r1cheu/gnnotator

genome annotation workflow

Overview

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Latest release: v0.1.0, Last update: 2025-07-14

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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/r1cheu/gnnotator . --tag v0.1.0

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 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.

Workflow overview

This workflow is a workflow for genome annotation. The workflow is built using snakemake and consists of the following steps:

  1. Annotate and mask interspersed repeats and low complexity DNA sequences (RepeatMasker) in assemblies

  2. Conduct transcript-based gene predictions, including RNA-seq reads alignment (hisat2) and alignment assembly pipeline (StringTie, Trinity, PASA)

  3. Conduct ab initio gene predictions based on Fgenesh, SNAP and Eviann

  4. Conduct homology-based annotation by miniprot

  5. Combine ab intio gene predictions and protein and transcript alignments into weighted consensus gene structures by EVM

Running the workflow

Input data

Modify the samplesheet file config/samples.tsv and prepare the data for the workflow.

id

1GS-002

Then, create two directocries data/assembly and data/rnaseq, note the tissue of RNA-seq should match that in config.yaml. e.g.

data
├── assembly
│   └── 1GS-002.fa
└── rnaseq
    ├── 1GS-002_fringe_R1.fastq
    ├── 1GS-002_fringe_R2.fastq
    ├── 1GS-002_leaf_R1.fastq
    ├── 1GS-002_leaf_R2.fastq
    ├── 1GS-002_root_R2.fastq
    ├── 1GS-002_seedling_R1.fastq
    └── 1GS-002_seedling_R2.fastq

Parameters

Change config.yaml to set the parameters for the workflow.

E.g. change the rna_seq_tissue to the tissue of RNA-seq data you have.

rna_seq_tissue:
  - LEAF
  - ROOT

For parameters of pasa_config and evm_weights, it is recommended to use the default values, unless you know what you are doing.

For user who use slurm, change the slurm account in slurm/config.yaml E.g.

default-resources:
  slurm_account: "your account"

And see Snakemake executor plugin: slurm for documentation.

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