mkrg01/genome_assembly_pipeline

An integrated pipeline for genome assembly and gene annotation

Overview

Latest release: None, Last update: 2025-08-18

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Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --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

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/mkrg01/genome_assembly_pipeline . --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 using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

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 Guide

This document explains:

  1. Required input files to be placed in the raw_data directory.

  2. Configuration parameters to set in config/config.yml.

1. Input Files (raw_data/)

The pipeline requires both PacBio HiFi reads and paired-end RNA-seq reads.

Place your raw sequencing files in the raw_data directory with the following naming conventions:

File Type

Naming Pattern

Example

Paired-end RNA-seq (R1)

<RNA_ID>_1.fastq.gz

RNASEQ1_1.fastq.gz

Paired-end RNA-seq (R2)

<RNA_ID>_2.fastq.gz

RNASEQ1_2.fastq.gz

Additional RNA-seq samples

Continue numbering

RNASEQ2_1.fastq.gz, RNASEQ2_2.fastq.gz

PacBio HiFi reads

<SAMPLE_ID>.hifi_reads.bam

SAMPLE.hifi_reads.bam

Notes:

  • RNA-seq files must be gzip-compressed FASTQ files (.fastq.gz).

  • PacBio HiFi reads must be provided as a BAM file.

  • The pipeline will automatically detect and process multiple RNA-seq samples if present.

2. Configuration File (config/config.yml)

Edit config/config.yml to match your dataset and analysis requirements.
Below are the available parameters:

Parameter

Description

Example

ploidy

Ploidy level of the genome (used by GenomeScope2).

"2" for diploid

fcs_gx_taxid

NCBI Taxonomy ID for FCS-GX screening. NCBI Taxonomy Tree

"122299" for Dioncophyllum thollonii

busco_lineage_dataset

BUSCO lineage dataset for genome completeness assessment. Lineage list

"embryophyta_odb12"

dfam_version

Version of the Dfam database for RepeatMasker. Dfam releases

"3.9"

dfam_lineage_id

Dfam lineage ID. See README.txt.

"6" (Viridiplantae)

dfam_lineage_name

Name of the Dfam lineage to use.

"Viridiplantae"

orthodb_version

Version of the OrthoDB database (used by Braker3). ProtHint instructions

"12"

orthodb_lineage

OrthoDB lineage dataset to use. Lineage list

"Viridiplantae"

orthodb_md5sum

MD5 checksum of the OrthoDB database. Checksums

"34c1f027a1a7b10f225b69fbd5500587"

Linting and formatting

Linting results

 1Lints for snakefile /tmp/tmpes0m2uyt/workflow/rules/genome_assembly.smk:
 2    * Path composition with '+' in line 258:
 3      This becomes quickly unreadable. Usually, it is better to endure some
 4      redundancy against having a more readable workflow. Hence, just repeat
 5      common prefixes. If path composition is unavoidable, use pathlib or
 6      (python >= 3.6) string formatting with f"...".
 7    * Path composition with '+' in line 308:
 8      This becomes quickly unreadable. Usually, it is better to endure some
 9      redundancy against having a more readable workflow. Hence, just repeat
10      common prefixes. If path composition is unavoidable, use pathlib or
11      (python >= 3.6) string formatting with f"...".
12    * Path composition with '+' in line 309:
13      This becomes quickly unreadable. Usually, it is better to endure some
14      redundancy against having a more readable workflow. Hence, just repeat
15      common prefixes. If path composition is unavoidable, use pathlib or
16      (python >= 3.6) string formatting with f"...".

Formatting results

 1[DEBUG] 
 2[DEBUG] In file "/tmp/tmpes0m2uyt/workflow/rules/softmask.smk":  Formatted content is different from original
 3[DEBUG] 
 4[DEBUG] In file "/tmp/tmpes0m2uyt/workflow/Snakefile":  Formatted content is different from original
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 6[DEBUG] In file "/tmp/tmpes0m2uyt/workflow/rules/gene_prediction.smk":  Formatted content is different from original
 7[DEBUG] 
 8[DEBUG] In file "/tmp/tmpes0m2uyt/workflow/rules/genome_assembly.smk":  Formatted content is different from original
 9[INFO] 4 file(s) would be changed 😬
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11snakefmt version: 0.11.0