MPUSP/snakemake-ont-bacterial-variants

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

Overview

Testing:

Last update: 2025-12-12

Latest release: v1.1.0

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

Authors: @tfwulff @m-jahn @rabioinf

Configuration

The following configuration details are extracted from the config's README file.

---

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.