Reaction Inclusion by Parsimony and Transcript Distribution
v3.4.81
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. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches have been shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. This method, known as RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.
Please cite when using:
Jenior ML, Moutinho Jr TJ, Dougherty BV, & Papin JA. (2020). Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. PLOS Comp Biol. https://doi.org/10.1371/journal.pcbi.1007099.
>=python-3.6.4
>=cobra-0.15.3
>=pandas-0.24.1
>=symengine-0.4.0
>=scipy-1.3.0
Installation is:
$ pip install riptide
riptide.read_transcription_file() - Generates dictionary of transcriptomic abundances from a file
REQUIRED
file : string
User-provided file name which contains gene IDs as rows and associated transcription values as columns per replicate
OPTIONAL
header : boolean
Defines if read abundance file has a header that needs to be ignored
Default is no header
sep: string
Defines what character separates entries on each line
Defaults to tab (.tsv)
rarefy : bool
Rarefies rounded transcript abundances to 90% of the smallest replicate
Default is False
level : int
Level by which to rarefy samples
Default is 100000
binning : boolean
Perform discrete binning of transcript abundances into quantiles
OPTIONAL, not advised
Default is False
quant_max : float
Largest quantile to consider
Default is 0.9
quant_min : float
Smallest quantile to consider
Default is 0.5
step : float
Step size for parsing quantiles
Default is 0.125
norm : bool
Normalize transcript abundances using RPM calculation
Performed by default
factor : numeric
Denominator for read normalization calculation
Default is 1e6 (RPM)
silent : bool
Silences std out
Default is False
riptide.maxfit() - Create context-specific model based on transcript distribution with maximum fit of flux distribution to input transctiptome
REQUIRED
model : cobra.Model
The model to be contextualized
transcriptome : dictionary
Dictionary of transcript abundances, output of read_transcription_file()
OPTIONAL
frac_min : float
Lower bound for range of minimal fractions to test
Default is 0.25
frac_max : float
Upper bound for range of minimal fractions to test
Default is 0.85
frac_step : float
Starting interval size within fraction range
Default is 0.1
prune : bool
Perform pruning step
Default is True
samples : int
Number of flux samples to collect
Default is 500
silent : bool
Silences std out
Default is False
minimum : float
Minimum linear coefficient allowed during weight calculation for pFBA
Default is False
conservative : bool
Conservatively remove inactive reactions based on GPR rules (all member reactions must be inactive to prune)
Default is False
objective : bool
Sets previous objective function as a constraint with minimum flux equal to user input fraction
Default is True
additive : bool
Pool transcription abundances for reactions with multiple contributing gene products
Default is False
direct : bool
Assigns both minimization and maximization step coefficents directly, instead of relying on abundance distribution
Default is False
set_bounds : bool
Uses flux variability analysis results from constrained model to set new bounds for all reactions
Default is True
tasks : list
List of gene or reaction ID strings for forced inclusion in final model (metabolic tasks or essential genes)
task_lb : float
Minimum flux bound for metabolic task reactions during pruning
Default is equal to threshold var
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
threshold : float
Minimum flux a reaction must acheive in order to avoid pruning during flux sum minimization step
Default is 1e-8
defined : False or list
User defined range of linear coeffients, needs to be defined in a list like [1, 0.5, 0.1, 0.01, 0.001]
Works best paired with binned abundance catagories from riptide.read_transcription_file()
Default is False
riptide.contextualize() - Create context-specific model based on transcript distribution with user-defined objective flux minimum
REQUIRED
model : cobra.Model
The model to be contextualized
OPTIONAL
transcriptome : dictionary
Dictionary of transcript abundances, output of read_transcription_file()
With default, an artifical transcriptome is generated where all abundances equal 1.0
fraction : float
Minimum objective fraction used during single run setting
Default is 0.8
* Other arguments from iterative implementation are carried over (excluding frac_min and frac_max)
riptide.save_output() - Writes RIPTiDe results to files in a new directory
REQUIRED
riptide_obj : RIPTiDe object
Class object creared by riptide.contextualize()
OPTIONAL
path : str
New directory to write output files
file_type : str
Type of output file for RIPTiDe model
Accepts either sbml or json
Default is JSON
silent : bool
Silences std out
Default is False
Comments before starting:
import riptide
my_model = cobra.io.read_sbml_model('tests/genre.sbml')
transcript_abundances_1 = riptide.read_transcription_file('tests/transcriptome1.tsv')
transcript_abundances_2 = riptide.read_transcription_file('tests/transcriptome2.tsv') # has replicates
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.save_output(riptide_obj=riptide_object_1_a, path='~/Desktop/example_riptide_output')
Initializing model and integrating transcriptomic data...
Pruning zero flux subnetworks...
Analyzing context-specific flux distributions...
Running max fit RIPTiDe for objective fraction range: 0.65 to 0.85
Progress: 100%
Testing local fractions to 0.3...
Progress: 100%
Context-specific metabolism fit with 0.35 of optimal objective flux
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)
Context-specific metabolism correlates with transcriptome (r=0.149, p=0.011 *)
Max fit RIPTiDe completed in, 4 minutes and 33 seconds
In the final step, RIPTiDe assesses the fit of transcriptomic data for the calculated network activity through correlation of transcript abundance and median flux value for each corresponding reaction. The Spearman correlation coefficient and associated p-value are the reported following the fraction of network topology that is pruned during the flux minimization step.
Max fit RIPTiDe tests all minimum objective flux fractions over the provided range and returns only the model with the best Spearman correlation between context-specific flux for reactions and the associated transcriptomic values. Note, terminating search if a subsequent iteration has a lower correlation coefficient than the last could result from a local maxima and must be considered if an exhaustive analysis is preferred.
The resulting object is a container for the following data structures.
Examples of accessing components of RIPTiDe output:
context_specific_GENRE = riptide_object.model
context_specific_FVA = riptide_object.flux_variability
context_specific_flux_samples = riptide_object.flux_samples
Thank you for your interest in RIPTiDe!
If you encounter any problems, please file an issue along with a detailed description.
Distributed under the terms of the MIT license, "riptide" is free and open source software
FAQs
Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe)
We found that riptide demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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