KasperSkytte/snakemake_savont

Minimalistic amplicon workflow for ONT data with ASVs generated by savont

Overview

Latest release: v2.0.1, Last update: 2026-11-02

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=KasperSkytte/snakemake_savont

Quality control: linting: passed formatting: passed

Workflow Rule Graph

This visualization of the workflow’s rule graph was automatically generated using Snakevision

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --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

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

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/KasperSkytte/snakemake_savont . --tag v2.0.1

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 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

To run the workflow using apptainer/singularity, use

snakemake --cores all --sdm apptainer

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

The configuration file config.yaml is used to set various options used throughout the workflow.

Option	Default value	Description
`input_dir`	`".test/nanopore_run_xyz123/fastq_pass"`	The input folder is expected to contain a subfolder for each sampleID/barcode, in which all fastq files (`.fastq`, `.fq`, optionally gzipped) will be concatenated, and the subfolder names used as sample IDs downstream. For nanopore this is usually the “fastq_pass” folder with demultiplexed reads. Subfolders named “unclassified” are ignored.
`output_dir`	`"output"`	Folder for the results.
`tmp_dir`	`"tmp"`	Folder for temporary files, which are deleted after a succesful run.
`log_dir`	`"logs"`	Folder for logs for each rule.
`threads`	`16`	Max number of threads to use for any individual rule.
`max_mem_mb`	`250000`	Max memory (MB) to request for any individual job. Memory is estimated from the input size and increased when a job is retried.
`pool_samples`	`False`	`True`: generate one set of ASVs from the reads of all samples pooled (`savont asv --pooled-samples`), which may recover more low-abundance ASVs, but needs much more memory. `False`: generate ASVs for each sample individually, which are merged by `savont export` afterwards. The results are the same tables for all samples either way.
`cutadapt_args`	`"-g AGAGTTTGATCMTGGCTCAG...CCAGCAGCCGCGGTAAT --revcomp --discard-untrimmed -e 0.15 -m 400 -M 600"`	Options passed on to `cutadapt` directly to trim primers and orient reads. Input, output, and threads are added automatically. The default is for the 27F/534R (V1-V3) primers. Set to `""` to skip trimming.
`min_reads`	`100`	Samples with fewer reads after trimming are left out of ASV generation (savont fails on samples with almost no reads).
`savont_asv_args`	`"--min-read-length 400 --max-read-length 600"`	Options passed on to the `savont asv` command directly, except input, output, and threads. Remember to set read lengths if not full-length 16S.
`classify_savont`	`True`	Whether to classify the ASVs using `savont classify` (alignment based, best at species level).
`savont_db`	`"databases/silva-138.2"`	Path to a savont database folder for `savont classify` and `savont sintax`. If it doesn’t exist and is named one of the savont databases (`emu-1`, `silva-138.2`, `greengenes2-2024.09`, `unite-10.0`) it is downloaded. Use `scripts/qiime2savont.py` to convert QIIME formatted databases like MiDAS.
`classify_savont_args`	`""`	Options passed on to the `savont classify` command directly, except input, output, database, and threads.
`classify_sintax`	`True`	Whether to classify the ASVs using `usearch -sintax`.
`sintax_db`	`"databases/MiDAS5.3_20240320/FLASVs_w_sintax.fa"`	Path to the taxonomic reference database in SINTAX format (fasta or `.udb`) used by `usearch -sintax`.
`classify_sintax_args`	`"-strand both -sintax_cutoff 0.8"`	Options passed on to the `usearch -sintax` command directly, except input, output, database, and threads.
`classify_savont_sintax`	`False`	Whether to classify the ASVs using `savont sintax` with `savont_db`. Genus level only, it does not classify to species.
`classify_savont_sintax_args`	`""`	Options passed on to the `savont sintax` command directly, except input, output, database, and threads.
`abund_usearch`	`True`	Whether to also estimate ASV abundances by mapping the trimmed reads of each sample to the ASVs using `usearch -otutab`. The savont abundance table is always made, as `savont export` is needed to merge the ASVs anyway.

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

KasperSkytte/snakemake_savont

Overview

Workflow Rule Graph

Deployment

Configuration

Configuration

Linting and formatting