Gene Catalog

Overview

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.).

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Approach

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Installation

  1. Clone repo from Github tutorial <https://github.com/MetaSUB-CAMP/camp_gene-catalog>.

2. Set up the conda environment using configs/conda/gene-catalog.yaml. ` cd camp_short-read-quality-control conda env create -f configs/conda/gene-catalog.yaml conda activate gene-catalog ` 3. AFTER activating the conda environment, download the bakta databases to a directory of your choosing. Make sure you update the parameters.yaml file with its location. Use absolute (not relative) paths when downloading the bakta dbs.

` bakta_db download --output /path/to/bakta/db amrfinder_update --database full/path/to/bakta/db/amrfinderplus-db ` 4. Test everything ` 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 the locations of the error-corrected FastQs, 2) the MultiQC report summarizing the properties for the QC-ed FastQ, and 3) summary statistics about the dataset after each error correction step indicating how many reads and/or bases were pruned

  • /path/to/work/dir/short_read_qc/final_reports/samples.csv for ingestion by the next module

  • /path/to/work/dir/short_read_qc/final_reports/*_multiqc_report.html, where * is ‘pre’ or ‘post’-module

  • /path/to/work/dir/short_read_qc/final_reports/read_stats.csv

Structure: ``` └── workflow

├── Snakefile ├── gene-catalog.py ├── utils.py └── __init__.py

` - ``workflow/gene-catalog.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.

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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_gene-catalog/workflow/gene-catalog.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_gene-catalog/workflow/gene-catalog.py –slurm

(-c max_number_of_parallel_jobs_submitted) -d /path/to/work/dir -s /path/to/samples.csv

EOF

```

Dependencies

<LIST ALL DEPENDENCIES>

Credits