MPUSP/snakemake-ont-bacterial-variants

A Snakemake workflow for the identification of variants in bacterial genomes using nanopore long-read sequencing.

Overview

Latest release: v1.1.1, Last update: 2026-03-24

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=MPUSP/snakemake-ont-bacterial-variants

Quality control: linting: passed formatting: passed

Topics: bioinformatics-pipeline conda nanopore singularity snakemake variant-calling workflow

Workflow Rule Graph

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

Rule Graph light

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/MPUSP/snakemake-ont-bacterial-variants . --tag v1.1.1

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 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.

Running the workflow

Input data

The workflow requires the following files to be located in the data directory:

  1. Whole-genome sequencing data in *.fastq.gz format in data/fastq

  2. Reference genome(s) in *.fa format in data/reference

Optionally, users can provide:

  • Reference genome annotation in *.gff format in data/annotation (for feature annotation in IGV report)

  • A *.bed file with genomic regions to ignore for variant calling in data/masked_region

Please ensure that the chromosome names in *.gff and *.bed files are the same as in the reference genome.

Input data files are provided in the samples.tsv table, whose location is inidcated in the config.yml file. The samplesheet must comply with the following structure:

  • sample defines the sample name that will be used throughout the workflow and thus needs to be unique.

  • fastq provides the path to the sample’s *.fastq.gz file.

  • reference provides the path to the reference genome *.fa file (may be the same for several / all samples).

  • annotation provides the path to the optional reference genome annotation in *.gff file (may be the same for several / all samples). If no annotation is provided, you must enter n/a!

  • masked_regions provides the path to the optional *.bed file for filtering genomic regions (may be the same for several / all samples). If no *.bed file is provided, you must enter n/a!

sample

fastq

reference

annotation

masked_regions

<sample1>

data/fastq/<fastq1>.fastq.gz

data/reference/<ref1>.fa

data/annotation/<anno1>.gff

data/masked_region/<region1>.bed

<sample2>

data/fastq/<fastq2>.fastq.gz

data/reference/<ref2>.fa

data/annotation/<anno2>.gff

data/masked_region/<region2>.bed

…

…

…

…

…

<sampleN>

data/fastq/<fastqN>.fastq.gz

data/reference/<refN>.fa

data/annotation/<annoN>.gff

data/masked_region/<regionN>.bed

Configuration and parameters

Before executing the workflow, you may want to adjust several options and parameters in the default config file config/config.yml:

  1. Directories:

    • indir: Input directory for all input files, data by default (see above)

    • outdir: Output directory (relative to working directory), results by default

  2. Sample information:

    • samples: Path to samplesheet (relative to working directory), samplesheet/samples.tsv by default

    • libprepkit: Kit from ONT used for library preparation, e.g. SQK-NBD114.24

    • basecalling_model: Model used for basecalling of raw sequencing data (required for variant calling using Medaka), currently supported models are:

      • r1041_e82_400bps_sup_v4.2.0

      • r1041_e82_400bps_sup_v4.3.0

  3. Tool parameters:

    • The number of cores can be adjusted here for the following tools: NGMLR, NanoPlot, MultiQC, Medaka, Clair3, Sniffles2, and cuteSV

    • You may further adjust the run parameters for the following tools (please refer to the reference provided for more details on run parameters):

      • Filtlong: By default, reads are filtered for a minimum length of 500 bp and a mean accuracy of at least 90% (Q10), with 90% of the longest and highest-quailty reads to be kept.

      • Clair3: Variants are called on all contigs in a haploid-sensitive, ONT-specific mode using --include_all_ctgs --haploid_sensitive --platform ont.

      • cuteSV: Variants are called with the suggested parameters for ONT data (--max_cluster_bias_INS 100 --diff_ratio_merging_INS 0.3 --max_cluster_bias_DEL 100 --diff_ratio_merging_DEL 0.3) and the genotyping option enabled (--genotype).

  4. Filtering of variants:

    • The variant quality thresholds can be adjusted here for all four variant callers

    • remove_common_variants: If True, variants which have been identified in all samples with the same reference genome by one tool are filtered out. This is helpful in case all samples derive from a strain, whose genome sequence already differs from the used reference sequence. If False, all variants are reported.

  5. Reporting options:

    • igv_region_length: Neighboring variants with a maximum bp distance indicated here [1 by default] are reported in one region in the IGV variant report. Increasing this parameter will reduce the file size of the resulting IGV HTML report, if hotspots / regions with many variants exist in a sample.

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter

Type

Description

Required

Default

indir

string

Input directory containing workflow data files.

yes

data

outdir

string

Output directory for all generated results.

yes

results

samples

string

Path to the sample sheet TSV file.

yes

samplesheet/samples.tsv

libprepkit

string

Oxford Nanopore library preparation kit identifier.

yes

SQK-NBD114.24

basecalling_model

string

Basecalling model identifier used by Dorado.

yes

r1041_e82_400bps_sup_v4.3.0

filtlong

Filtlong filtering options.

yes

. params

array

List of command-line parameters passed to Filtlong.

yes

ngmlr

NGMLR resource settings.

yes

. threads

integer

Number of CPU threads allocated to NGMLR.

yes

20

nanoplot

NanoPlot resource settings.

yes

. threads

integer

Number of CPU threads allocated to NanoPlot.

yes

5

multiqc

MultiQC resource settings.

yes

. threads

integer

Number of CPU threads allocated to MultiQC.

yes

5

medaka

Medaka resource settings.

yes

. threads

integer

Number of CPU threads allocated to Medaka.

yes

10

clair3

Clair3 resource and parameter settings.

yes

. threads

integer

Number of CPU threads allocated to Clair3.

yes

10

. params

array

List of command-line parameters passed to Clair3.

yes

sniffles2

Sniffles2 resource settings.

yes

. threads

integer

Number of CPU threads allocated to Sniffles2.

yes

10

cutesv

cuteSV resource and parameter settings.

yes

. threads

integer

Number of CPU threads allocated to cuteSV.

yes

10

. params

array

List of command-line parameters passed to cuteSV.

yes

quality_threshold

Minimum quality score thresholds for variant filtering by caller.

yes

. medaka

integer

Minimum QUAL threshold for Medaka variants.

yes

10

. clair3

integer

Minimum QUAL threshold for Clair3 variants.

yes

5

. sniffles2

integer

Minimum QUAL threshold for Sniffles2 variants.

yes

10

. cutesv

integer

Minimum QUAL threshold for cuteSV variants.

yes

5

remove_common_variants

boolean

Filter out variants observed across all samples sharing a reference genome.

yes

true

igv_region_length

integer

Maximum base-pair distance to merge neighboring variants into one IGV report region.

yes

1

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