples in workflow/utils.py expects Illumina reads in FastQ (may be gzipped) form
   • 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_binning/workflow/binning.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_binning/workflow/binning.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

Finishing Up

1. Each module includes a built-in Jupyter notebook designed to summarize data efficiently and visually. The following visualization generated by Dataviz.ipynb using the included test dataset. The test dataset is a toy human gut microbiome i.e.: simulated Illumina short reads covering 4 bacterial reference genomes from the Unified Human Gut Genome at 10X coverage per genome.

   Create overview graphs for all of the binned samples comparing them by size (see example below) by following the instructions in the Jupyter notebook:

conda activate camp
jupyter notebook &

2. After checking over /path/to/work/dir/binning/*/sample_name/bins/ and making sure you have bins that seem reasonable given the input dataset, you can delete all intermediate files to save space.

python /path/to/camp_binning/workflow/binning.py cleanup \
    -d /path/to/work/dir \
    -s /path/to/samples.csv

3. If for some reason the module keeps failing, CAMP can print a script containing all of the remaining commands that can be run manually.

python /path/to/camp_binning/workflow/binning.py --dry_run \
    -d /path/to/work/dir \
    -s /path/to/samples.csv

Credits

• This package was created with Cookiecutter as a simplified version of the project template.
• Free software: MIT
• Documentation: https://camp-documentation.readthedocs.io/en/latest/binning.html