https://github.com/michaelwitting/lipidnetworkpredictr

Model based prediction of C. elegans lipids
https://github.com/michaelwitting/lipidnetworkpredictr

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Model based prediction of C. elegans lipids

• Host: GitHub
• URL: https://github.com/michaelwitting/lipidnetworkpredictr
• Owner: michaelwitting
• Created: 2019-06-02T15:09:43.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-10-29T09:39:19.000Z (2 months ago)
• Last Synced: 2024-10-29T11:45:53.398Z (2 months ago)
• Language: R
• Size: 3.49 MB
• Stars: 0
• Watchers: 3
• Forks: 1
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

        
# LipidNetworkPredictR

## Overview 

Genome scale metabolic networks typically do not represent lipid metabolism in 

full detail. This package aims to use template reactions to generate a lipid 

reaction network at resolution of acyl chains.

## Installation

To install _LipidNetworkPredictR_ from GitHub, install the package via `devtools`:

```r 

if (!requireNamespace("devtools", quietly = TRUE))

    install.packages("devtools")

library(devtools)

install_github("michaelwitting/LipidNetworkPredictR")

```

The code in the Github repository is a place where we publicly disclose our 

development process.

## Quick start

```{r}

library("LipidNetworkPredictR")

## define the fatty acids that the reactions will be built upon

FA <- c("FA(14:0(12Me))", "FA(16:0(14Me))", "FA(15:1(9Z)(14Me))",        

	"FA(17:0(16Me))", "FA(12:0(11Me))", "FA(13:0(12Me))", "FA(14:0(13Me))",

	"FA(15:0(14Me))", "FA(16:0(15Me))", "FA(12:0)", "FA(14:0)")

## create data.frame with reactions and reaction order, for illustrative

## reasons this will only represent a small subset of the actual lipid 

## metabolism

reactions <- rbind(

	c(1, "RHEA:15421", "M_ATP + M_CoA + M_FA = M_PPi + M_AMP + M_AcylCoA", FALSE),

	c(2, "RHEA:15325", "M_Glycerol-3-P + M_AcylCoA = M_CoA + M_LPA", FALSE),

	c(3, "RHEA:19709", "M_LPA + M_AcylCoA = M_CoA + M_PA", FALSE),

	c(4, "RHEA:27429", "M_H2O + M_PA = M_Pi + M_1,2-DG", FALSE)

)

reactions <- data.frame(order = reactions[, 1], reaction_RHEA = reactions[, 2],

	reaction_formula = reactions[, 3], directed = reactions[, 4])

reactions$order <- as.numeric(reactions$order)

reactions$directed <- as.logical(reactions$directed)

## run the function

reaction_l <- create_reactions(substrates = list(FA = FA), reactions = reactions)

## create the adjacency matrix

adj <- create_reaction_adjacency_matrix(reaction_l)

```

The `adj` object can be further analyzed in subsequent analysis (e.g. by using `igraph`).