AuFAMe : Automated Functional Annotation and Metabolic networks building from bacterial genomes.

Workflow to reconstruct multiple metabolic graphs directly from sequence fasta.

To access to the strict workflow used for the following article : Evidencing strain-dependency of metabolic pathways within 1,494 lactic bacteria genomes with the in silico screening Prolipipe pipeline, please place yourself in the AuFAMe---Prolific-version branch

Table of contents

• License
• Installation
  • Dependencies
  • Installation of Pathway Tools
  • Getting the MetaCyc PADMET file
  • pip
• Usage

License

This workflow is licensed under the GNU GPL-3.0-or-later, see the LICENSE file for details.

Installation

Dependencies

These tools are needed:

• Prokka
• EggNOG-mapper
• Bakta
• Pathway Tools (which needs Blast)

And some python packages:

• mpwt
• padmet
• pandas

Installation of Pathway Tools

To run annotation based reconstruction, you need to install Pathway Tools. This tool is available at the Pathway Tools website.

Getting the MetaCyc PADMET file

You also should install the MetaCyc_XX.X.padmet (the version number of MetaCyc is replaced with XX.X), and then you should update your config.txt files for each study. This is the way to get a MetaCyc_XX.padmet file: Firstly, download the flat files of MetaCyc in DAT format at the https://biocyc.org/download.shtml webpage. Secondly, put all the downloaded DAT files in a directory (it is named FLAT_DIR here). Thirdly run this command:

padmet pgdb_to_padmet --pgdb=FLAT_DIR --output=metacyc_XX.X.padmet --version=XX.X --db=Metacyc -v

pip

If you have installed all the dependencies, you can just install AuFAMe with:

pip install aufame

Usage

Before launching AuFAMe, you will need to setup the configfile and place it in the output directory of your choice. Configfile is composed of 5 categories :

• Conda environments / Singularity images : provides path to yaml files (by default) or already available conda environment. Since singularity images can be used for tools calling (mpwt, emapper2gbk till now), you can specify path to corresponding Singularity images and AuFAMe will prioritize their use.

• Databases : provides path to the 3 databases (+1 facultative) used during the workflow and need to be downloaded :

  • Bakta,
  • EggNOG-mapper,
  • emapper2gbk uses GO ontology database that can cause errors when trying to access online. you can download it here,
  • MetaCyc.

• Files : provides path to

  • input directory containing genomes ; each genome must be placed in a subdirectory having the same name, supported fasta extensions are ".fasta", ".fa"
  • taxon file ; it can follow Prolipipe's taxonfile structure, the 3 columns are mandatory (but the first, "Species", can be empty)
  • output directory : not to change if the config file is already in your output directory

• EggNOG optimization option :

  AuFAMe can place temporarily the EggNOG database in memory to accelerate annotation. It's composed of 2 parameters : - eggnog_db_optim : fill it with "yes" if you want AuFAMe to move it temporarily in memory (needs 50G free). - eggnog_db_optim_path : you can specify the directory name in memory (need to start with /dev/shm)

• Other parameters : : you can specify the annotation tools to rely on, and intermediate files to remove

Once in an environment able to run Snakemake, you can launch AuFAMe with the following command :

snakemake --use-singularity --use-conda --conda-frontend conda -s path/to/aufame.smk -d [OUTPUT_DIR] -p --rerun-triggers mtime --resources process_data_jobs=10 --rerun-incomplete --keep-going --cores [NB_THREADS]