Matteopaluh / KEMET

KEGG Module Evaluation Tool

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KEMET

KEgg Module Evaluation Tool
"KEMET - a python tool for KEGG Module evaluation and microbial genome annotation expansion"
DOI:10.1016/j.csbj.2022.03.015.svg

Script description

The kemet.py script works as a command line tool that serves three main functions:

  1. Evaluate KEGG Modules Completeness and summarize metabolic potential of MAGs/Genomes of interest, organizing the info into tables.

  2. Perform HMM-based searches for ortholog genes (KO) of interest, to expand KEGG Module Completeness evaluation.

  3. Genome-scale models (GSMM or GEM) gapfill with evidence from nucleotidic HMM searches, regarding KOs of interest.

KEMET is well suited for metagenome-spanning analyses as well as single genomes usage, in order to get a better understanding of microbial metabolic and ecological functions.

Citing KEMET

If you are using this program, please refer to our paper published in Computational and Structural Biotechnology Journal, available here.

Installation

The program is designed to have an easy installation procedure on UNIX-based machines, nonetheless the code is compatible with Windows systems.

Full installation is achieved in just a couple of minutes following the command-lines described in the wiki pages.

This tool was meant to have few external dependencies to ensure stability.

General Setup process and use - Conda environment

Refer to the Setup wiki page to properly set the working directory.
Moreover it is important to follow the instructions to place relevant input files in the appropriate subdirectories and using proper format for said files.


Command line (minimal required arguments)

./kemet.py [FASTA_file] -a [FORMAT] --hmm_mode [MODE] --gsmm_mode [MODE] (--skip_hmm) (--skip_gsmm) (--no_genome)

[FASTA_file]: FASTA file indication of the MAG/Genome of interest (with or without path indication e.g. genomes/bin1.fasta). With further arguments it can also be the indication of a KEGG annotation file.

-a [FORMAT]: program used to annotate KEGG KOs, i.e. KEGG annotation format (either eggnog / kaas / kofamkoala) - used to generate KEGG MODULES recap tables. Default file extension must be maintained (e.g. .emapper.annotations, .ko)

--hmm_mode [MODE]: when HMM analysis is desired, use this parameter to indicate a subset of KOs to search further using profile HMMs. [MODE] should be either one of onebm, module, kos, as described in the wiki pages.

--gsmm_mode [MODE]: when GSMM/GEM gapfilling is desired, use this parameter to indicate whether to perform de-novo GSMM/GEM reconstruction or add reactions to an existing model. [MODE] should be either denovo or existing, respectively, as described in the wiki pages.

--skip_hmm: use this to stop after KEGG MODULES Completeness Evaluation. The only output would be organized tables of metabolic potential.

--skip_gsmm: use this to stop after HMM analysis.

--no_genome: use this to indicate the (path to a) file with KEGG annotations, in order not to include MAG/Genome operations. Using this will result in stopping after KEGG MODULES Completeness Evaluation.


Other suggested optional parameters include:

--log: store KEMET progress in a log file (STRONGLY suggested).
-v: print more informations, for progress reporting purposes & more info.
-q: print less informations and silence MAFTT and HMMER soft-errors (suggested).

--as_kegg: changes how incomplete KEGG MODULES are summarized in recap tables - following KEGG-Mapper convention, i.e. Modules with less than 3 blocks are marked as INCOMPLETE regardless of the number of incomplete blocks. This imply using a more conservative approach regarding annotations.

--threshold_value [VALUE]: use another quality filter to differentiate between legit HMM hits (default: 0.43).


Script details

For detailed info on the process/outputs of each KEMET task, as well as info on custom KEGG Modules & other, please refer to the wiki pages.


Credits

Developed by Matteo Palù at Università degli Studi di Padova (2020-2023).

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KEGG Module Evaluation Tool

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Language:Python 78.6%Language:Jupyter Notebook 21.4%