ClavelLab/genome-assembly

A Snakemake workflow assembling bacterial genomes according to the standard operating procedure in the Clavel Lab

Overview

Topics: bacterial-genomes genome-assembly snakemake snakemake-workflows

Latest release: None, Last update: 2025-03-04

Linting: linting: failed, Formatting:formatting: failed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/ClavelLab/genome-assembly . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Configuration

Isolates table

The configuration file config/config.yaml should indicate the path to the tabular file describing the isolates and the raw sequences to be processed. It is specified as follows:

samples: config/isolates.tsv

The raw sequence files can be freely named and will be standardized later. In the example below, both names are accepted.

isolate	forward_file	reverse_file
Favorite_isolate	/home/rickastley/raw-sequences/Favorite_isolate_R1.fastq.gz	/home/rickastley/raw-sequences/Favorite_isolate_R1.fastq.gz	
Slowgrower_isolate	/home/rickastley/raw-sequences/ComplexName-of-the-isolate_R1.fastq.gz	/home/rickastley/raw-sequences/ComplexName-of-the-isolate_R2.fastq.gz	

An example of semi-automatic creation of the table of isolates and FASTQ files

In the configuration directory of the workflow:

cd config/

List all the forward (R1) and reverse (R2) FASTQ files in their directory

ls /data/project_fastq/PROJECT*R1_001.fastq.gz > R1
ls /data/project_fastq/PROJECT*R2_001.fastq.gz > R2

Extract the isolate name located before the first underscore

# Example of the path /data/PROJECT_fastq/PROJECTH117_S106_R1_001.fastq.gz
sed -e 's/^.*PROJECT/PROJECT/' -e 's/_.*//' R1 > sample

Paste together the columns and separate with tabulations (default)

paste -- sample R1 R2 > project

Add the necessary header to generate the required table as project.tsv

cat <(echo -e "isolate\tforward_file\treverse_file" ) project > project.tsv
rm R1 R2 sample project

Note: Don't forget to update the configuration file with the correct filename!

Computing settings

The maximum number of threads to be used for parallelisation should be indicated as follow (and as well during the snakemake invocation with the -c 9 flag):

threads: 9

The CheckM software runs a full tree by default that uses ~40Gb of RAM. If you don't have such resources, use a reduced tree (~14Gb of RAM) by setting the following flag to true:

reduced_tree: false

Databases and external resources

Place in the resources folder the FASTA files describing the adapters and the phiX sequences and indicate their path with these two flags:

adapters: resources/NexteraPE-PE.fa
phix: resources/phix.fasta

Specify where should the database of bakta should be found or if not where they should be stored using this flag:

bakta_db: /data/bakta_db

Assembly parameters

Set the minimum contig size to be kept in the assembly in basepair:

min_contig_length: 500

Linting and formatting

Linting results

NotImplementedError in file /tmp/tmpqbdcoz5x/workflow/rules/common.smk, line 7:
Remote providers have been replaced by Snakemake storage plugins. Please use the corresponding storage plugin instead (snakemake-storage-plugin-*).

Formatting results

[DEBUG] 
[DEBUG] 
[DEBUG] 
[DEBUG] In file "/tmp/tmpqbdcoz5x/workflow/rules/plasmid_reconstruction.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] 
[DEBUG] In file "/tmp/tmpqbdcoz5x/workflow/rules/common.smk":  Formatted content is different from original
[DEBUG] 
[INFO] 2 file(s) would be changed 😬
[INFO] 4 file(s) would be left unchanged 🎉

snakefmt version: 0.10.2