# #——style-knitr eval = TRUE,呼应= FALSE,结果= "飞机 "-------------------- BiocStyle:乳胶 () ## ---- 设置,包括= FALSE,缓存= FALSE ------------------------------------- 库(knitr) opts_chunk美元集(out.width =“0.7 \ \ maxwidth”,fig.align = "中心 ") ## ---- load_bsgenome、消息= FALSE ------------------------------------------ 图书馆(BSgenome.Hsapiens.UCSC.hg38) g < BSgenome.Hsapiens.UCSC.hg38: BSgenome.Hsapiens.UCSC。hg38 # #——load_branchpointer消息= FALSE ------------------------------------- 库(分歧点)# #——read_exon_gtf消息= FALSE, eval = FALSE ------------------------------ # 外显子< - gtfToExons(“gencode.v26.annotation.gtf ") ## ---- read_exon_small、消息= FALSE ---------------------------------------- smallExons < -执行(“extdata”、“gencode.v26.annotation.small。gtf”,包= "分歧点”)外显子<——gtfToExons (smallExons) # #——show_introns消息= FALSE ------------------------------------------- queryIntronFile < -执行(“extdata”、“intron_example.txt”,包=“分歧点”)queryIntronTable <——read.delim (queryIntronFile)头(queryIntronTable) # #——read_introns消息= FALSE ------------------------------------------- queryIntron < - readQueryFile (queryIntronFile queryType =“地区”,exons = exons) head(queryIntron) ## ----make_intron, message=FALSE-------------------------------------------- queryIntronFromGTF <- makeBranchpointWindowForExons("ENSE00000939171.1"， idType = "exon_id"， exons = exons) head(queryIntronFromGTF) #用于多个id:queryIntronFromGTF < - makeBranchpointWindowForExons (c(“ENSE00000939171.1”、“ENSE00001814242.1”),idType =“exon_id”,外显子,外显子)头(queryIntronFromGTF) # #——predict_introns消息= FALSE ---------------------------------------- branchpointPredictionsIntron < - predictBranchpoints (queryIntron queryType =“地区”,BSgenome = g)头(branchpointPredictionsIntron) # #——brca2-plot ------------------------------------------------------------ plotBranchpointWindow (queryIntron $ id [2],branchpointPredictionsIntron probabilityCutoff = 0.52, plotMutated = FALSE, plotStructure = TRUE,外显子,外显子)# #——show_snp消息= FALSE ----------------------------------------------- querySNPFile < -执行(“extdata”、“SNP_example.txt”,包=“分歧点”)querySNPTable <——read.delim (querySNPFile)头(querySNPTable) # #——read_snp消息= FALSE ----------------------------------------------- querySNP < - readQueryFile (querySNPFile queryType =“国民党”,外显子=外显子,filter = TRUE)头(querySNP) # #——read_snp_mart消息= FALSE ------------------------------------------ 库(biomaRt)集市< - useMart(数据集“ENSEMBL_MART_SNP”=“hsapiens_snp”,主机= " www.ensembl.org ") querySNP < makeBranchpointWindowForSNP (c(“rs587776767”、“rs786205083”),集市。snp = mart, exons = exons, filter =FALSE) head(querySNP) ## ----snp_att_fa, message=FALSE, eval=FALSE--------------------------------- # branchpointPredictionsSNP <- predictBranchpoints(querySNP， # queryType = " snp "， # genome = " grch38 .primary_assembly.基因组。fa"， # bedtoolsLocation="/Apps/bedtools2/bin/bedtools") ## ----snp_att_BS, message=FALSE--------------------------------------------- #for query SNPs branchpointPredictionsSNP <- predictBranchpoints(querySNP, queryType =" SNP"， BSgenome = g) head(branchpointPredictionsSNP) # summary effect:querySNPSummary <- predictionsToSummary(querySNP,branchpointPredictionsSNP) head(querySNPSummary) ## ----rs587776767-plot------------------------------------------------------ plotBranchpointWindow(querySNP$id[2]， branchpointPredictionsSNP, probabilityCutoff = 0.52, plotMutated = TRUE, plotStructure = TRUE, exons = exons) ## ----运行时间，message=FALSE, eval=FALSE---------------------------------- ## #注释插入子中分支点的步骤时间:# gtfToExons() # #用户系统运行# # 41.385 3.848 47.096 # #设置1。294 lincRNA introns在chr22: # makeBranchpointWindowForExons() # #用户系统运行# # 0.196 0.024 0.226 # predictBranchpoints() # #用户系统运行# # 208.934 4.157 225.849 # # #设置2。 3693 protein coding exons on chr22: # makeBranchpointWindowForExons() # # user system elapsed # # 0.245 0.013 0.261 # predictBranchpoints() # # user system elapsed # # 2332.519 38.266 2482.032 # # # Step times for annotating branchpoints with SNPs: # # 29899 GWAS SNPS # readQueryFile(filter = TRUE) # # user system elapsed # # 5.997 1.608 7.773 # readQueryFile(filter = FALSE) # # user system elapsed # # 1.744 0.427 2.339 # # # 298 filtered SNPS # predictBranchpoints() # # user system elapsed # # 172.495 2.485 181.876 # # predictionsToSummary() # # user system elapsed # # 0.057 0.003 0.061 ## ----sessionInfo, eval=TRUE------------------------------------------------ sessionInfo()