Short-Read Assembly

Overview

This module is designed to function as both a standalone MAG short-read assembly pipeline as well as a component of the larger CAMP/CAP2 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.

workflow loc

Approach

Installation

  1. Clone repo:

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

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

` cd camp_short-read-assembly conda env create -f configs/conda/short-read-assembly.yaml conda activate short-read-assembly `

  1. Make sure the installed pipeline works correctly. pytest only generates temporary outputs so no files should be created.

` pytest .tests/unit/ `

Quickstart

Running each CAMP module takes the same three steps, listed below.

  1. As with all CAMP modules, update the parameters.yaml file:

<TABLE OF PARAMETERS AND DESCRIPTIONS>

  1. Generate your samples.csv file in the following format:

<SAMPLES.CSV FORMAT>

  1. Deploy!

<SNAKEMAKE COMMAND>

Module details

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/metaspades.scaffolds.fasta and/or megahit.contigs.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 unit test generation 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.

  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 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!

` python /path/to/camp_short-read-assembly/workflow/short-read-quality-control.py -d /path/to/work/dir -s /path/to/samples.csv `

  • Note: This setup allows the main Snakefile to live outside of the work directory.

Running on a slurm cluster

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-quality-control/workflow/short-read-assembly.py –slurm

(-c max_number_of_parallel_jobs_submitted)

-d /path/to/work/dir -s /path/to/samples.csv

EOF

Credits