Metadata-Version: 2.1
Name: Meneco
Version: 2.0.2
Summary: Metabolic Network Completion. Compute minimal completions to your draft network with reactions from a repair network.
Home-page: http://bioasp.github.io/meneco/
Author: Sven Thiele
Author-email: sthiele78@gmail.com
License: GPLv3+
Description: # Meneco [![PyPI version](https://img.shields.io/pypi/v/meneco.svg)](https://pypi.org/project/meneco/)
        
        ## Installation
        
        Requires **Python >= 3.7**
        
        Required packages (starting from version 2.0 of the package):
        
        * [``Clyngor``](https://github.com/Aluriak/clyngor) or [``Clyngor_with_clingo``](https://github.com/Aluriak/clyngor-with-clingo) that includes the solvers
        
        You can install Meneco by running:
        
        ```sh
        python setup.py install
        ```
        
        You should always use a virtual environment ([virtualenv](https://virtualenv.pypa.io/en/latest/), [virtualenv wrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)) when using Python
        
        ## Usage from console
        
        Typical usage is:
        
        ```sh
        meneco -d draftnetwork.sbml -s seeds.sbml -t targets.sbml -r repairnetwork.sbml
        ```
        
        For more options you can ask for help as follows:
        
        ```text
        usage: meneco [-h] -d DRAFTNET -s SEEDS -t TARGETS [-r REPAIRNET]
                           [--enumerate] [--json]
        
        optional arguments:
          -h, --help            show this help message and exit
          -d DRAFTNET, --draftnet DRAFTNET
                                metabolic network in SBML format
          -s SEEDS, --seeds SEEDS
                                seeds in SBML format
          -t TARGETS, --targets TARGETS
                                targets in SBML format
          -r REPAIRNET, --repairnet REPAIRNET
                                perform network completion using REPAIRNET a metabolic
                                network in SBML format
          --enumerate           enumerate all minimal completions
          --json                produce JSON output
        ```
        
        ## Calling Meneco from a python script
        
        You can use Meneco from python by calling the command run_meneco() with the paths of files as input arguments and a boolean value for the enumeration (`True` for the enumeration, else `False`) :
        
        ```py
        from meneco import run_meneco
        
        result = run_meneco(draftnet="toy/draft.sbml",
                        seeds="toy/seeds.sbml",
                        targets="toy/targets.sbml",
                        repairnet="toy/repair.sbml",
                        enumeration=False,
                        json=True)
        ```
        
        The output will be the set of unproducible targets, reconstructable targets, a dictionnary of essentials reactions for each target, one minimal solution, the set of reactions belonging to the intersection of solutions, the set of reactions belonging to the union of solutions and a list of lists corresponding to the reactions for each solution (if enumeration == True).
        
        For a step by step demonstration on how to use Meneco as a library, have a look at our notebooks [here](https://mybinder.org/v2/gh/bioasp/meneco/master?filepath=meneco.ipynb) or [here](https://colab.research.google.com/drive/170IQ8YV-J0R1GH6rsU2t8YUKOkApLeNF?usp=sharing).
        
        ## Bibliography
        
        Please cite the following paper when using Meneco:
        
        **S. Prigent et al., “Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks,” PLOS Computational Biology, vol. 13, no. 1, p. e1005276, Jan. 2017. [https://doi.org/10.1371/journal.pcbi.1005276](https://doi.org/10.1371/journal.pcbi.1005276)**
        
        The concepts underlying Meneco is described in this paper:
        
        T. Schaub and S. Thiele, “Metabolic network expansion with answer set programming,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, vol. 5649 LNCS, pp. 312–326. [https://doi.org/10.1007/978-3-642-02846-5_27](https://doi.org/10.1007/978-3-642-02846-5_27)
        
        A first application of the method was presented in:
        
        G. Collet et al., “Extending the Metabolic Network of Ectocarpus Siliculosus Using Answer Set Programming,” in LPNMR 2013: Logic Programming and Nonmonotonic Reasoning, 2013, pp. 245–256. [https://doi.org/10.1007/978-3-642-40564-8_25](https://doi.org/10.1007/978-3-642-40564-8_25)
        
        ## Samples
        
        Sample files for the reconstruction of Ectocarpus are available here: [ectocyc.sbml][1], [metacyc_16-5.sbml][2], [seeds.sbml][3], [targets.sbml][4]
        
        [1]: http://bioasp.github.io/downloads/samples/ectodata/ectocyc.sbml
        [2]: http://bioasp.github.io/downloads/samples/ectodata/metacyc_16-5.sbml
        [3]: http://bioasp.github.io/downloads/samples/ectodata/seeds.sbml
        [4]: http://bioasp.github.io/downloads/samples/ectodata/targets.sbml
        
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