Short-Read Assembly

Overview

This module is designed to function as both a standalone short-read assembly 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.).

Both MetaSPAdes and MegaHit are provided as assembly algorithm options.

Installation

1. Clone repo from Github.

git clone https://github.com/MetaSUB-CAMP/camp_short-read-assembly

2. Set up the conda environment using configs/conda/short-read-assembly.yaml.

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

# Create and activate conda environment 
cd camp_short-read-assembly
conda env create -f configs/conda/short-read-assembly.yaml
conda activate short-read-assembly

3. Update the relevant parameters i.e.: choice and mode (metagenomics, metaviral, etc.) of assembler in test_data/parameters.yaml.

4. Make sure the installed pipeline works correctly. With 40 threads and a maximum of 50 GB allocated, the test dataset should finish in approximately 2 minutes.

# Run tests on the included sample dataset
python /path/to/camp_short-read-assembly/workflow/short-read-assembly.py test

Using the Module

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

Output: 1) An output config file summarizing 2) the module's outputs, which are assembled contigs.

• /path/to/work/dir/short-read-assembly/final_reports/samples.csv for ingestion by the next module

• /path/to/work/dir/short-read-assembly/final_reports/sample_name.metaspades.fasta and/or sample_name.megahit.fasta, which are the outputs of MetaSPAdes and MegaHit respectively

Structure:

└── workflow
    ├── Snakefile
    ├── short-read-assembly.py
    ├── utils.py
    └── __init__.py

• workflow/short-read-assembly.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.

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/.

   • ingest_samples in workflow/utils.py expects Illumina reads in FastQ (may be gzipped) form and de novo assembled contigs in FastA 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.

python /path/to/camp_short-read-assembly/workflow/short-read-assembly.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.

sbatch -J jobname -o jobname.log << "EOF"
#!/bin/bash
python /path/to/camp_short-read-assembly/workflow/short-read-assembly.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. To plot grouped bar graph(s) of the number of reads and bases in each de novo assembly (from each sample), set up the dataviz environment and follow the instructions in the Jupyter notebook:

conda env create -f configs/conda/dataviz.yaml
conda activate dataviz
jupyter notebook &

2. After checking over final_reports/ and making sure you have everything you need, you can delete all intermediate files to save space.

python3 /path/to/camp_short-read-assembly/workflow/short-read-assembly.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.

python3 /path/to/camp_short-read-assembly/workflow/short-read-assembly.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/short-read-assembly.html