Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe)

## Project 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 (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.
```

## 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
```

### Arguments for core RIPTiDe functions:

**riptide.read_transcription_file() - Generates dictionary of transcriptomic abundances from a file**

```
REQUIRED
file : string
User-provided file name which contains gene IDs and associated transcription values
OPTIONAL
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)
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)
```

**riptide.contextualize() - Create context-specific model based on transcript distribution**

```
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
samples : int
Number of flux samples to collect
Default is 500
silent : bool
Silences std out
Default is False
exch_weight : bool
Weight exchange reactions the same as adjacent transporters
Default is True
processes : int
The number of parallel processes to run for FVA. Optional and if not passed,
will be set to the number of CPUs found. Necessary to change if
your trying to run paralell instance of RIPTiDe on the same machine
Default is none
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 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
important : list
List of gene or reaction ID strings for which the highest weights are assigned regardless of transcription
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)
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-6
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
open_exchanges : bool
Sets all exchange reactions bounds to (-1000., 1000)
Default is False
```

**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 SBML
```

**riptide.maxfit_contextualize() - Iterative RIPTiDe for a range of minimum objective fluxes, returns model with best fit to transcriptome**

```
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 0.65
frac_max : float
Upper bound for range of minimal fractions to test
Default is 0.85
frac_step : float
Increment to parse input minimal fraction range
Default is 0.02
first_max : bool
Exits early if next subsequent iteration has a worse correlation
Default is False
ADDITIONAL
All other optional parameters for riptide.contextualize()
'''
```

## Usage

**Comments before starting:**

- Make sure that genes in the transcriptome file matches those that are in your model.
- Check the example files for proper data formatting
- Binning genes into discrete thresholds for coefficient assignment is available in riptide.read_transcription_file() (not recommended)
- Opening the majority of exchange reactions (bounds = +/- 1000) may yeild better prediction when extracellular conditions are unknown
- The resulting RIPTiDe object has multiple properties including the context-specific model and flux analyses, accessing each is described below

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_maxfit = riptide.maxfit_contextualize(model=my_model, transcriptome=transcript_abundances_2) riptide.save_output(riptide_obj=riptide_object_1_a, path='~/Desktop/riptide_output')

### Example riptide.contextualize() 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)
Context-specific metabolism correlates with transcriptome (r=0.149, p=0.011 *)
RIPTiDe completed in 17 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.

### Example riptide.maxfit_contextualize() stdout report:

```
Running max fit RIPTiDe for objective fraction range: 0.65 to 0.85 with intervals of 0.02
Fraction = 0.65 | Rho = 0.133240946224708 ; p = 0.022539650586387808
Fraction = 0.67 | Rho = 0.14077134473559572 ; p = 0.015894179122544805
Fraction = 0.69 | Rho = 0.1520149134045448 ; p = 0.009524663445348861
Fraction = 0.71 | Rho = 0.13110725631615502 ; p = 0.024321642238502965
Fraction = 0.73 | Rho = 0.13237289551367823 ; p = 0.02227922201118654
Fraction = 0.75 | Rho = 0.16300275363352243 ; p = 0.004717779535792638
Fraction = 0.77 | Rho = 0.1654088122634874 ; p = 0.004130926552670629
Fraction = 0.79 | Rho = 0.14987674311575683 ; p = 0.00886481076374894
Fraction = 0.81 | Rho = 0.14130237734773532 ; p = 0.011919438643686176
Fraction = 0.83 | Rho = 0.1462715719228634 ; p = 0.009217085164184352
Fraction = 0.85 | Rho = 0.1434644566586587 ; p = 0.010243253465463456
Testing local objective fractions to 0.77...
Fraction = 0.76 | Rho = 0.15531657586600148 ; p = 0.007128666937148176
Fraction = 0.78 | Rho = 0.15220140666126936 ; p = 0.008385013921449185
Context-specific metabolism fit with 0.77 of optimal objective flux
Max fit RIPTiDe completed in, 4 minutes and 33 seconds
```

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.

### Resulting RIPTiDe object (class) properties:

The resulting object is a container for the following data structures.

**model**- Contextualized genome-scale metabolic network reconstruction**transcriptome**- Transcriptomic abundances provided by user**percent_of_mapping**- Percent of genes in mapping file found in input GENRE**minimization_coefficients**- Linear coefficients used during flux sum minimization**maximization_coefficients**- Linear coefficients for each reaction based used during flux sampling**pruned**- Dictionary containing the IDs of genes, reactions, and metabolites pruned by RIPTiDe**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**metabolic_tasks**- User defined reactions whose activity is saved from pruning**concordance**- Spearman correlation results between linear coefficients and median fluxes from sampling**gpr_integration**- Whether GPR rules were considered during assignment of linear coefficients**defined_coefficients**- Range of linear coefficients RIPTiDe is allowed to utilize provided as a list**included_important**- Reactions or Genes included in the final model which the user defined as important**additional_parameters**- Dictionary of additional parameters RIPTiDe uses

### Additional maxfit-only RIPTiDe object (class) properties:

**fraction_bounds**- Minimum and maximum values for the range of objective flux minimum fractions tested**fraction_step**- Increment for series of objective flux minima created within fraction bound range

**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

## 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 along with a detailed description.

Distributed under the terms of the MIT license, "riptide" is free and open source software

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