FeGenie: A Python repository from note

FeGenie

Please see the Wiki page for introduction and tutorial on how to use this tool.

Special thanks to Michael Lee (https://github.com/AstrobioMike) for helping to put together the setup.sh script, which signficantly eases installation.

Easy Installation (if you have Conda installed)

git clone https://github.com/Arkadiy-Garber/FeGenie.git
cd FeGenie
bash setup.sh
conda activate fegenie
FeGenie.py -h

Easy Installation (if you don't have Conda)

git clone https://github.com/Arkadiy-Garber/FeGenie.git
cd FeGenie
bash setup_noconda.sh
./FeGenie.py -h

Quick-start (if you installed using the 'setup.sh' script)

FeGenie.py -bin_dir /directory/of/bins/ -bin_ext fasta -t 16 -out output_fegenie

The argument for -bin_ext needs to represent the filename extension of the FASTA files in the selected directory that you would like analyzed (e.g. fa, fasta, fna, etc).

Quick-start (if you installed using the 'setup_noconda.sh' script)

./FeGenie.py -hmm_lib hmms/iron -bin_dir /directory/of/bins/ -bin_ext fasta -t 16 -out output_fegenie

hmms/iron directory can be found within FeGenie's main repository -t 8 means that 8 threads will be used for HMMER and BLAST. If you have less than 16 available on your system, set this number lower (default = 1)

Running with docker

In case of running FeGenie with docker the only dependency you need to have installed is docker itself (installation guide).

With docker installed you can run FeGenie in the following way:

docker run -it -v $(pwd):/data note/fegenie-deps ./FeGenie.py -bin_dir test_dataset -bin_ext txt -out fegenie_out -hmm_lib hmms/iron -t $(nproc)

./FeGenie.py ... follows normal, non-dockerized flow of arguments.

Beware that you need to mount directories which contain files FeGenie is supposed to read. If you are not familiar with docker then run docker run command from the directory into which you cloned FeGenie repository. If all the files you pass to FeGenie are in inside this directory and you use relative filepaths (like e.g. hmms/iron) everything will work just fine.

Upcoming Updates (we welcome more suggestions, which can be submitted as an Issue)

Ability to accept previously-annotated genomes and gene-calls.
Include Cytochrome 579 (and possible rusticyanin)
Improve dilineation between MtrA and MtoA for better resolution with respect to identification of iron reduction and iron oxidation, respectively.
Option to report absolute values for gene counts (rather than normalized gene counts)
Include option to release all results (regardless of whether rules for reporting were met)
Identification of iron-sulfur proteins.

Citing FeGenie:

Garber AI, Nealson KH, Okamoto A, McAllister SM, Chan CS, Barco RA and Merino N (2020) FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies. Front. Microbiol. 11:37. doi: 10.3389/fmicb.2020.00037

note/FeGenie