UNIPred

UNIPred [1] is an unbalance-aware network integration method to construct a more reliable and informative  composite network for the automated function prediction of proteins. UNIPred assigns a weight (real number in [0, 1] interval) to the input network according to its 'informativeness' with respect to a given functional class, taking into account the unbalance between annotated and unannotated proteins.

        

       

UNIPred is also available as web tool.

UNIPred code

The source code can be downloaded here. The archive contains two files:

• UNIpred.R:                            R code implementing the UNIPred integration method
• UNIpred_optimization.c:       C source code implementing the core optimization procedure of UNIPred

R CMD SHLIB UNIPred_optimization.c    

Usage of UNIPred: example with yeast networks

We consider a simple example to integrate two networks for the prediction of the FunCat [2] category "01" (Metabolism) in the yeast model organism.

First we must load the UNIPred code in memory:

> source("UNIPred.R")

We use yeast data from the CRAN package 'bionetdata' [3]. We start with binary protein-protein interaction data (Yeast.STRING.data) from the STRING data base [4]; in the bionetdata package these data are represented through a binary named matrix. Names correspond to systematic names of yeast genes. Yeast.STRING.FunCat represents FunCat annotations through a binary matrix  for the genes included in Yeast.STRING.data . Annotations refer to the funcat-2.1 scheme, available from the MIPS web site:

> library(bionetdata); > data(Yeast.STRING.FunCat); > data(Yeast.STRING.data); > labels <- Yeast.STRING.FunCat; > labels[labels == 0] <- -1; > labels <- labels[, -which(colnames(labels)=="00")]; # excluding the dummy "00" root > W <- unlist(Yeast.STRING.data); > proteins <- rownames(W); > labeling <- labels[proteins, 1]; # first class corresponding to "01" (Metabolism) > names(labeling) <- proteins; > w1 <- UNIPred(W,labeling)

Then we consider binary protein-protein interactions (Yeast.Biogrid.data)  downloaded from the BioGRID database [5], that collects PPI data from both high-throughput studies and conventional focused studies :

> data(Yeast.Biogrid.data) > data(Yeast.Biogrid.FunCat) > labels <- Yeast.Biogrid.FunCat; > labels[labels == 0] <- -1; > labels <- labels[, -which(colnames(labels)=="00")]; # excluding the dummy "00" root > W2 <- unlist(Yeast.Biogrid.data); > proteins2 <- rownames(W2); > labeling <- labels[proteins2, 1]; # first class > names(labeling) <- proteins2; > w2 <- UNIPred(W2,labeling)

Once obtained the UNIPred weights for both the networks, each of the considered networks is extended by including the union of the proteins, and then a weighted integration is performed by using the weights computed by UNIPred:

> totprot <- union(proteins, proteins2) > n <- length(totprot) > ext.W <- matrix(0, nrow=n, ncol=n); > rownames(ext.W) <- colnames(ext.W) <- totprot; > n <- length(totprot) > ext.W2 <- matrix(0, nrow=n, ncol=n); > rownames(ext.W2) <- colnames(ext.W2) <- totprot; > ext.W[proteins,proteins]<-W; > ext.W2[proteins2,proteins2]<-W2; > integrated.W<- w1*ext.W + w2*ext.W2

It is easy to extend this procedure by adding other networks, computing the weights associated to each network through UNIPred. Finally the integrated network can be given as input to a graph-based algorithm (e.g. COSNet [6]) to predict whether the unlabeled nodes/proteins of the network belong to the functional class under study.

References