riptide 1.7.1

Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe)

RIPTiDe
=======

Reaction Inclusion by Parsimonious usage and Transcript Distribution

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 (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Previous techniques for GENRE 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 (pFBA) 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). Parsimonious transcript data integration improves context-specific predictions of bacterial metabolism in complex environments. BioRxiv.


Installation
------------

Installation is simply::

pip install riptide

..

Other dependencies include both cobrapy and symengine which are automatically installed.
Cobrapy should be >=version 0.13

.. _riptide: https://github.com/mjenior/riptide



Example Use
-----------

The base use case of RIPTiDe is as follows:

.. code-block:: python

from riptide import *

my_model = cobra.io.read_sbml_model('examples/genre.sbml')

transcript_abundances_1 = read_transcription_file('examples/transcriptome1.tsv')
transcript_abundances_2 = read_transcription_file('examples/transcriptome2.tsv')

riptide_object_1 = riptide(my_model, transcript_abundances_1)
riptide_object_2 = riptide(my_model, transcript_abundances_2)
..

Additional parameters for main RIPTiDe function:

read_transcription_file()::

read_abundances_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
sep: string
Defines what character separates entries on each line
defaults to tab (.tsv)
..

riptide()::

model : cobra.Model
The model to be contextualized
transcription : dictionary
Dictionary of transcript abundances, output of read_transcription_file()
defined : False or File
Text file containing reactions IDs for forced inclusion listed on the first line and exclusion
listed on the second line (both .csv and .tsv formats supported)
samples : int
Number of flux samples to collect, default is 10000, If 0, sampling skipped
percentiles : list of floats
Percentile cutoffs of transcript abundance for linear coefficient assignments to associated reactions
Default is [50.0, 62.5, 75.0, 87.5]
coefficients : list of floats
Linear coefficients to weight reactions based on distribution placement
Default is [1.0, 0.5, 0.1, 0.01, 0.001]
fraction : float
Minimum percent of optimal objective value during FBA steps
Default is 0.8
conservative : bool
Conservatively remove inactive reactions based on genes
Default is False
bound : bool
Bounds each reaction based on transcriptomic constraints
Default is False
..

The resulting RIPTiDe object properties::

model = contextualized genome-scale metabolic network reconstruction
fluxes = Flux sampling or flux variability analysis pandas object
quantile_range = percentile intervals by which to parse transcript abundance distribution
linear_coefficient_range = linear coeeficients assigned to corresponding quantile
fraction_of_optimum = minimum percentage of optimal allowable flux through the objective during contextualization

..

Thank you for your interest in RIPTiDe, for additional questions please email [e-mail].