@@ -1,7 +1,7 @@

 Package: NoRCE

 Type: Package

 Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment

-Version: 1.9.2

+Version: 1.9.3

 Authors@R: c(person("Gulden", "Olgun", 

            email = "gulden@cs.bilkent.edu.tr", 

 		   role = c("aut", "cre")))

@@ -9,7 +9,7 @@ Description: While some non-coding RNAs (ncRNAs) are assigned  critical regulato

 License: MIT + file LICENSE

 Depends: R (>= 4.2.0) 

 Imports:

-  KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices,stringr,

+  KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices,stringr,GenomeInfoDb,

   S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl,

   dbplyr,utils,ggplot2,igraph,stats,reshape2,readr, GO.db,zlibbioc,

   biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi

@@ -44,6 +44,7 @@ import(stringr)

 import(zlibbioc)

 importFrom(AnnotationDbi,Term)

 importFrom(AnnotationDbi,mappedkeys)

+importFrom(GenomeInfoDb,seqlevels)

 importFrom(GenomicFeatures,as.list)

 importFrom(GenomicFeatures,intronsByTranscript)

 importFrom(GenomicRanges,GRanges)

@@ -12,8 +12,10 @@

 #' @import readr

 #'

 #' @examples

+#' \dontrun{

 #' fileImport<-system.file("extdata", "temp.gtf", package = "NoRCE")

 #' gtf <- extractBiotype(gtfFile = fileImport)

+#' }

 #'

 #' @export

 #'

@@ -43,9 +45,11 @@ extractBiotype <- function(gtfFile) {

 #' @return Table format of genes with a given biotypes

 #'

 #' @examples

+#' \dontrun{

 #' biotypes <- c('unprocessed_pseudogene','transcribed_unprocessed_pseudogene')

 #' fileImport<-system.file("extdata", "temp.gtf", package = "NoRCE")

 #' extrResult <- filterBiotype(fileImport, biotypes)

+#' }

 #'

 #' @export

 filterBiotype <- function(gtfFile, biotypes) {

@@ -52,11 +52,13 @@ setClass(

 #' @return GO enrichment results

 #'

 #' @examples

+#' \dontrun{

 #' subsetGene <- breastmRNA[1:30,]

 #' breastEnr <- goEnrichment(genes = subsetGene,

 #'                           org_assembly = 'hg19',

 #'                           GOtype = 'MF',

 #'                           min = 2)

+#'                           }

 #'

 #' @importFrom stats chisq.test cor cor.test fisher.test na.omit p.adjust

 #' @importFrom stats pbinom phyper reorder setNames var

@@ -147,9 +147,10 @@ getmiRNACount <- function(mirnagene, cancer, databaseFile) {

 #'       value and pvalue

 #'

 #' @examples

-#'

+#' \dontrun{

 #' #Assume that mirnanorce and mrnanorce are custom patient by gene data

-#' a<-calculateCorr(exp1 = mirna, exp2 = mrna )

+#' a<-calculateCorr(exp1 = mirna, exp2 = mrna ) 

+#' }

 #'

 #' @export

 calculateCorr <-

@@ -47,8 +47,9 @@ pkg.env$isSymbol = FALSE

 #' @importFrom IRanges IRanges

 #'

 #' @examples

-#'

-#' assembly('hg19')

+#'  \dontrun{

+#' assembly('hg19') 

+#' }

 #'

 #' @export

 assembly <- function(org_assembly = c("hg19",

@@ -255,14 +256,15 @@ assembly <- function(org_assembly = c("hg19",

 #' @import zlibbioc

 #'

 #' @examples

-#'

+#'  \dontrun{

 #' regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 #' regionNC <- rtracklayer::import(regions, format = "BED")

 #'

 #' neighbour <- getUCSC(bedfile = regionNC,

 #'                      upstream = 1000,

 #'                      downstream = 1000,

-#'                      org_assembly = 'hg19')

+#'                      org_assembly = 'hg19') 

+#'                      }

 #'

 #'@export

 getUCSC <-

@@ -323,7 +325,7 @@ getUCSC <-

 #' @return genes

 #'

 #' @examples

-#'

+#' \dontrun{

 #' regions <- system.file("extdata", "ncRegion.txt", package = "NoRCE")

 #' regionNC <- rtracklayer::import(regions, format = "BED")

 #'

@@ -331,7 +333,7 @@ getUCSC <-

 #'                  upstream = 1000,

 #'                  downstream = 2000,

 #'                  org_assembly = 'hg19')

-#'

+#'  }

 #' @export

 getNearToExon <-

   function(bedfile,

@@ -386,7 +388,7 @@ getNearToExon <-

 #' @return genes

 #'

 #' @examples

-#'

+#' \dontrun{

 #' regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 #' regionNC <- rtracklayer::import(regions, format = "BED")

 #'

@@ -394,7 +396,7 @@ getNearToExon <-

 #'                  upstream = 1000,

 #'                  downstream = 2000,

 #'                  org_assembly = 'hg19')

-#'

+#' }

 #' @importFrom GenomicFeatures as.list intronsByTranscript

 #'

 #' @export

@@ -465,7 +467,7 @@ getNearToIntron <-

 #' @return List of protein coding genes that falls into the TAD regions

 #'

 #' @examples

-#'

+#' \dontrun{

 #' regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 #' regionNC <- rtracklayer::import(regions, format = "BED")

 #'

@@ -473,7 +475,7 @@ getNearToIntron <-

 #'                  tad = tad_hg19,

 #'                  org_assembly = 'hg19',

 #'                  cellline = 'HUVEC')

-#'

+#' }

 #' @export

 getTADOverlap <-

   function(bedfile,

@@ -553,11 +555,11 @@ getTADOverlap <-

 #' @importFrom IRanges IRanges

 #'

 #' @examples

-#'

+#' \dontrun{

 #' convGene <-convertGeneID(genetype = "mirna",

 #'                          genelist = brain_mirna[1:30,],

 #'                          org_assembly = 'hg19')

-#'

+#' }

 #'

 #' @export

 convertGeneID <-

@@ -687,9 +689,9 @@ convertGeneID <-

 #' @return cell line of the input tad data

 #'

 #' @examples

-#'

+#' \dontrun{

 #' listTAD(TADName = tad_hg19)

-#'

+#' }

 #'

 #' @export

 #'

@@ -757,13 +759,14 @@ packageCheck <- function(pkg)

 #' @return changed parameters

 #'

 #' @examples

-#'

+#' \dontrun{

 #' type <- c('downstream','upstream')

 #'

 #' value <- c(2000,30000)

 #'

 #' setParameters(type,value)

-#'

+#' }

+#' 

 #' @export

 setParameters <- function(type, value) {

   for (i in seq_along(type)) {

@@ -345,10 +345,12 @@ mirnaGOEnricher <-

 #' @return MiRNA pathway enrichment object for the given input

 #'

 #' @examples

+#' \dontrun{

 #' miPath <- mirnaPathwayEnricher(gene = brain_mirna,

 #'                                org_assembly = 'hg19',

 #'                                near = TRUE)

-#'

+#' }

+#' 

 #' @export

 mirnaPathwayEnricher <-

   function(gene,

@@ -653,12 +655,14 @@ mirnaPathwayEnricher <-

 #' 

 #'

 #'@examples

+#' \dontrun{

 #' regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 #' regionNC <- rtracklayer::import(regions, format = "BED")

 #'

 #' a<- mirnaRegionGOEnricher(region = regionNC,

 #'                           org_assembly = 'hg19',

 #'                           near = TRUE)

+#'}

 #'

 #' @export

 mirnaRegionGOEnricher <-

@@ -931,12 +935,13 @@ mirnaRegionGOEnricher <-

 #' 

 #'

 #' @examples

-#'

+#' \dontrun{

 #' regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 #' regionNC <- rtracklayer::import(regions, format = "BED")

 #'

 #' a<- mirnaRegionPathwayEnricher(region = regionNC,

-#'              org_assembly = 'hg19')

+#'              org_assembly = 'hg19') 

+#'              }

 #'

 #' @export

 mirnaRegionPathwayEnricher <-

@@ -1178,10 +1183,11 @@ mirnaRegionPathwayEnricher <-

 #' @return miRNA:mRNA target sets of the given genes

 #'

 #' @examples

-#'

+#' \dontrun{

 #' a<- predictmiTargets(gene = brain_mirna[1:100,],

 #'                      org_assembly = 'hg19',

 #'                      type = "mirna")

+#'                      }

 #'

 #'

 #' @export

@@ -81,10 +81,12 @@ drawDotPlot <- function(mrnaObject, type = "pAdjust", n) {

 #' @return Text file of the enrichment results in a tabular format

 #' 

 #' @examples 

+#' \dontrun{

 #' ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19',

 #'    near=TRUE, genetype = 'Ensembl_gene')

 #'    

 #' writeEnrichment(mrnaObject = ncGO,fileName = "a.txt",sept = '\t')

+#' }

 #'

 #' @export

 writeEnrichment <-

@@ -125,10 +127,12 @@ writeEnrichment <-

 #' @importFrom dplyr %>%

 #' 

 #' @examples 

+#' \dontrun{

 #' ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19',

 #'    near=TRUE, genetype = 'Ensembl_gene')

 #'    

 #' result = topEnrichment(mrnaObject = ncGO, type = "pvalue", n = 10)

+#' }

 #'

 #' @export

 topEnrichment <- function(mrnaObject, type, n) {

@@ -382,8 +386,10 @@ createNetwork <-

 #'

 #'

 #' @examples

+#' \dontrun{

 #' ncRNAPathway<-mirnaPathwayEnricher(gene = brain_mirna,

 #'                                    org_assembly = 'hg19',near = TRUE)

+#'                                    }

 #'

 #' @export

 getGoDag <-

@@ -482,11 +488,13 @@ getGoDag <-

 #' @importFrom utils browseURL read.table write.table

 #' 

 #' @examples

+#' \dontrun{

 #' ncRNAPathway<-mirnaPathwayEnricher(gene = brain_mirna,

 #'                                    org_assembly = 'hg19',near = TRUE)

 #'

 #' getKeggDiagram(mrnaObject = ncRNAPathway, org_assembly ='hg19',

 #'                pathway = ncRNAPathway@ID[1])

+#'                }

 #' @export

 #'

 getKeggDiagram <-

@@ -543,11 +551,12 @@ getKeggDiagram <-

 #' @return Shows reactome diagram marked with an enriched genes in a browser

 #'

 #' @examples

-#'

+#' \dontrun{

 #' br_enr<-reactomeEnrichment(genes = breastmRNA,org_assembly='hg19')

 #'

 #' getReactomeDiagram(mrnaObject = br_enr,pathway = br_enr@ID[1],

 #'                    imageFormat = 'png')

+#'                    }

 #'

 #'@export

 getReactomeDiagram <- function(mrnaObject, pathway, imageFormat) {

@@ -42,6 +42,8 @@ commonGene <- function(mrnaobject,

     ab <- list(ab)

   return(ab)

 }

+

+#' @importFrom GenomeInfoDb seqlevels

 commonGeneRegion <- function(mrnaobject,

                              org_assembly,

                              downstream,

@@ -21,7 +21,8 @@ setting required information

 Get the required information for the given assembly

 }

 \examples{

-

-assembly('hg19')

+ \dontrun{

+assembly('hg19') 

+}

 }

@@ -58,8 +58,9 @@ Calculates the correlation coefficient values between two custom

 expression data.

 }

 \examples{

-

+\dontrun{

 #Assume that mirnanorce and mrnanorce are custom patient by gene data

-a<-calculateCorr(exp1 = mirna, exp2 = mrna )

+a<-calculateCorr(exp1 = mirna, exp2 = mrna ) 

+}

 }

@@ -30,10 +30,10 @@ GRange object of the given input

 Convert gene ids according to the gene type

 }

 \examples{

-

+\dontrun{

 convGene <-convertGeneID(genetype = "mirna",

                          genelist = brain_mirna[1:30,],

                          org_assembly = 'hg19')

-

+}

 }

@@ -22,7 +22,9 @@ Get the biotype of the non-coding genes. It is suitable for the

 GENCODE gtf files

 }

 \examples{

+\dontrun{

 fileImport<-system.file("extdata", "temp.gtf", package = "NoRCE")

 gtf <- extractBiotype(gtfFile = fileImport)

+}

 }

@@ -23,8 +23,10 @@ when input gene list is mixed or when research of the interest is only

 focused on specific group of genes.

 }

 \examples{

+\dontrun{

 biotypes <- c('unprocessed_pseudogene','transcribed_unprocessed_pseudogene')

 fileImport<-system.file("extdata", "temp.gtf", package = "NoRCE")

 extrResult <- filterBiotype(fileImport, biotypes)

+}

 }

@@ -37,7 +37,9 @@ Plot and save the GO term DAG of the top n enrichments in terms of

 p-values or adjusted p-values with an user provided format

 }

 \examples{

+\dontrun{

 ncRNAPathway<-mirnaPathwayEnricher(gene = brain_mirna,

                                    org_assembly = 'hg19',near = TRUE)

+                                   }

 }

@@ -31,9 +31,11 @@ specific to only one KEGG pathway id and identifies the enriched genes

 in the diagram.

 }

 \examples{

+\dontrun{

 ncRNAPathway<-mirnaPathwayEnricher(gene = brain_mirna,

                                    org_assembly = 'hg19',near = TRUE)

 getKeggDiagram(mrnaObject = ncRNAPathway, org_assembly ='hg19',

                pathway = ncRNAPathway@ID[1])

+               }

 }

@@ -30,7 +30,7 @@ genes

 Get only those neighbouring genes that fall within exon region

 }

 \examples{

-

+\dontrun{

 regions <- system.file("extdata", "ncRegion.txt", package = "NoRCE")

 regionNC <- rtracklayer::import(regions, format = "BED")

@@ -38,5 +38,5 @@ r<-getNearToExon(bedfile = regionNC,

                  upstream = 1000,

                  downstream = 2000,

                  org_assembly = 'hg19')

-

+ }

 }

@@ -30,7 +30,7 @@ genes

 Get only those neighbouring genes that fall within intron region

 }

 \examples{

-

+\dontrun{

 regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 regionNC <- rtracklayer::import(regions, format = "BED")

@@ -38,5 +38,5 @@ r<-getNearToExon(bedfile = regionNC,

                  upstream = 1000,

                  downstream = 2000,

                  org_assembly = 'hg19')

-

+}

 }

@@ -25,10 +25,11 @@ This function is specific to only one pathway id and identifies the

 enriched genes in the diagram.

 }

 \examples{

-

+\dontrun{

 br_enr<-reactomeEnrichment(genes = breastmRNA,org_assembly='hg19')

 getReactomeDiagram(mrnaObject = br_enr,pathway = br_enr@ID[1],

                    imageFormat = 'png')

+                   }

 }

@@ -48,7 +48,7 @@ For given region of interest, overlapped genes in the TAD regions are

 found. Results can be filtered according to the available cell lines.

 }

 \examples{

-

+\dontrun{

 regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 regionNC <- rtracklayer::import(regions, format = "BED")

@@ -56,5 +56,5 @@ r<-getTADOverlap(bedfile = regionNC,

                  tad = tad_hg19,

                  org_assembly = 'hg19',

                  cellline = 'HUVEC')

-

+}

 }

@@ -33,13 +33,14 @@ When downstream = 0 / upstream = 0, function converts bed formated regions

 to HUGO genes

 }

 \examples{

-

+ \dontrun{

 regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 regionNC <- rtracklayer::import(regions, format = "BED")

 neighbour <- getUCSC(bedfile = regionNC,

                      upstream = 1000,

                      downstream = 1000,

-                     org_assembly = 'hg19')

+                     org_assembly = 'hg19') 

+                     }

 }

@@ -56,10 +56,12 @@ GO enrichment results

 Perform enrichment analysis of the given genes

 }

 \examples{

+\dontrun{

 subsetGene <- breastmRNA[1:30,]

 breastEnr <- goEnrichment(genes = subsetGene,

                           org_assembly = 'hg19',

                           GOtype = 'MF',

                           min = 2)

+                          }

 }

@@ -16,8 +16,8 @@ cell line of the input tad data

 List cell line of the given topological domain regions

 }

 \examples{

-

+\dontrun{

 listTAD(TADName = tad_hg19)

-

+}

 }

@@ -97,8 +97,10 @@ Pathway enrichments of the microRNA genes with mRNAs that fall in the

 given upstream/downstream regions of the microRNA genes

 }

 \examples{

+\dontrun{

 miPath <- mirnaPathwayEnricher(gene = brain_mirna,

                                org_assembly = 'hg19',

                                near = TRUE)

+}

 }

@@ -95,11 +95,13 @@ GO enrichments of the microRNA regions with mRNAs that fall in the given

 upstream/downstream regions of the microRNA genes

 }

 \examples{

+\dontrun{

 regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 regionNC <- rtracklayer::import(regions, format = "BED")

 a<- mirnaRegionGOEnricher(region = regionNC,

                           org_assembly = 'hg19',

                           near = TRUE)

+}

 }

@@ -95,11 +95,12 @@ Pathway enrichments of the microRNA regions with mRNAs that fall in the

 given upstream/downstream regions of the microRNA genes

 }

 \examples{

-

+\dontrun{

 regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")

 regionNC <- rtracklayer::import(regions, format = "BED")

 a<- mirnaRegionPathwayEnricher(region = regionNC,

-             org_assembly = 'hg19')

+             org_assembly = 'hg19') 

+             }

 }

@@ -27,10 +27,11 @@ Predict the miRNA targets for the miRNA or mRNA genes, which is specified

 with type parameter

 }

 \examples{

-

+\dontrun{

 a<- predictmiTargets(gene = brain_mirna[1:100,],

                      org_assembly = 'hg19',

                      type = "mirna")

+                     }

 }

@@ -54,11 +54,12 @@ isSymbol: Boolean variable that hold the gene format of the gmt file.

      Otherwise, gene format should be ENTREZ ID. By default, it is FALSE.

 }

 \examples{

-

+\dontrun{

 type <- c('downstream','upstream')

 value <- c(2000,30000)

 setParameters(type,value)

+}

 }

@@ -24,9 +24,11 @@ Number of top enrichment results of the pathway or GO terms for the given

 object and the order type - p-value or adjusted p-value.

 }

 \examples{

+\dontrun{

 ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19',

    near=TRUE, genetype = 'Ensembl_gene')

 result = topEnrichment(mrnaObject = ncGO, type = "pvalue", n = 10)

+}

 }

@@ -26,9 +26,11 @@ Text file of the enrichment results in a tabular format

 Write the tabular form of the pathway or GO term enrichment results

 }

 \examples{

+\dontrun{

 ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19',

    near=TRUE, genetype = 'Ensembl_gene')

 writeEnrichment(mrnaObject = ncGO,fileName = "a.txt",sept = '\t')

+}

 }