PathoGenOmics-Lab/VIPERA

A Snakemake workflow for SARS-CoV-2 Viral Intra-Patient Evolution Reporting and Analysis

Overview

Latest release: v1.3.0, Last update: 2025-11-24

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Topics: bioinformatics intrahost sars-cov-2 virus-evolution reporting snakemake

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda. 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/PathoGenOmics-Lab/VIPERA . --tag v1.3.0

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.

Instructions

To run VIPERA, an environment with Snakemake version 7.19 or later is needed (see the Snakemake docs for setup instructions).

This guide provides command-line instructions for running VIPERA with Snakemake versions prior to 8. All configuration parameters are fully cross-compatible. The original publication used Snakemake 7.32, but newer versions can also be used with only minor changes. For details, see the Snakemake migration guide. For example, existing profiles are cross-compatible as well, but note that the --use-conda flag is deprecated starting with Snakemake 8. Instead, use --software-deployment-method conda.

tl;dr

  • Needs:

    • Snakemake 7.19 or later.

    • One FASTA per sample.

    • One BAM per sample.

    • One metadata CSV with columns ID, CollectionDate (YYYY-MM-DD), ResidenceCity and GISAIDEPI (can be empty).

  • Setup: edit targets.yaml (set SAMPLES and METADATA, at least) or build it using build_targets.py. Leave CONTEXT_FASTA: null to auto-download from GISAID (needs config/gisaid.yaml with your username and password) or set a local FASTA path if download fails (see GISAID disclaimer).

  • Run: snakemake (--use-conda | --sdm conda) -c4.

Inputs and outputs

The workflow requires a set of FASTA files (one per target sample), a corresponding set of BAM files (also one per target sample), and a metadata table in CSV format with one row per sample. The metadata must include at least the following columns:

  • ID: unique sample identifier, used to match sequencing files with metadata.

  • CollectionDate: the date the sample was collected (YYYY-MM-DD).

  • ResidenceCity: the location where the sample was collected.

  • GISAIDEPI: GISAID accession identifier (EPI_ISL_... or empty).

The path to these input files is set in two configuration files in YAML format: config.yaml (for general workflow settings) and targets.yaml (for specific dataset-related settings). The latter must be modified by the user to point the SAMPLES and METADATA parameters to your data. The OUTPUT_DIRECTORY parameter should point to your desired results directory.

The script build_targets.py simplifies the process of creating the targets configuration file. To run this script, you need to have PyYAML installed. It takes a list of sample names, a directory with BAM and FASTA files, the path to the metadata table and the name of your dataset as required inputs. Then, it searches the directory for files that have the appropriate extensions and sample names and adds them to the configuration file.

An example file could look like this:

OUTPUT_NAME:
  "your-dataset-name"
SAMPLES:
  sample1:
    bam: "path/to/sorted/bam1.bam"
    fasta: "path/to/sequence1.fasta"
  sample2:
    bam: "path/to/sorted/bam2.bam"
    fasta: "path/to/sequence2.fasta"
  ...
METADATA:
  "path/to/metadata.csv"
OUTPUT_DIRECTORY:
  "output"
CONTEXT_FASTA:
  null
MAPPING_REFERENCES_FASTA:
  null

This information may also be provided through the --config parameter.

Automated construction of a context dataset

Setting the CONTEXT_FASTA parameter to null (default) will enable the automatic download of sequences from the GISAID EpiCoV SARS-CoV-2 database. An unset parameter has the same effect. To enable this, you must also sign up to the GISAID platform and provide your credentials by creating and filling an additional configuration file (default: config/gisaid.yaml) as follows:

USERNAME: "your-username"
PASSWORD: "your-password"

A set of samples that meet the spatial, temporal and phylogenetic criteria set through the download_context rule will be retrieved automatically from GISAID. These criteria are:

  • Location matching the place(s) of sampling of the target samples

  • Collection date within the time window that includes 95% of the date distribution of the target samples (2.5% is trimmed at each end to account for extreme values) ± 2 weeks

  • Pango lineage matching that of the target samples

Then, a series of checkpoint steps are executed for quality assurance:

  • Remove context samples whose GISAID ID match any of the target samples

  • Enforce a minimum number of samples to have at least as many possible combinations as random subsample replicates for the diversity assessment (set in config.yaml)

The workflow will continue its execution until completion if the obtained context dataset passes these checkpoints. Otherwise, the execution will be terminated and, to continue the analysis, an external context dataset must be provided through the CONTEXT_FASTA parameter. This can be done by editing targets.yaml or via the command line:

snakemake --config CONTEXT_FASTA="path/to/fasta"

[!IMPORTANT] The GISAID EpiCoV database is proprietary and not openly accessible. For details, refer to the GISAID Terms of Use. VIPERA uses GISAIDR to automate access to GISAID data. However, this access can be unstable or occasionally fail due to changes in the platform. Possible workarounds are documented in the GISAIDR repository (e.g. issues #55 and #58). If programmatic access fails, a suitable context dataset must be manually provided by setting the CONTEXT_FASTA parameter to the path of a FASTA file. As a last resort, some of the analyses can be allowed to run even if context-dependent rules fail by passing the --keep-going flag to Snakemake. To replicate our work, the automatic context dataset is available via DOI: 10.55876/gis8.250718er (EPI_SET_250718er). Read more about EPI_SETs here.

Mapping reference sequence

Setting MAPPING_REFERENCES_FASTA to null (default) will enable the automatic download of the reference sequence(s) that were used to map the reads and generate the BAM files. An unset parameter has the same effect. If the required sequence is not available publically or the user already has it at your disposal, it can be provided manually by setting the parameter to the path of the reference FASTA file.

Workflow configuration variables

All of the following variables are pre-defined in config.yaml:

  • ALIGNMENT_REFERENCE: NCBI accession number of the reference record for sequence alignment.

  • PROBLEMATIC_VCF: URL or path of a VCF file containing problematic genome positions for masking.

  • GENETIC_CODE_JSON: path of a JSON file containing a genetic code for gene translation.

  • TREE_MODEL: substitution model used by IQTREE (see docs).

  • UFBOOT & SHALRT: settings for ultrafast bootstrap (see UFBoot) and Shimodaira–Hasegawa approximate likelihood ratio test bootstrap (see SH-aLRT) in IQTREE runs:

    • REPS: number of replicates.

    • THRESHOLD: value cutoff for visualization.

  • VC: variant calling configuration:

    • MIN_QUALITY: minimum base quality for ivar variants (option -q).

    • MIN_FREQ: minimum frequency threshold for ivar variants (option -t).

    • MIN_DEPTH: minimum read depth for ivar variants (option -m).

    • MAX_DEPTH: maximum read depth for samtools mpileup (option -d).

  • DEMIX: demixing configuration (uses Freyja, see also its docs):

    • PATHOGEN: pathogen of interest for freyja update (option --pathogen); must be ‘SARS-CoV-2’.

    • MIN_QUALITY: minimum quality for freyja variants (option --minq).

    • MAX_DEPTH: maximum read depth for samtools mpileup (option -d).

    • COV_CUTOFF: minimum depth to calculate the reported “coverage” (percent of sites with that depth) for freyja demix (option --covcut).

    • MIN_ABUNDANCE: minimum lineage estimated abundance for freyja demix (option --eps).

    • CONFIRMED_ONLY: exclude unconfirmed lineages in freyja demix (option --confirmedonly).

    • DEPTH_CUTOFF: minimum depth on each site for freyja demix (option --depthcutoff).

    • RELAXED_MRCA: assign clusters using relaxed (as opposed to strict) MRCA, used with DEPTH_CUTOFF, for freyja demix (option --relaxedmrca).

    • RELAXED_MRCA_THRESH: RELAXED_MRCA threshold for freyja demix (option --relaxedthresh).

    • AUTO_ADAPT: use error profile to set adaptive lasso penalty parameter for freyja demix (option --autoadapt).

  • WINDOW: sliding window of nucleotide variants per site configuration:

    • WIDTH: number of sites within windows.

    • STEP: number of sites between windows.

  • GB_FEATURES: optional mapping to filter which features from the GenBank file are used by some analyses (e.g. rules window and n_s_sites). If GB_FEATURES is empty or unset, all features are used. Filtering is applied in order:

    • INCLUDE: mapping of qualifier names to sequences of values. If present, only features that match at least one key/value pair in INCLUDE are included in the analyses. For example, having INCLUDE: {gene: [S, N]} keeps features whose gene qualifier equals S or N.

    • EXCLUDE: mapping of qualifier names to sequences of values. After INCLUDE is applied, any feature that matches any key/value pair in EXCLUDE is omitted. For example, having EXCLUDE: {gene: [S, N]} removes features whose gene qualifier equals S or N.

  • ANNOTATION: settings for variant annotation and functional effect prediction using SnpEff, which uses ALIGNMENT_REFERENCE for selecting the database to annotate.

    • SNPEFF_COLS: mapping of column names (which appear in result tables) to VCF fields (extracted after annotation with SnpSift). Some columns are hard-coded in the code, so removing them is not advised. Additional columns can be added as needed.

    • FILTER_INCLUDE & FILTER_EXCLUDE: mapping of column names (from SNPEFF_COLS) to lists of values used for filtering the annotated variants table. FILTER_INCLUDE is applied first, then FILTER_EXCLUDE.

      • FILTER_INCLUDE: keeps variants that match at least one listed value.

      • FILTER_EXCLUDE: removes variant that matches any listed value.

    • VARIANT_NAME_PATTERN: string template used to build the variant name shown in the results table (column VARIANT_NAME). The template is interpreted with glue and can use any column name from SNPEFF_COLS and some R functions. For example, "{GENE}:{coalesce(HGVS_P, HGVS_C)}" creates names like S:p.D614G (using HGVS_P when available, otherwise HGVS_C).

  • GISAID: automatic context download configuration.

    • CREDENTIALS: path of the GISAID credentials in YAML format.

    • DATE_COLUMN: name of the column that contains sampling dates (YYYY-MM-DD) in the input target metadata.

    • LOCATION_COLUMN: name of the column that contains sampling locations (e.g. city names) in the input target metadata.

    • ACCESSION_COLUMN: name of the column that contains GISAID EPI identifiers in the input target metadata.

  • DIVERSITY_REPS: number of random sample subsets of the context dataset for the nucleotide diversity comparison.

  • USE_BIONJ: use the BIONJ algorithm (Gascuel, 1997) instead of NJ (neighbor-joining; Saitou & Nei, 1987) to reconstruct phylogenetic trees from pairwise distances.

  • COR: configuration for correlation analyses of allele frequency data over time and between variants. This parameter controls how correlation tests are performed using R’s cor.test and cor functions (see R documentation).

    • METHOD: correlation method to use. Valid options are “pearson” (default), “kendall”, or “spearman”.

    • EXACT: boolean flag indicating whether to compute an exact p-value when possible. This option applies only to certain methods and may be set to null (default) to let R decide automatically.

  • LOG_PY_FMT: logging format string for Python scripts.

  • PLOTS: path of the R script that sets the design and style of data visualizations.

  • PLOT_GENOME_REGIONS: path of a CSV file containing genome regions, e.g. SARS-CoV-2 non-structural protein (NSP) coordinates, for data visualization (columns: region, start, end).

  • REPORT_QMD: path of the report template in Quarto markdown (QMD) format.

  • REPORT_CSS: path of the report stylesheet definition in CSS format.

Workflow visualization

Snakemake enables easy visualization of workflows and rule relationships. The --rulegraph option outputs a DOT file that describes dependencies between rules. The example below produces an image using Graphviz:

snakemake --forceall --rulegraph | dot -Tpng >.rulegraph.png

Snakemake rule graph

The same graph can also be rendered with other tools such as snakevision (v0.1.0).

snakemake --forceall --rulegraph | snakevision -s all -o .rulegraph_sv.svg

Snakemake rule graph using snakevision

The --dag option emits an directed acyclic graph (DAG) that corresponds to the rule instances that would be executed for the current dataset. The example below produces an image using Graphviz:

snakemake --forceall --dag | dot -Tpng >.dag.png

Snakemake DAG

Run modes

To run the analysis with the default configuration, run the following command (change the -c/--cores argument to use a different number of CPUs):

snakemake --use-conda -c4

To run the analysis in an HPC environment using SLURM, we provide a default SLURM profile configuration as an example that should be modified to fit your needs. Read more about Snakemake profiles here. To use the profile, install the Snakemake executor plugin for SLURM and run one of the following commands:

snakemake --slurm --profile profile/slurm  # Snakemake v7
snakemake --profile profile/slurm          # Snakemake v8+

Additionally, we offer the option of running the workflow within a containerized environment using a pre-built Docker image, provided that Apptainer/Singularity is available on the system. This eliminates the need for further conda package downloads and environment configuration. To do that, simply add the option --use-apptainer to any of the previous commands.

Using Apptainer for running VIPERA in the Windows Subsystem for Linux (WSL) may encounter errors due to the default file permissions configuration, which conflicts with Snakemake’s containerized conda environment activation mechanism. Thus, running the containerized VIPERA workflow on the WSL is not advised. Additionally, certain known issues arise when utilizing non-default temporary directories and Snakemake shadow directories. To address this issue, use the default temporary directory (e.g. export TMPDIR=/tmp in Linux machines) and specify the shadow prefix (--shadow-prefix /tmp) before executing the containerized workflow.

Linting and formatting

Linting results

  1Lints for snakefile /tmp/tmpa7deqqcu/PathoGenOmics-Lab-VIPERA-e7d89b5/workflow/rules/common.smk:
  2    * Absolute path "/"sequences.fasta" in line 52:
  3      Do not define absolute paths inside of the workflow, since this renders
  4      your workflow irreproducible on other machines. Use path relative to the
  5      working directory instead, or make the path configurable via a config
  6      file.
  7      Also see:
  8      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 16      Also see:
 17      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 23      Also see:
 24      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 30      Also see:
 31      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 33      Do not define absolute paths inside of the workflow, since this renders
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 37      Also see:
 38      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 45      file.
 46      Also see:
 47      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
 48    * Absolute path "/f" in line 14:
 49      Do not define absolute paths inside of the workflow, since this renders
 50      your workflow irreproducible on other machines. Use path relative to the
 51      working directory instead, or make the path configurable via a config
 52      file.
 53      Also see:
 54      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 56      Do not define absolute paths inside of the workflow, since this renders
 57      your workflow irreproducible on other machines. Use path relative to the
 58      working directory instead, or make the path configurable via a config
 59      file.
 60      Also see:
 61      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 69      Also see:
 70      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 76      Also see:
 77      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
 78    * Absolute path "/"freyja_data" in line 11:
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 83      Also see:
 84      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 90      Also see:
 91      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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 98      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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100      Do not define absolute paths inside of the workflow, since this renders
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102      working directory instead, or make the path configurable via a config
103      file.
104      Also see:
105      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
106    * Absolute path "/"freyja_data" in line 15:
107      Do not define absolute paths inside of the workflow, since this renders
108      your workflow irreproducible on other machines. Use path relative to the
109      working directory instead, or make the path configurable via a config
110      file.
111      Also see:
112      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
113    * Absolute path "/"{sample}/{sample}_depth.txt" in line 34:
114      Do not define absolute paths inside of the workflow, since this renders
115      your workflow irreproducible on other machines. Use path relative to the
116      working directory instead, or make the path configurable via a config
117      file.
118      Also see:
119      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
120    * Absolute path "/"{sample}/{sample}_variants.tsv" in line 35:
121      Do not define absolute paths inside of the workflow, since this renders
122      your workflow irreproducible on other machines. Use path relative to the
123      working directory instead, or make the path configurable via a config
124      file.
125      Also see:
126      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
127    * Absolute path "/"{sample}/{sample}_depth.txt" in line 52:
128      Do not define absolute paths inside of the workflow, since this renders
129      your workflow irreproducible on other machines. Use path relative to the
130      working directory instead, or make the path configurable via a config
131      file.
132      Also see:
133      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
134    * Absolute path "/"{sample}/{sample}_variants.tsv" in line 53:
135      Do not define absolute paths inside of the workflow, since this renders
136      your workflow irreproducible on other machines. Use path relative to the
137      working directory instead, or make the path configurable via a config
138      file.
139      Also see:
140      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
141    * Absolute path "/"freyja_data" in line 54:
142      Do not define absolute paths inside of the workflow, since this renders
143      your workflow irreproducible on other machines. Use path relative to the
144      working directory instead, or make the path configurable via a config
145      file.
146      Also see:
147      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
148    * Absolute path "/"freyja_data" in line 55:
149      Do not define absolute paths inside of the workflow, since this renders
150      your workflow irreproducible on other machines. Use path relative to the
151      working directory instead, or make the path configurable via a config
152      file.
153      Also see:
154      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
155    * Absolute path "/"freyja_data" in line 56:
156      Do not define absolute paths inside of the workflow, since this renders
157      your workflow irreproducible on other machines. Use path relative to the
158      working directory instead, or make the path configurable via a config
159      file.
160      Also see:
161      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
162    * Absolute path "/"samples" in line 67:
163      Do not define absolute paths inside of the workflow, since this renders
164      your workflow irreproducible on other machines. Use path relative to the
165      working directory instead, or make the path configurable via a config
166      file.
167      Also see:
168      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
169    * Absolute path "/"samples" in line 95:
170      Do not define absolute paths inside of the workflow, since this renders
171      your workflow irreproducible on other machines. Use path relative to the
172      working directory instead, or make the path configurable via a config
173      file.
174      Also see:
175      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
176    * Absolute path "/"summary.csv" in line 97:
177      Do not define absolute paths inside of the workflow, since this renders
178      your workflow irreproducible on other machines. Use path relative to the
179      working directory instead, or make the path configurable via a config
180      file.
181      Also see:
182      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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184Lints for snakefile /tmp/tmpa7deqqcu/PathoGenOmics-Lab-VIPERA-e7d89b5/workflow/rules/vaf.smk:
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190      Also see:
191      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
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197      Also see:
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14[DEBUG] In file "/tmp/tmpa7deqqcu/PathoGenOmics-Lab-VIPERA-e7d89b5/workflow/rules/context.smk":  Formatted content is different from original
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16[DEBUG] In file "/tmp/tmpa7deqqcu/PathoGenOmics-Lab-VIPERA-e7d89b5/workflow/rules/fasta.smk":  Formatted content is different from original
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18[DEBUG] In file "/tmp/tmpa7deqqcu/PathoGenOmics-Lab-VIPERA-e7d89b5/workflow/rules/common.smk":  Formatted content is different from original
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20[DEBUG] In file "/tmp/tmpa7deqqcu/PathoGenOmics-Lab-VIPERA-e7d89b5/workflow/rules/evolution.smk":  Formatted content is different from original
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22[DEBUG] In file "/tmp/tmpa7deqqcu/PathoGenOmics-Lab-VIPERA-e7d89b5/workflow/rules/report.smk":  Formatted content is different from original
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24[DEBUG] In file "/tmp/tmpa7deqqcu/PathoGenOmics-Lab-VIPERA-e7d89b5/workflow/rules/asr.smk":  Formatted content is different from original
25[INFO] 12 file(s) would be changed 😬
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27snakefmt version: 0.11.2