Getting_started"

In MoonFinder: Identify Moonlighting ncRNAs

knitr::opts_chunk$set(
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Getting Started with the MoonFinder package

Lixin Cheng

2019-10-01

Overview:

MoonFinder is developed for the identification of moonlighting RNAs at the genome level based on the functional annotation and interactome data of ncRNAs and proteins. MoonFinder provides an extensive framework for the detection of protein modules and establishment of RNA-module associations.

Citation:

Identification and characterization of moonlighting long non-coding RNAs based on RNA and protein interactome. Bioinformatics, 2018.
A network-based algorithm for the identification of moonlighting non-coding RNAs and its application in sepsis. Briefings in Bioinformatics, 2019

Getting started:

Identify the moonlight lncRNAs

# BiocManager::install("org.Hs.eg.db") 
# BiocManager::install("clusterProfiler") 
# install.packages("MoonFinder") 
library(org.Hs.eg.db)
library(clusterProfiler)
library(MoonFinder)
data("ppi","simMatBP.Resnik","moduleList","rna2module")
rnaStatMat = moonlightingCoefficient(rna2module,simMatBP.Resnik)
head(rnaStatMat)

Set up the SDI threshold according to SDI distributions

sdi = as.numeric(rnaStatMat[,3])
dev.new(width=4, height=4)
hist(sdi,border=FALSE,xlab="MC",xlim=c(0,1),ylim=c(0,4),
    breaks=seq(0,1,0.02),prob=TRUE,main="Resnik")
lines(density(sdi),lwd=2,col="orange")
mlncRNA = rnaStatMat[sdi>0.4,1]

Illustration of the mediated modules for a given moonlighting ncRNA.

library(igraph)
g <- graph.edgelist(ppi,directed=FALSE)
moduleHeatmap("CRNDE",rna2module,moduleList,g)

Comparison of the functions enriched by different modules:

library(clusterProfiler)
dev.new(width=14, height=7)
moduleFunctionComparing("CRNDE",rna2module,moduleList)

MoonFinder documentation built on Oct. 30, 2019, 11:31 a.m.