reconstructGsMN: Reconstuction of the specific-organism genome-scale metabolic...
In RevEcoR: Reverse Ecology Analysis on Microbiome

Description Usage Arguments Details Value See Also Examples

Reconstruction of genome-scale metabolic network (GsMN) whose nodes represents compounds and whose edges represents reactions.

1 2	reconstructGsMN(metabolic.data, RefData = RefDbcache, threshold = 10, is.gaint = TRUE)

`metabolic.data,`	df or a character vector. More details see function `getOrgMetabolicData` and `details`
`RefData`	The reference metabolic data. It does not need reference data While organism metabolic data was collected from KEGG database, and RefData is set to NULL. Otherwise, RefDbCache, an internal dataset in this package, was taken as the Reference metabolic data for Genome scale metabolic reconstruction.
`threshold`	numeric, Nodes belonging to components with fewer than the value of threshold nodes will be ignored. This is a good option for networks that contain many small and trivial components. Default is 10.
`is.gaint`	logical, Ignore all nodes except those in the giant component: selecting the only main largest component (connected set of nodes) of the network. All smaller components will be ignored. This is a good option for networks with a dominant component. Default is TRUE.

The input of this function can be of two forms. If organims is collected in KEGG database, it can be obtained with getOrgMetabolicData which is a data frame. Otherwise, metabolic.data could be a character vecotr which contains the KEGG Orthology annotated information on this organism, e.g. we can download this KO annotation profile in the https://img.jgi.doe.gov website for species detected in a human microbime which not contained in KEGG organism database. Several functions, such as link{read.table} and read.delim could help us to read KO annotation profile.

igraph object

getOrgMetabolicData

## not run (organism in KEGG)
## metabolic.data <- getOrgMetabolicData("buc")
## g <- reconstructGsMN(metabolic.data)

## species detected in a human microbiome
annodir <- system.file("extdata","koanno.tab",package = "RevEcoR")
metabolic.data <- read.delim2(file=annodir,stringsAsFactors=FALSE)
##load the reference metabolic data
data(RefDbcache)
g2 <- reconstructGsMN(metabolic.data, RefData = RefDbcache)