microbetag.genres
=================

.. py:module:: microbetag.genres


Classes
-------

.. autoapisummary::

   microbetag.genres.GEMSReconstruction


Functions
---------

.. autoapisummary::

   microbetag.genres.ms_reconstruct
   microbetag.genres.dnngior_gapfill
   microbetag.genres.run_carve


Module Contents
---------------

.. py:class:: 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.

   :param config: Instance of the :class:`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


   .. py:method:: rast_annotate_genomes()

      Pool for running rast annotations for a list of bins



   .. py:method:: 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



   .. py:method:: 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.



   .. py:method:: 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.



   .. py:method:: fgs_annotate_genomes()

      Use FragGeneScan to get .faa files.



.. py:function:: ms_reconstruct(faa, outdir=None)

   Running ModelSEEDpy on its own conda environment


.. py:function:: dnngior_gapfill(draft_model, medium=None, outdir=None)

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


.. py:function:: run_carve(faa_files, output_dir, dna=False)

   Build a GEM using carveme for a list of