smsk_trinotate: A Snakemake workflow to anotate a transcriptome

1. Description

This is a workflow based on smsk to perform the Transdecoder/Trinotate workflow automaticaly.

2. First steps

0. Make sure you have conda and snakemake installed

1. Clone this repo

   sh git clone https://github.com/jlanga/smsk_trinotate cd smsk_trinotate    

2. Execute the pipeline with test data:

   snakemake --use-conda -j 4

3. Analyzing your data

Just paste the path of your transcriptome assembly in the second line (transcript.fa), and the gene-transcript mapping file (transcript.g2t.tsv).

4. File organization

The hierarchy of the folder is the one described in Good enough practices in scientific computing:

The report you want will be in results/trinotate/trinotate.tsv

smsk
├── bin: external scripts/binaries
├── data: raw data or links to backup data.
    ├── download: downloaded files required for trinotate and transdecoder
    └── db: processed databases for blast and hmmscan
├── doc: documentation.
├── README.md
├── results: processed data.
    ├── raw: links and auxiliary files
    ├── transdecoder: predicted cds and protein sequences, results from blast and hmmscan
    └── trinotate: final report, sqlite database, blastx, blastp and hmmscan results.
├── Snakefile: driver script of the project. Mostly links to src/snakefiles.
└── src: project's source code, config.yaml, snakefiles tarballs, etc.

5. Notes

• For performance reasons, diamond substitutes blastp and blastx both in Transdecoder and Trinotate.

• Because RNAMMER, TmHMM and SignalP require a registrations, I do not provide rules to perform those analyses.

Update (2017-07-20): Rules for Rnammer, TmHMM and SignalP are provided but commented in the snakefiles. To run those analysis, uncomment rules trinotate_signalp, trinotate_rnammer, and trinotate_tmhmm, and uncomment also the blocks in the rule trinotate_load from file src/snakefiles/trinotate.py. Finally, modify the config.yaml file with the path to the rnammer executable.

Bibliography