microbetag.genres

Classes

GEMSReconstruction

Class for Genome Scale Metabolic Network Reconstruction (GENRE).

Functions

ms_reconstruct(faa[, outdir])	Running ModelSEEDpy on its own conda environment
dnngior_gapfill(draft_model[, medium, outdir])	draft_model: Path to draft .xml
run_carve(faa_files, output_dir[, dna])	Build a GEM using carveme for a list of

Module Contents

class microbetag.genres.GEMSReconstruction(config)

Class for Genome Scale Metabolic Network Reconstruction (GENRE). It makes use of either ModelSEEDpy or CarvMe, two well established methods for building GENRES.

Parameters:

config – Instance of the Config class.

• threads

• bin_filenames

• bins_path

• reconstructions

• sc_input_type

• for_reconstructions

• genres

• gapfill_model

• gapfill_media

Note

CarveMe:

Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic acids research. 2018 Sep 6;46(15):7542-53. DOI: https://doi.org/10.1093/nar/gky537

ModelSEEDpy:

Faria JP, Liu F, Edirisinghe JN, Gupta N, Seaver SM, Freiburger AP, Zhang Q, Weisenhorn P, Conrad N, Zarecki R, Song HS. ModelSEED v2: High-throughput genome-scale metabolic model reconstruction with enhanced energy biosynthesis pathway prediction. bioRxiv. 2023 Oct 6:2023-10. DOI: https://doi.org/10.1101/2023.10.04.556561

Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature biotechnology. 2010 Sep;28(9):977-82. DOI: https://doi.org/10.1038/nbt.1672

rast_annotate_genomes()

Pool for running rast annotations for a list of bins

rast_annotate_a_genome(bin_filename)

RAST annotate a user’s bin based on: https://www.bv-brc.org/docs/cli_tutorial/rasttk_getting_started.html#the-concept-of-the-genome-typed-object

modelseed_reconstructions()

Pool for running GENREs reconstruction using the final .faa files from the rast_annotate_genomes() function

Note

Currently is not being as the RAST server seems not that stable to have several queries.

carve_reconstructions()

Reconstruct a GENRE using CarveMe and a .faa as input. You can get such a file after running RAST annotation or after any gene prediction tool such as Prodigal, FragenScan etc.

fgs_annotate_genomes()

Use FragGeneScan to get .faa files.

microbetag.genres.ms_reconstruct(faa, outdir=None)

Running ModelSEEDpy on its own conda environment

microbetag.genres.dnngior_gapfill(draft_model, medium=None, outdir=None)

draft_model: Path to draft .xml medium: path to tab-separated file .tsv

microbetag.genres.run_carve(faa_files, output_dir, dna=False)

Build a GEM using carveme for a list of