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

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

Special thanks toMichael Lee for helping to put together the Conda environment for FeGenie. Thanks toNatasha Pavlovikj for creating the Conda recipe for FeGenie. Thanks toMichał Sitko for creating a Dockerfile for FeGenie.

conda create -n fegenie -c conda-forge -c bioconda -c defaults fegenie=1.0 --yesconda activate fegenieFeGenie.py -h

and when you are done using FeGenie and would like to deactivate the Conda environment for FeGenie

conda deactivate

git clone https://github.com/Arkadiy-Garber/FeGenie.gitcd FeGeniebash setup.sh./FeGenie.py -h

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

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).

./FeGenie.py -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)

FeGenie introductory slideshow:

Content |Video presentation

FeGenie video tutorial:

Content |Video presentation

To start the tutorial, hit the 'launch binder' button below, and follow the commands in 'Walkthrough'

(Initially forked fromhere. Thank you to the awesomebinder team!)

Enter the main FeGenie directory

cd FeGenie

print the FeGenie help menu

FeGenie -h

run FeGenie on test dataset

FeGenie.py -bin_dir genomes/ -bin_ext fna -out fegenie_out

Go into the output directory and check out the output files

cd fegenie_outless FeGenie-geneSummary-clusters.csv

run FeGenie on gene calls

FeGenie.py -bin_dir ORFs/ -bin_ext faa -out fegenie_out --orfs

run FeGenie on gene calls, and use reference database (RefSeq sub-sample) for cross-validation

FeGenie.py -bin_dir ORFs/ -bin_ext faa -out fegenie_out --orfs -ref refseq_db/refseq_nr.sample.faa

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

With docker installed you can runFeGenie in the following way:

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

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

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

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