mkrg01/genome_assembly_pipeline

An integrated pipeline for eukaryotic genome assembly and gene annotation

Overview

Latest release: None, Last update: 2025-10-03

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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/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

PacBio HiFi reads

*.hifi_reads.bam

SAMPLE1.hifi_reads.bam

Index for HiFi reads

*.hifi_reads.bam.pbi

SAMPLE1.hifi_reads.bam.pbi

Paired-end RNA-seq (R1)

*_1.fastq.gz

RNASEQ1_1.fastq.gz

Paired-end RNA-seq (R2)

*_2.fastq.gz

RNASEQ1_2.fastq.gz

Notes:

  • The pipeline will automatically detect and process multiple BacBio HiFi and 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

assembly_name

Name used for output files

Dioncophyllum_thollonii

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"

tidk_clade

Clade for tidk find. Lineage list

"Caryophyllales"

tidk_telomeric_repeat_unit

A telomeric repeat unit for tidk search. A Telomeric Repeat Database

"AAACCCT"

dfam_version

Version of the Dfam database for RepeatMasker. Dfam releases

"3.9"

dfam_partitions

Dfam partitions. See README.txt.

"0,5,6" (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 rule fcs_adaptor_screen (line 290, /tmp/tmpaz5nz1z8/workflow/rules/genome_assembly.smk):
 2    * Specify a conda environment or container for each rule.:
 3      This way, the used software for each specific step is documented, and the
 4      workflow can be executed on any machine without prerequisites.
 5      Also see:
 6      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 7      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 8
 9Lints for rule fcs_adaptor_clean (line 331, /tmp/tmpaz5nz1z8/workflow/rules/genome_assembly.smk):
10    * Specify a conda environment or container for each rule.:
11      This way, the used software for each specific step is documented, and the
12      workflow can be executed on any machine without prerequisites.
13      Also see:
14      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
15      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
16
17Lints for rule fcs_gx_get_db (line 357, /tmp/tmpaz5nz1z8/workflow/rules/genome_assembly.smk):
18    * Specify a conda environment or container for each rule.:
19      This way, the used software for each specific step is documented, and the
20      workflow can be executed on any machine without prerequisites.
21      Also see:
22      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
23      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
24
25Lints for rule fcs_gx_check_db (line 388, /tmp/tmpaz5nz1z8/workflow/rules/genome_assembly.smk):
26    * Specify a conda environment or container for each rule.:
27      This way, the used software for each specific step is documented, and the
28      workflow can be executed on any machine without prerequisites.
29      Also see:
30      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
31      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
32
33Lints for rule fcs_gx_screen (line 418, /tmp/tmpaz5nz1z8/workflow/rules/genome_assembly.smk):
34    * Specify a conda environment or container for each rule.:
35      This way, the used software for each specific step is documented, and the
36      workflow can be executed on any machine without prerequisites.
37      Also see:
38      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
39      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
40
41Lints for rule fcs_gx_clean (line 449, /tmp/tmpaz5nz1z8/workflow/rules/genome_assembly.smk):
42    * Specify a conda environment or container for each rule.:
43      This way, the used software for each specific step is documented, and the
44      workflow can be executed on any machine without prerequisites.
45      Also see:
46      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
47      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers

Formatting results

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11snakefmt version: 0.11.2