SimpleDeNovoTranscripts

Table of contents

• Script overview
• Usage
  • Prepare environment
  • How to run:
  • Input data:
    • --sra input:
    • --local input:
• Run example
  • Output
• Full help
• Program versions

Script overview

The script automates a workflow of either fetching Sequence Read Archive (SRA) data or local data, and then setting up configurations to run one of two snakemake pipelines. --sra runs SRA prefetch before starting the pipeline Snakefile_SRA, while --local starts directly with the Snakefile_local.

This is written specifically for the venom group at UiO and is integrated with the supercomputer Saga at Sigma2.

Usage

Prepare environment

No installation required, everything you need is already prepared. Set up a screen or tmux environment then load the snakemake environment:

module load Mamba/4.14.0-0
source ${EBROOTMAMBA}/bin/activate
conda activate /cluster/projects/nn9825k/admin/mamba/marius_snakemake

How to run:

• To fetch SRA data and run the snakemake pipeline:

  python master.py --account my_slurm_account --compleasm busco_dataset --sra my_sra_table.tsv

• To run the snakemake pipeline with local data:

  python master.py --account my_slurm_account --compleasm busco_dataset --local my_local_table.tsv

Input data:

--sra input:

This table requires two columns: species_id and sra_accession. They should be tab-separated.

sample_sra_table.tsv:

species1  SRR1234567
species2  SRR1234568
species3  SRR1234569

In the example above:

• species1, species2, and species3 are the identifiers for different species or samples.
• SRR1234567, SRR1234568, and SRR1234569 are SRA accession numbers.

--local input:

This table can have three or optionally four columns: species_id, r1, r2, and optionally long_reads. They should be tab-separated.

sample_local_data_table.tsv:

species1  /path/to/species1_R1.fastq  /path/to/species1_R2.fastq  /path/to/species1_long_reads.fastq
species2  /path/to/species2_R1.fastq  /path/to/species2_R2.fastq  
species3  /path/to/species3_R1.fastq  /path/to/species3_R2.fastq  /path/to/species3_long_reads.fastq

In the example above:

• species1, species2, and species3 are the identifiers for different species or samples.
• The paths /path/to/species1_R1.fastq, /path/to/species1_R2.fastq, etc., are the file paths to the paired-end reads for the corresponding sample.
• The optional long_reads column is for long-read sequencing data.

Run example

# Input spider_table.tsv
Stegodyphus_dumicola  SRR10216514
Triconephila_clavata  SRR15356212

# Run
python master.py --sra spider_table.tsv --compleasm arachnida --account nn9825k

Output

├── 0_slurm_logs # Slurm submission logs
│   ├── compleasm_Stegodyphus_dumicola_slurm_9743926.log
│   ├── compleasm_Trichonephila_clavata_slurm_9743250.log
│   ├── SRA_Stegodyphus_dumicola_slurm_9741736.log
│   ├── SRA_Trichonephila_clavata_slurm_9741727.log
│   ├── transdecoder_Stegodyphus_dumicola_slurm_9743907.log
│   ├── transdecoder_Trichonephila_clavata_slurm_9743913.log
│   ├── trimmomatic_Stegodyphus_dumicola_slurm_9741749.log
│   ├── trimmomatic_Trichonephila_clavata_slurm_9741748.log
│   ├── trinity_Stegodyphus_dumicola_slurm_9741918.log
│   └── trinity_Trichonephila_clavata_slurm_9741902.log
├── 1_fastqc # FastQC results
│   ├── after_trim
│   └── before_trim
├── 2_trimmomatic # Trimming results
│   ├── Stegodyphus_dumicola
│   └── Trichonephila_clavata
├── 3_trinity
│   ├── Stegodyphus_dumicola
│   └── Trichonephila_clavata
├── 4_compleasm # Compleasm results
│   ├── Stegodyphus_dumicola_compleasm
│   └── Trichonephila_clavata_compleasm
├── 5_transdecoder # Transdecoder results
│   ├── Stegodyphus_dumicola
│   └── Trichonephila_clavata
├── master.py
├── README.md
├── slurm_scripts 
│   ├── compleasm.slurm
│   ├── SRA.slurm
│   ├── transdecoder.slurm
│   ├── trimmomatic_fastq.slurm
│   └── trinity.slurm
├── snakefiles
│   ├── Snakefile_local
│   └── Snakefile_SRA
├── spiders_sra.list
├── SRA_prefetch # If --SRA
│   ├── SRR10216514
│   └── SRR15356212
├── SRA_reads # If --SRA
│   ├── Stegodyphus_dumicola
│   └── Trichonephila_clavata
└── yaml
    ├── local.yaml
    └── sra.yaml

Full help

python master.py -h, --help

usage: master.py [-h] --account ACCOUNT --compleasm {insecta,arachnida,endopterygota,hymenoptera,sauropsida} [--trimmomatic TRIMMOMATIC] (--sra SRA | --local LOCAL)

Generate YAML files.

options:
  -h, --help            show this help message and exit
  --account ACCOUNT     Input slurm account
  --compleasm {insecta,arachnida,endopterygota,hymenoptera,sauropsida}
                        Dataset
  --trimmomatic TRIMMOMATIC
                        Options for trimmomatic. Default: "ILLUMINACLIP:/cluster/software/Trimmomatic/0.39-Java-11/adapters/TruSeq3-PE-2.fa:2:30:10 SLIDINGWINDOW:4:30 MINLEN:80"
  --sra SRA             Table with species_id and sra_accession (two columns, tab separated)
  --local LOCAL         Table with three (optionally four) columns: species_id r1 r2 [long_reads] (tab separated)

Program versions

• Snakemake v7.32.4
• SRA SRA-Toolkit v3.0.3
• FastQC v0.11.9
• Trimmomatic v0.39
• Trinity v2.15.1
• Compleasm v0.2.2
• Transdecoder v5.7.0