NoRCE provides a comprehensive, systematical, user-friendly pipeline for preprocessing, annotation, and enrichment of all types of ncRNA genes such as lncRNA, mature and precursor miRNA, snoRNA, siRNA, circular RNA. NoRCE analyses are based on spatially proximal mRNAs at a certain distance for a set of non-coding RNA genes or regions of interest. Moreover, specific analyses such as biotype filtering, miRNA-mRNA co-expression analysis, miRNA target predictions can be performed for filtering. Besides, it allows curating the gene set according to the topologically associating domain (TAD) boundaries. NoRCE accepts gene lists and gene regions as an input.
NoRCE supports many organisms and assemblies. To work with supported organisms, the user has to provide the organism assembly,
org_assembly, with a keyword that is listed in the table.
To install and load NoRCE:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("NoRCE")
To load the NoRCE:
Moreover, NoRCE can be downloaded from the GitHub repository.
NoRCE gives freedom to the user. However, to prevent getting lost in parameters, most of them are set to their default value. To change those parameters, which are listed in the Default Parameters section,
setParameters function should be called, and parameters and their user-defined values should be defined. The table in the Default Parameters section illustrates the possible parameters and their values.
type <- c('downstream','upstream') value <- c(2000,30000) setParameters(type,value) #To change the single parameter setParameters("pathwayType","reactome")
GO enrichment analysis can be performed based on gene neighborhood, predicted targets, co-expression values, and/or considering topological domain boundaries. HUGO, ENSEMBL gene, ENSEMBL transcript gene, ENTREZ ID, and miRBase names are supported formats for the input gene list. Moreover, NoRCE accepts a list of genomic regions. The input genomic region list should be in a BED format.
Each analysis is controlled by corresponding parameters. When related parameters are set, the gene sets resulting from the intersection of those analyses will be considered for enrichment analysis. However, co-expression analysis can be augmented with other analyses.
GO enrichment pipeline analysis is carried out with
geneRegionGOEnricher functions for an input gene set and regions, respectively. Also, the miRNA gene enrichment pipeline is carried out with
mirnaRegionGOEnricher functions. However, the user can use other functions for performing subparts of the analysis. Species assembly must be defined using the
org_assembly parameter, which is provided in the Keyword column in the table above. NoRCE allows the user to use a background gene set.
near parameter is set to
TRUE, the closest genes for the input gene list are retrieved. The gene neighborhood taken into consideration is controlled by the
downstream parameters. By default, those parameters are set to 10 kb and all genes that fall into 10 kb upstream and downstream of the input genes are retrieved. Also, using
searchRegion parameter, the analysis can be performed for only those genes whose exon or intron regions fall into the specified upstream and downstream range of the input genes.
To perform enrichment analysis based on considering only gene neighborhood:
#Set the neighbourhood region as exon ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19', near=TRUE, genetype = 'Ensembl_gene')
To perform enrichment analysis based on exon neighbourhood:
#Set the neighbourhood region as exon setParameters("searchRegion", "exon") #NoRCE automatically consider only the exon of the genes ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19', near=TRUE, genetype = 'Ensembl_gene')
NoRCE accepts the BED formatted input gene regions. To perform the enrichment analysis on gene regions:
#Change back to all search regions setParameters("searchRegion", "all") #Import the gene set regions regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE") regionNC <- import(regions, format = "BED") #Perform the analysis on the gene regions regionGO<-geneRegionGOEnricher(region = regionNC, org_assembly= 'hg19', near = TRUE)
For a set of miRNA genes, the user can filter the gene set using miRNA target predictions. This analysis is controlled by the
target parameter. Once the
target parameter is set to
TRUE, TargetScan predictions are used to curate the gene list that will be enriched.
To performed GO enrichment based on neighboring coding genes of brain miRNA targeted by the same brain miRNA gene set.
mirGO<-mirnaGOEnricher(gene = brain_mirna, org_assembly='hg19', near=TRUE, target=TRUE)
Gene annotation based on topologically associating domain regions is conducted whether ncRNAs fall into the TAD boundaries and coding gene assignment only those that are in the same TAD boundaries are included in the neighborhood coding gene set. If cell-line(s) for TAD boundaries are specified, only regions that are associated with the given cell-line(s) are considered. User-defined and pre-defined TAD boundaries can be used to find potential gene setlist for enrichment. For human, mouse, and fruit fly, pre-defined TAD boundaries are supplied and custom TAD boundaries must be in a BED format. Cell-lines are controlled by the
cellline parameter. Cell-lines can be listed with the
To perform enrichment based on TAD boundaries that also fall into the noncoding gene's neighborhood
mirGO<-mirnaGOEnricher(gene = brain_mirna, org_assembly='hg19', near=TRUE, isTADSearch = TRUE, TAD = tad_hg19)
User-defined TAD regions can be used as an input for the TAD regions and gene enrichment can be performed based on these custom TAD regions.
TAD parameter is provided to input the bed formatted TAD regions.
# Read the custom TAD boundaries cus_TAD<-system.file("extdata", "DER-18_TAD_adultbrain.txt", package = "NoRCE") tad_custom <- import(cus_TAD, format = 'bed') # Use custom TAD boundaries for enrichment ncGO_tad <- geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', genetype = 'Ensembl_gene', isTADSearch = TRUE, TAD = tad_custom)
To retrieve the list of cell-lines from the given TAD boundaries:
a<-listTAD(TADName = tad_hg19)
Enrichment based on correlation analysis is conducted with the binary
express parameter. For a given cancer, a pre-calculated Pearson correlation coefficient between miRNA-mRNA and miRNA-lncRNA expressions can be used to augment or filter the results. Users can define the correlation coefficient cutoff and cancer of interest with
cancer parameter, respectively. miRCancer.db, the pre-computed miRNA-mRNA and miRNA-lncRNA correlation database, must be downloaded to the local and the path of the database must be given as an input to a
ncGO_tad <- geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', genetype = 'Ensembl_gene',near = TRUE, express = TRUE, databaseFile = '\\miRCancer\\miRCancer.db', cancer = 'GBMLGG')
Two custom-defined expression data can be utilized to augment or filter the coding genes that are found using the previous analysis. To make this option on,
isCustomExp parameter has to set
TRUE, and custom expressions should be inputted to the
exp2 parameters. Expression data must be patient by gene data and headers should be gene names. If no header is defined,
label2 must be used to define the headers. The correlation cutoff can be defined with the
To perform GO enrichment for the intersection of the miRNA targets and custom RNAseq expression of miRNA and mRNA data in NoRCE repository:
# miRNA targets and custom RNAseq expression of miRNA and mRNA are used miGO1 <- mirnaGOEnricher(gene = brain_mirna, org_assembly = 'hg19', target = TRUE, express = TRUE, isCustomExp = TRUE, exp1 = mirna, exp2 = mrna)
To augment the results,
isUnionCorGene parameter has to set to
# Union of miRNA targets and custom RNAseq expression of miRNA and mRNA is utilized for enrichment analysis miGO1 <- mirnaGOEnricher(gene = brain_mirna, org_assembly = 'hg19', target = TRUE, express = TRUE, isCustomExp = TRUE, exp1 = mirna, exp2 = mrna, isUnionCorGene = TRUE)
As in GO enrichment analysis, pathway enrichment analysis can be performed based on gene neighborhood, predicted targets, correlation coefficient, and/or topological domain analysis. Each parameter is controlled by the related parameters and HUGO, ENSEMBL gene, ENSEMBL transcript, ENTREZ ID, and miRBase name is supported for the input gene list. Non-coding genes can be annotated and enriched with KEGG, Reactome, and Wiki pathways.
geneRegionPathwayEnricher functions fulfill the pathway enrichment for the genes and regions expect the miRNA genes and for the miRNA
mirnaRegionPathwayEnricher is used. By default, KEGG enrichment is performed.
# KEGG enrichment is performed miPathway <- mirnaPathwayEnricher(gene = brain_mirna, org_assembly = 'hg19', near = TRUE, target = TRUE)
To perform Reactome and Wiki enrichment
# Change the pathway database setParameters("pathwayType","reactome") nc2 <- genePathwayEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', near = TRUE, genetype = 'Ensembl_gene') # Wiki pathway Enrichment # Change the pathway database type and multiple testing correction method type <- c('pathwayType', 'pAdjust') value<-c('wiki', 'bonferroni') setParameters(type,value) nc2 <- genePathwayEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', near = TRUE, genetype = 'Ensembl_gene')
Moreover, pathway enriched can be performed based on a custom GMT file. GMT file supports both gene format of ENTREZ ID, Symbol and it is controlled by the
setParameters("pathwayType","other") # Name of the gmt file in the local wp.gmt <- "custom.gmt" # GMT file should be on the working directory ncGO_bader <- genePathwayEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19',genetype = 'Ensembl_gene',gmtName = wp.gmt)
Users can work with only biotypes of interest or can extract some biotypes from the gene list of interest. To get the biotype specific gene list, which is annotated with given biotype(s),
filterBiotype function should be called. The path of the GTF file or the path of the zipped GTF file must be provided.
biotypes <- c('unprocessed_pseudogene','transcribed_unprocessed_pseudogene') #Temp.gft is a subset of GENCODE Long non-coding RNA gene annotation fileImport<-system.file("extdata", "temp.gtf", package = "NoRCE") extrResult <- filterBiotype(fileImport, biotypes)
Moreover, users can collect the input gene list's gene biotype relationships using
extractBiotype function from the given GTF formatted file.
#Extract biotype gene interactions from the given GTF formatted file fileImport<-system.file("extdata", "temp.gtf", package = "NoRCE") #Creates 2 dimensional input Gene-Biotype matrix gtf <- extractBiotype(gtfFile = fileImport)
NoRCE allows us to visualize the enrichment results for pathway and GO enrichments in several forms.
Users can write the results in a tabular format in a TXT file. To save the result in a tabular format,
writeEnrichment function should be called. Users can write sorted all or their defined number of enrichment results using the
n parameter. If the
n parameter is not defined, all results will be written. Results are sorted based on p-value or p-adjusted value. Users should use the
type parameter to identify their sort preferences. By default, results are sorted based on the p-adjusted value. By default, it is tab-separated but the users can use their preferred separator.
The tabular format contains those fields: GO/pathway ID, pathway/GO term, p-value, p-adjust, gene ratio, background gene ratio, number of enriched genes, annotated gene list with the enriched terms, input noncoding gene list that is annotated with enriched protein-coding genes.
writeEnrichment(mrnaObject = ncGO,fileName = "result.txt",sept = "\t",type = "pvalue") writeEnrichment(mrnaObject = ncGO,fileName = "result.txt",sept = "\t",type = "pvalue", n = 5)
Results can be visualized in the dot plot. Top enrichment results according to p-value or p-adjust value are provided, also, the number of annotated genes with the enriched term is supplied. Users have an option to select p-value or p-adjust value for visualization with the
type parameter also the
n parameter should be used for top enrichment results. Dot plots can be used for pathway and GO enrichment.
drawDotPlot(mrnaObject = ncGO, type = "pvalue", n = 25)
NoRCE generates gene - enriched terms network to present the relationship between terms and enriched results. Same with dot plots,
type parameter and
n parameter can be utilized. By default, nodes are the enriched terms and annotated genes, edges represent the relationship. However, the users can choose to generate a network where nodes input noncoding gene lists that are annotated with enriched protein-coding genes instead of protein-coding genes. This is control by the boolean
isNonCode parameter. If it is set to
TRUE, input noncoding genes are utilized. Also, by default, GO-term/pathway-term are used for network generation, but, GO IDs or pathway IDs can be used. This is control by the boolean
takeID parameter. If it is set to
TRUE, the naming decision of the GO/pathway node is GO ID-pathway ID. This network can be used for the pathway and GO enrichment findings.
createNetwork(mrnaObject = ncGO, type = 'pvalue', n = 2, isNonCode = TRUE)
NoRCE draws the GO directed acyclic graph for a given number of enrichments. Enriched GO terms are colored in terms of their p-value or p-adjust value. Users can download PNG or SVG formatted DAGs.
getGoDag(mrnaObject = ncGO,type = 'pvalue', n = 2,filename = 'dag.png', imageFormat = "png")
Reactome and KEGG pathway maps can be generated in a browser. Users should select the enriched pathway,
pathway. Even though the Reactome map is visualized in the browser, the user can select PNG or SVG as an image format with
getKeggDiagram(mrnaObject = ncRNAPathway, org_assembly ='hg19', pathway = ncRNAPathway@ID)
getReactomeDiagram(mrnaObject = ncRNAPathway, pathway = miGO1@ID, imageFormat = "png")
You can get the miRNA targets for a given input. The output is miRNA-mRNA target pairs.
target <- predictmiTargets(gene = brain_mirna[1:100,], org_assembly = 'hg19', type = "mirna")
NoRCE can also perform co-expression analysis between two datasets.
exp2 parameters take expression data and each column must contain gene expression. If the expression data does not contain the header information,
label2 parameter must be used for this information.
calculateCorr function calculates the variance of each gene's expression, and genes that vary lesser than the user-defined variance cutoff,
varCutoff, are excluded from the analysis. The users can select their preferred correlation method,
corrMethod, with an alternative hypothesis,
alternate for a given p-value, confidence and minimum correlation cut off,
corAnalysis<-calculateCorr(exp1 = mirna, exp2 = mrna )
NoRCE can identify the GO annotatıon for a given gene set. To only annotate the genes with GO terms,
annGO function should be called. The input gene list should be in the HUGO gene format.
geneList <- c("FOXP2","SOX4","HOXC6") annot <- annGO(genes = geneList, GOtype = 'BP',org_assembly = 'hg19')[]
User can detect closeby genes in a given upstream and downstream ranges.
neighbour <- getUCSC(bedfile = regionNC, upstream = 1000, downstream = 1000, org_assembly = 'hg19')
|Parameter||Possible or Example Values|
|upstream||Numeric Interval in bp, Ex: 100|
|downstream||Numeric Interval in bp. Ex: 100|
|GOtype||'BP', 'CC', 'MF'|
|pCut||Threshold value for the pvalue. Range between [0,1]. Ex: 0.05|
|pAdjCut||Threshold value for the adjusted pvalue. Range between [0,1]. Ex: 0.05|
|pAdjust||'holm','hochberg','hommel','bonferroni', 'BH', 'BY','fdr', 'none'|
|enrichTest||'hyper', 'binom', 'fisher', 'chi'|
|varCutoff||Variance cutoff. Ex: 0.05|
|minAbsCor||Cutoff value for correlation. It has to range between [-1,1]|
|pcut||P-value cut off for the correlation. EX: 0.05|
|conf||Confidence value for correlation. Ex: 0.95|
|min||Minimum number of genes for enrichment. Ex: 5|
|cellline||List of cell-lines in TAD boundaries.|
|corrMethod||'pearson', 'kendall', 'spearman'|
|alternate||'greater', 'two.sided', 'less'|
|express||TRUE, FALSE. By default it is FALSE|
|target||TRUE, FALSE. By default it is FALSE|
|near||TRUE, FALSE. By default it is FALSE|
|isTADSearch||TRUE, FALSE. By default it is FALSE|
|cancer||ACC, BLCA, BRCA, CESC, CHOL, COAD, COADREAD, DLBC, ESCA, GBMLGG, HNSC, KICH, KIPAN, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SARC,SKCM, STAD, STES, TGCT, THCA, THYM, UCEC, UCS, UVM|
Citation : Olgun G, Nabi A, Tastan O (2019). “NoRCE: Non-coding RNA sets cis enrichment tool.” bioRxiv, 663765.
@Credits: Gulden Olgun