deyuanyang92-dev/wgs_skim

None

Overview

Latest release: None, Last update: 2026-10-30

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=deyuanyang92-dev/wgs_skim

Quality control: linting: failed formatting: failed

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/deyuanyang92-dev/wgs_skim . --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

config/config.yaml holds every option with its default and a comment. Only reads_dir (or samples) is required:

snakemake --cores 32 --sdm conda --config reads_dir=/path/to/rawdata

  • reads_dir: directory of paired FASTQ files; sample names are taken from the file names (see README, Input).

  • samples: alternatively a sample sheet, TSV or CSV with columns sample, fastq_1, fastq_2.

  • taxon: annelid (default), mollusc, fish or crustacean — sets MEANGS class, genetic code, MitoM clade and BUSCO lineage; override any of them with meangs_class, genetic_code, clade, busco_lineage.

  • step switches: meangs, mito, nr, assembly, nuc_markers, genome_size, annotate, topology, blast (true/false); or steps: to name the steps to run.

  • blast_db: local nt prefix; empty uses NCBI online BLAST. email is needed for GenBank metadata.

Threads and memory are runtime settings: use --cores, --set-threads RULE=N, --set-resources RULE:mem_mb=N or a profile, as for any Snakemake workflow. The options are validated against workflow/schemas/config.schema.yaml.

Workflow parameters

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

Parameter

Type

Description

Required

Default

reads_dir

string

directory of paired FASTQ files; sample names are detected automatically

samples

string

optional sample sheet (TSV/CSV: sample, fastq_1, fastq_2) instead of reads_dir

outdir

string

results

db_dir

string

taxon

string

annelid

genetic_code

[‘integer’, ‘string’]

meangs_class

string

clade

string

mitos_refdir

string

existing MITOS2 reference directory containing refseq89m

busco_lineage

string

steps

string

run only these steps (comma separated); empty = all switched on

qc

boolean

false: the reads are used as they are (fastp only counts them)

true

busco_mode

string

genome

mitofinder_ref

string

mitofinder_sif

string

mitofinder_assembly

string

mitofinder_min_contig

integer

1000

mitofinder_min_contig_rna

integer

300

mitofinder_cov_factor

[‘integer’, ‘number’]

5

mitofinder_max_pairs

integer

10000000

mitofinder_concat_transcripts

boolean

true

mitofinder_blast

boolean

true

mitofinder_blast_genes

string

all

mitofinder_min_pident

[‘integer’, ‘number’]

85

mitofinder_trna

string

mitfi

mitofinder_mem_gb

[‘integer’, ‘number’]

32

meangs

boolean

true

mito

boolean

true

nr

boolean

true

assembly

boolean

true

nuc_markers

boolean

true

genome_size

boolean

true

annotate

boolean

true

topology

auto

mitofinder

boolean

false

blast

boolean

true

getorganelle_kmers

string

21,45,65,85,105

getorganelle_rounds

integer

10

meangs_reads

integer

2000000

insert_size

integer

350

mt_iter_rounds

integer

3

mt_iter_R

integer

20

mt_from_spades

boolean

true

mt_iter_max_data_gb

number

20

nr_seed

string

spades_modes

string

genome

spades_kmers

[‘string’, ‘integer’]

rnaspades_kmers

[‘string’, ‘integer’]

auto

spades_careful

boolean

false

spades_only_assembler

boolean

false

spades_mode_gib_per_gb

number

5

trinity_gib_per_gb

number

6

trinity_extra

string

nr_label

string

histone_seed

string

histone_label

string

nuclear_max_gb

[‘string’, ‘number’]

Gb of reads per GetOrganelle round (rDNA, histones)

5,10

spades_min_clean_gb

number

0

spades_gib_per_gb

number

6.5

spades_min_mem_gb

number

32

topology_pairs

integer

10000000

kmer_size

integer

21

ploidy

integer

2

blast_db

string

blast_mt

string

full and/or mitochondrial genes, comma separated

cox1

blast_nr

string

full and/or 18S ITS ITS1 5.8S ITS2 28S

18S,ITS,28S

mitom_commit

string

27264d2cf672c718deff6915317a7ee01852830b

mitom_nuc_commit

string

6f75f73373ad5198c200b6da222a5975d7ee73ea

mitom_dir

string

use this MitoM checkout instead of the pinned commits

blast_topn

integer

5

email

string

Linting and formatting

Linting results
 1/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/google/auth/transport/grpc.py:43: FutureWarning: grpcio < 1.83.0 does not support Post-Quantum Cryptography (PQC). Support for non-PQC environments is deprecated. In April 2027, google-auth will raise its minimum requirements to enforce grpcio >= 1.83.0. For more details on Google Cloud's post-quantum security migration, visit: https://cloud.google.com/security/resources/post-quantum-cryptography
 2  warnings.warn(
 3Using workflow specific profile workflow/profiles/default for setting default command line arguments.
 4No validator found for JSON Schema version identifier 'http://json-schema.org/draft-07/schema#'
 5Defaulting to validator for JSON Schema version 'https://json-schema.org/draft/2020-12/schema'
 6Note that schema file may not be validated correctly.
 7[wgs_skim] MitoFinder without the Singularity image (mitofinder_sif): tRNAs with tRNAscan-SE, not MiTFi
 8WorkflowError in file "/tmp/tmpfpdpyrkt/workflow/rules/common.smk", line 44:
 9give the reads directory: --config reads_dir=/path/to/rawdata (or samples=sheet.tsv)
10  File "/tmp/tmpfpdpyrkt/workflow/rules/common.smk", line 44, in <module>
Formatting results
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22[INFO] 8 file(s) would be changed 😬
23[INFO] 5 file(s) would be left unchanged 🎉
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25snakefmt version: 0.11.5