cmc-aau/nanopore_16Samp

Snakemake workflow to process 16S nanopore amplicon sequences (of any length) and produce an abundance table including taxonomy

Overview

Topics:

Latest release: v2.0.5, Last update: 2024-08-26

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/cmc-aau/nanopore_16Samp . --tag v2.0.5

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

input_dir: The input folder is expected to contain a subfolder for each sampleID/barcode, then all fastq files in each subfolder are concatenated and the folder name is used as sample ID downstream. This is usually the "fastq_pass" folder from nanopore sequencing and basecalling output
output_dir: Output directory with the final results
tmp_dir: Directory for temporary files
log_dir: Directory for log files for all rules
db_fasta: Database for minimap2 in fasta file format. Each sequence header must only contain an ID matching the taxonomy file below, nothing else.
db_tax: 2-column TSV file with corresponding taxonomy for each sequence in the above fasta file. The first column is for the sequence IDs, the second a semi-colon separated taxonomy string.
minalignlen: Minimum alignment length for the mapping. Any alignments shorter than this threshold will be filtered
minIDfrac: Minimum identity threshold of each mapping (value must be between 0.0-1.0). Any alignments with an identity lower than this threshold will be filtered.
filtlong_args: Arguments passed on to the filtlong command for pre-filtering reads
max_threads: Max number of threads for any rule

Have a look in the .test directory for example files.

Database files

If you want to use the SILVA database, you can use this R script to create the two requires database files. For the MiDAS database the files can be downloaded directly from download section on the website.

Linting and formatting

Linting results

Traceback (most recent call last):

  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/cli.py", line 2024, in args_to_api
    any_lint = workflow_api.lint()
               ^^^^^^^^^^^^^^^^^^^

  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/api.py", line 337, in _handle_no_dag
    return method(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/api.py", line 354, in lint
    workflow.include(

  File "/home/runner/micromamba-root/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/snakemake/workflow.py", line 1412, in include
    exec(compile(code, snakefile.get_path_or_uri(), "exec"), self.globals)

  File "/tmp/tmp16k5p49e/cmc-aau-nanopore_16Samp-9978980/workflow/Snakefile", line 17, in <module>
    ]
      


... (truncated)

Formatting results

[DEBUG] 
[DEBUG] In file "/tmp/tmp16k5p49e/cmc-aau-nanopore_16Samp-9978980/workflow/rules/05-mapping_overview.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp16k5p49e/cmc-aau-nanopore_16Samp-9978980/workflow/rules/04-map2db.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp16k5p49e/cmc-aau-nanopore_16Samp-9978980/workflow/rules/03-checksum_fq_headers.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp16k5p49e/cmc-aau-nanopore_16Samp-9978980/workflow/rules/02-qfilter.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp16k5p49e/cmc-aau-nanopore_16Samp-9978980/workflow/rules/01-cat_fastq.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp16k5p49e/cmc-aau-nanopore_16Samp-9978980/workflow/rules/06-abund_table.smk":  Formatted content is different from original
[DEBUG] 
[DEBUG] In file "/tmp/tmp16k5p49e/cmc-aau-nanopore_16Samp-9978980/workflow/Snakefile":  Formatted content is different from original
[INFO] 7 file(s) would be changed 😬

snakefmt version: 0.10.2