Metadata-Version: 2.1
Name: riptide
Version: 2.3.7
Summary: Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe)
Home-page: https://github.com/mjenior/riptide
Author: Matthew Jenior
Author-email: mattjenior@gmail.com
License: MIT
Description: # RIPTiDe
        
        **R**eaction **I**nclusion by **P**arsimony and **T**ranscript **D**istribution
        
        ----
        
        Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Previous techniques for transcript integration have focused on creating maximum consensus with the input datasets. However, these approaches have collectively performed poorly for metabolic predictions even compared to transcript-agnostic approaches of flux minimization that identifies the most efficient patterns of metabolism given certain growth constraints. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux in conjunction with transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes. RIPTiDe requires a low level of manual intervention which leads to reduced bias in predictions. 
        
        Please cite when using:
        ```
        Jenior ML, Moutinho TJ, and Papin JA. (2019). Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. bioRxiv 637124; doi: https://doi.org/10.1101/637124
        ```
        
        Utilizes python implementation of the gapsplit flux sampler. Please also cite:
        ```
        Keaty TC and Jensen PA (2019). gapsplit: Efficient random sampling for non-convex constraint-based models. bioRxiv 652917; doi: https://doi.org/10.1101/652917
        ```
        
        ## Dependencies
        ```
        >=python-3.6.4
        >=cobra-0.15.3
        >=pandas-0.24.1
        >=symengine-0.4.0
        >=scipy-1.3.0
        ```
        
        ## Installation
        
        Installation is:
        ```
        $ pip install riptide
        ```
        
        Or from github:
        ```
        $ pip install git+https://github.com/mjenior/riptide
        ```
        
        ## Usage
        
        ```python
        from riptide import *
        
        my_model = cobra.io.read_sbml_model('examples/genre.sbml')
        
        transcript_abundances_1 = riptide.read_transcription_file('examples/transcriptome1.tsv')
        transcript_abundances_2 = riptide.read_transcription_file('examples/transcriptome2.tsv', replicates=True)
        
        riptide_object_1_a = riptide.contextualize(model=my_model, transcriptome=transcript_abundances_1)
        riptide_object_1_b = riptide.contextualize(model=my_model, transcriptome=transcript_abundances_1, tasks=['rxn1'], exclude=['rxn2','rxn3'])
        riptide_object_2 = riptide.contextualize(model=my_model, transcriptome=transcript_abundances_2)
        ``` 
        
        ### Additional parameters for main RIPTiDe functions:
        
        **riptide.read_transcription_file()**
        ```
        file : string
            User-provided file name which contains gene IDs and associated transcription values
        header : boolean
            Defines if read abundance file has a header that needs to be ignored
            default is no header
        replicates : boolean
            Defines if read abundances contains replicates and medians require calculation
            default is no replicates (False)
        sep: string
            Defines what character separates entries on each line
            defaults to tab (.tsv)
        ```
        
        **riptide.contextualize()**
        ```
        model : cobra.Model
            The model to be contextualized (REQUIRED)
        transcriptome : dictionary
            Dictionary of transcript abundances, output of read_transcription_file (REQUIRED)
        samples : int
            Number of flux samples to collect, default is 500
        norm : bool
            Normalize transcript abundances using RPM calculation
            Performed by default
        fraction : float
            Minimum percent of optimal objective value during FBA steps
            Default is 0.8
        minimum : float
            Minimum linear coefficient allowed during weight calculation for pFBA
            Default is None
        conservative : bool
            Conservatively remove inactive reactions based on genes
            Default is False
        bound : bool
            Bounds each reaction based on transcriptomic constraints
            Default is False
        objective : bool
            Sets previous objective function as a constraint with minimum flux equal to user input fraction
            Default is True
        set_bounds : bool
            Uses flax variability analysis results from constrained model to set new bounds for all reactions
            Default is True
        tasks : list
            List of reaction ID strings for forced inclusion in final model (metabolic tasks)
        exclude : list
            List of reaction ID strings for forced exclusion from final model
        gpr : bool
            Determines if GPR rules will be considered during coefficient assignment
            Default is False
        ```
        
        ### Example stdout report:
        ```
        
        Initializing model and integrating transcriptomic data...
        Pruning zero flux subnetworks...
        Analyzing context-specific flux distributions...
        
        Reactions pruned to 285 from 1129 (74.76% change)
        Metabolites pruned to 285 from 1132 (74.82% change)
        Flux through the objective DECREASED to ~54.71 from ~65.43 (16.38% change)
        Contextualized GENRE is concordant with the transcriptome (p=0.003)
        
        RIPTiDe completed in 15 seconds
        
        ```
        
        ### Resulting RIPTiDe object (class) properties:
        
        - **model** - Contextualized genome-scale metabolic network reconstruction
        - **transcriptome** - Transcriptomic abundances provided by user
        - **minimization_coefficients** - Linear coefficients used during flux sum minimization
        - **maximization_coefficients** - Linear coefficients for each reaction based used during flux sampling
        - **flux_samples** - Flux samples from constrained model
        - **flux_variability** - Flux variability analysis from constrained model
        - **fraction_of_optimum** - Minimum specified percentage of optimal objective flux during contextualization
        - **user_defined** - User defined reactions in a 2 element dictionary that either were included or excluded
        - **concordance** - Spearman correlation results between linear coefficients and median fluxes from sampling
        
        ## Additional Information
        
        Thank you for your interest in RIPTiDe, for additional questions please email mljenior@virginia.edu.
        
        If you encounter any problems, please [file an issue](https://github.com/mjenior/riptide/issues) along with a detailed description.
        
        Distributed under the terms of the [MIT](http://opensource.org/licenses/MIT) license, "riptide" is free and open source software
        
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