ltalignani/evo-shave

DNAseq pipeline

Overview

Latest release: None, Last update: 2026-06-13

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=ltalignani/evo-shave

Quality control: linting: failed formatting: failed

Wrappers: bio/fastqc bio/gatk/combinegvcfs bio/multiqc bio/picard/markduplicates bio/samtools/index bio/trimmomatic/pe

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/ltalignani/evo-shave . --tag None

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

Required files

config/samples.tsv

One row per biological sample. The sample column must match the sample names used in config/units.tsv.

Column

Description

sample

Unique sample identifier (string, may contain letters, digits, hyphens, dots)

Example:

sample
FCV003
MPLS001
109

config/units.tsv

One row per sequencing unit (one sample may span multiple lanes / units). BAMs from multiple units are merged automatically before downstream processing.

Column

Description

sample

Sample identifier — must match samples.tsv

unit

Lane or unit identifier (e.g. L1, L8)

platform

Sequencing platform (e.g. ILLUMINA)

fq1

Path to R1 FASTQ file (gzip-compressed)

fq2

Path to R2 FASTQ file (gzip-compressed)

Example:

sample  unit  platform   fq1                           fq2
FCV003  L1    ILLUMINA   raw/FCV003_L1_R1.fastq.gz     raw/FCV003_L1_R2.fastq.gz
MPLS001 L1    ILLUMINA   raw/MPLS001_L1_R1.fastq.gz    raw/MPLS001_L1_R2.fastq.gz

Key parameters (config/config.yaml)

Reference genome

refs:
  ref_name: "AalbF5"
  reference: "resources/genomes/AalbF5.fasta"   # path to FASTA
  index: "resources/genomes/AalbF5.fasta.fai"   # samtools fai index
  dict: "resources/genomes/AalbF5.dict"          # Picard sequence dictionary

BWA indices must be pre-built in resources/indexes/bwa/.

Variant caller

caller: "HaplotypeCaller"   # or "UnifiedGenotyper"

  • HaplotypeCaller (GATK4): per-sample GVCF → joint genotyping. Recommended for most use cases.

  • UnifiedGenotyper (GATK3): multi-sample calling with indel realignment. Use to match MalariaGEN phase 2/3 parameters.

Chromosome / scaffold selection

chromosomes:
  auto: true       # read contigs from .fai at parse time (recommended)
  min_size: 0      # exclude scaffolds smaller than N bp (0 = keep all)
  pattern: ""      # regex filter on contig names, e.g. "^NC_" (empty = keep all)
  list:            # used when auto: false
    - "NC_085136.1"
  vcf_output: "both"   # "per_contig" | "merged" | "both"

MarkDuplicates

markdup:
  skip: false            # set to true for ddRAD-seq data
  remove-duplicates: false

Set skip: true when processing ddRAD-seq libraries: enzymatic digestion produces reads sharing the same start coordinates, which Picard would incorrectly flag as PCR duplicates.

Hard filtering thresholds

filtering:
  hard:
    snvs: "QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0"
    indels: "QD < 2.0 || FS > 200.0 || SOR > 10.0 || ReadPosRankSum < -20.0"

Variants failing any threshold are tagged FILTER in the output VCF. Adjust thresholds based on your species and library characteristics.

Trimmomatic

trimmomatic:
  adapters:
    truseq2-pe: "resources/adapters/TruSeq2-PE.fa"
  settings: "LEADING:20 TRAILING:3 SLIDINGWINDOW:5:20 AVGQUAL:20 MINLEN:50"
  phred: "-phred33"

Results archiving (optional)

transfer:
  results_dir: "/path/to/archive"
  run_name: "evo-shave"

Used by transfer_results.sh to rsync outputs to a shared archive after the run.

Workflow parameters

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

Parameter

Type

Description

Required

Default

samples

string

Path to the samples file

yes

units

string

Path to the units file

yes

resources

yes

. tmpdir

string

Temporary directory

yes

trimmomatic

yes

. adapters

yes

. . nextera

string

yes

. . truseq2-pe

string

yes

. . truseq2-se

string

yes

. . truseq3-pe

string

yes

. . truseq3-pe-2

string

yes

. . truseq3-se

string

yes

. settings

string

yes

. phred

string

yes

refs

yes

. ref_name

string

yes

. path

string

yes

. reference

string

yes

. index

string

yes

. dict

string

yes

markdup

yes

. remove-duplicates

boolean

yes

caller

string

yes

gatk

yes

. haplotypecaller

string

yes

. output_mode

string

yes

. genomicsdbimport

string

yes

. genotypegvcfs

string

yes

chromosomes

array

yes

filtering

yes

. hard

yes

. . snvs

string

yes

. . indels

string

yes

Linting and formatting

Linting results
1FileNotFoundError in file "/tmp/tmplakgbhkd/workflow/rules/common.smk", line 58:
2[Errno 2] No such file or directory: 'resources/genomes/GCA_018104305.1_AalbF3_genomic.fna.fai'
3  File "/tmp/tmplakgbhkd/workflow/rules/common.smk", line 85, in <module>
4  File "/tmp/tmplakgbhkd/workflow/rules/common.smk", line 58, in get_chromosomes
5  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/pandas/io/parsers/readers.py", line 1405, in read_table
6  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/pandas/io/parsers/readers.py", line 620, in _read
7  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
8  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/pandas/io/parsers/readers.py", line 1880, in _make_engine
9  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/pandas/io/common.py", line 873, in get_handle
Formatting results
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40snakefmt version: 0.11.5