sirusb

# rtracklayer يمكن استعمال الدوال الجاهزة في حزمة
encodePilotRegions <- import.bed("encodePilotRegion.bed")
encodePilotRegions
#GRanges object with 44 ranges and 1 metadata column:
#       seqnames                 ranges strand   |        name
#          <Rle>              <IRanges>  <Rle>   | <character>
#   [1]     chr1 [151158061, 151658060]      *   |      ENr231
#   [2]     chr2 [ 51658705,  52158704]      *   |      ENr112
#   [3]     chr2 [118294574, 118794573]      *   |      ENr121
#   [4]     chr2 [220277346, 220777345]      *   |      ENr331

sirusb

require(pasillaBamSubset)
require(GenomicAlignments)
# BAM نقراء الملف 
bamFile <- untreated1_chr4()
gReads <- readGAlignmentsFromBam(bamFile)
#GAlignments object with 204355 alignments and 0 metadata columns:
#           seqnames strand       cigar    qwidth     start       end     width     njunc
#              <Rle>  <Rle> <character> <integer> <integer> <integer> <integer> <integer>
#       [1]     chr4      -         75M        75       892       966        75         0
#       [2]     chr4      -         75M        75       919       993        75         0

sirusb

require(pasillaBamSubset)
require(Rsamtools)
bamFile <- untreated1_chr4()
region <- GRanges("chr4",IRanges(100000L, 110000L))
param <- ScanBamParam(which=region, what=scanBamWhat())
mappedReads <- scanBam(bamFile, param=param)

sirusb

require(RNAseqData.HNRNPC.bam.chr14)
require(pasillaBamSubset)
require(Rsamtools)

bamFile <- untreated1_chr4()
# نلاحظ أن المجموعة اس هي المتواجدة هنا مما يدل على ان السلاسل احادية النهاية
quickBamFlagSummary(bamFile, main.groups.only = TRUE)
#                                group |    nb of |    nb of | mean / max
#                                   of |  records |   unique | records per
#                              records | in group |   QNAMEs | unique QNAME

sirusb

#Usage example:
#EntrezID<-c("2114","9757","5886","9373","6921","4088","7006","6196","10054","10945")
#EnsemblID<-EntrezToEnsembl(EntrezID)

EntrezToEnsembl<-function(EntrezID){
  require(biomaRt);
  ensemble<-useMart("ensembl");
  hsp<-useDataset(mart=ensemble,dataset="hsapiens_gene_ensembl");
  ids<-getBM(filters= "entrezgene",
              attributes= c("entrezgene","ensembl_gene_id", "external_gene_id","description"),

sirusb

 DoDAVIDGOAnnotation <-function(topics,pval=0.05,GOlimit=5){
    
    clusters<-1:ncol(topics)
    
    Groups<-c();
    Annot<-c();
    pvalue<-c();
    counts<-c();
    for(clus in clusters){
        #Get the genes in that cluster