Last updated: 2019-05-06

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vgRNA

library(m6Amonster)
vgRNA <-c("RSVvgRNA1","RSVvgRNA2")
gtf <- "~/Database/genome/RSV/GFP.RSV_gene.gtf"
RSV.A549 <- countReads(samplenames = paste(vgRNA,"align_RSV",sep = "." ),
                          gtf = gtf,
                          bamFolder = "/home/zijiezhang/RSV/201803/bam_files",
                          outputDir = "/home/zijiezhang/RSV/201803",
                          modification = "m6A",
                          threads = 2,saveOutput =F,
                       binSize = 30
                          )
Reading gtf file to obtain gene model
Filter out ambiguous model...
Gene model obtained from gtf file...
counting reads for each genes, this step may takes a few hours....
Hyper-thread registered: TRUE 
Using 2 thread(s) to count reads in continuous bins...
Time used to count reads: 0.108239122231801 mins...

Report the peaks on vgRNA.

RSV.A549 <-  m6Amonster:::callPeakBinomial(RSV.A549,min_counts = 10, threads = 10)
vgRNA_peak <- reportConsistentPeak(readsOut = RSV.A549,samplenames = paste(vgRNA,"align_RSV",sep = "." ))
Reporting peak concsistent in all samples for
 RSVvgRNA1.align_RSV RSVvgRNA2.align_RSV 
Hyper-thread registered: TRUE 
Using 1 thread(s) to report merged report...
Time used to report peaks: 0.0257289528846741 mins... 
annotation <- read.table("~/Database/genome/RSV/GFP.RSV_annotation.txt",sep = "\t",header = T)
anno.gr <- makeGRangesFromDataFrame(annotation,keep.extra.columns = T)

vgRNA_gr <- makeGRangesFromDataFrame(vgRNA_peak)
anno.vgRNA <- as.data.frame(findOverlaps(vgRNA_gr, anno.gr, ignore.strand = T) )
vgRNA_peak$name <- as.character(vgRNA_peak$name)
vgRNA_peak$name [anno.vgRNA$queryHits] <- as.character(annotation[anno.vgRNA$subjectHits,"gene"])

write.table(dplyr::filter(vgRNA_peak,score<1e-20),file = "~/RSV/RSV_m6Aseq_analysis/data/RSVvgRNA_A549_peaks.xls", sep = "\t",col.names = T,row.names = F,quote = F)

Plot the coverage of vgRNA.

library(MyTools)
RSV.A549_plot <- gtfToGeneModel( "~/Database/genome/RSV/GFP.RSV.gtf")
plotVirusCov(RSV.A549$bamPath.ip, RSV.A549$bamPath.input ,RSV.A549_plot,libraryType = "opposite",center = mean,annotation)+scale_fill_discrete(name = "IP",labels = c("Genome","anti-Genome"))+ xlab("Genome location") + ylab("Normalized coverage") + scale_colour_discrete(name = "INPUT",labels = c("Genome","anti-Genome"))+theme(legend.text = element_text(face = "bold",size = 18), legend.title = element_text(face = "bold",size = 20),axis.text = element_text(face = "bold",size = 18),axis.title = element_text(face = "bold",size = 20) )

Expand here to see past versions of unnamed-chunk-3-1.png:
Version Author Date
295e8d4 scottzijiezhang 2019-01-23

Infected sample

infected <- c("RSVinfect1","RSVinfect2","mutRSVinfect1","mutRSVinfect2")
RSV_infect <- countReads(samplenames = paste(infected,"align_RSV",sep = "." ),
                          gtf = gtf,
                          bamFolder = "/home/zijiezhang/RSV/201803/bam_files",
                          outputDir = "/home/zijiezhang/RSV/201803",
                          modification = "m6A",
                          threads = 2,saveOutput = F,
                         binSize = 30
                          )
Reading gtf file to obtain gene model
Filter out ambiguous model...
Gene model obtained from gtf file...
counting reads for each genes, this step may takes a few hours....
Hyper-thread registered: TRUE 
Using 2 thread(s) to count reads in continuous bins...
Time used to count reads: 0.719945641358693 mins... 
RSV_infect <-  m6Amonster:::callPeakBinomial(RSV_infect,threads = 10)

Report peaks for infected samples

WT_peak <- reportConsistentPeak(RSV_infect,samplenames = paste(infected,"align_RSV",sep = "." )[1:2])
Reporting peak concsistent in all samples for
 RSVinfect1.align_RSV RSVinfect2.align_RSV 
Hyper-thread registered: TRUE 
Using 1 thread(s) to report merged report...
Time used to report peaks: 0.00843370358149211 mins... 
## annotate peak
WT_peak_gr <- makeGRangesFromDataFrame(WT_peak)
anno.WT <- as.data.frame(findOverlaps(WT_peak_gr, anno.gr, ignore.strand = T,minoverlap = 100) )
WT_peak$name <- as.character(WT_peak$name)
WT_peak$name [anno.WT$queryHits] <- as.character(annotation[anno.WT$subjectHits,"gene"])
write.table(dplyr::filter(WT_peak,score<1e-5),file = "~/RSV/RSV_m6Aseq_analysis/data/RSVinfected_A549_peaks.xls", sep = "\t",col.names = T,row.names = F,quote = F)

Plot WT coverage

plotVirusCov(RSV_infect$bamPath.ip[1:2],RSV_infect$bamPath.input[1:2] ,RSV.A549_plot,libraryType = "opposite",center = mean,annotation,hideStrand = "-")+scale_fill_discrete(name = "IP",labels = c("anti-Genome/mRNA")) + xlab("Genome location") + ylab("Normalized coverage")+ scale_colour_discrete(name = "INPUT",labels = c("anti-Genome/mRNA"))+theme(legend.text = element_text(face = "bold",size = 18), legend.title = element_text(face = "bold",size = 20),axis.text = element_text(face = "bold",size = 18),axis.title = element_text(face = "bold",size = 20) )

Expand here to see past versions of unnamed-chunk-6-1.png:
Version Author Date
295e8d4 scottzijiezhang 2019-01-23

Session information

sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 17.10

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] grid      stats4    parallel  stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] MyTools_0.0.0               ChIPseeker_1.18.0          
 [3] Guitar_1.20.0               bindrcpp_0.2.2             
 [5] m6Amonster_0.1.5            RcppArmadillo_0.9.200.5.0  
 [7] Rcpp_1.0.0                  reshape2_1.4.3             
 [9] GenomicAlignments_1.18.0    SummarizedExperiment_1.12.0
[11] DelayedArray_0.8.0          BiocParallel_1.16.1        
[13] matrixStats_0.54.0          rtracklayer_1.42.1         
[15] doParallel_1.0.14           iterators_1.0.10           
[17] foreach_1.4.4               ggplot2_3.1.0              
[19] Rsamtools_1.34.0            Biostrings_2.50.1          
[21] XVector_0.22.0              GenomicFeatures_1.34.1     
[23] AnnotationDbi_1.44.0        Biobase_2.42.0             
[25] GenomicRanges_1.34.0        GenomeInfoDb_1.18.1        
[27] IRanges_2.16.0              S4Vectors_0.20.1           
[29] BiocGenerics_0.28.0        

loaded via a namespace (and not attached):
  [1] backports_1.1.2                        
  [2] fastmatch_1.1-0                        
  [3] workflowr_1.1.1                        
  [4] plyr_1.8.4                             
  [5] igraph_1.2.2                           
  [6] lazyeval_0.2.1                         
  [7] splines_3.5.3                          
  [8] gridBase_0.4-7                         
  [9] urltools_1.7.1                         
 [10] digest_0.6.18                          
 [11] htmltools_0.3.6                        
 [12] GOSemSim_2.8.0                         
 [13] viridis_0.5.1                          
 [14] GO.db_3.7.0                            
 [15] gdata_2.18.0                           
 [16] magrittr_1.5                           
 [17] memoise_1.1.0                          
 [18] cluster_2.0.7-1                        
 [19] R.utils_2.7.0                          
 [20] enrichplot_1.2.0                       
 [21] prettyunits_1.0.2                      
 [22] colorspace_1.4-0                       
 [23] blob_1.1.1                             
 [24] ggrepel_0.8.0                          
 [25] dplyr_0.7.8                            
 [26] crayon_1.3.4                           
 [27] RCurl_1.95-4.11                        
 [28] jsonlite_1.5                           
 [29] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [30] bindr_0.1.1                            
 [31] ape_5.2                                
 [32] glue_1.3.0                             
 [33] gtable_0.2.0                           
 [34] zlibbioc_1.28.0                        
 [35] UpSetR_1.3.3                           
 [36] scales_1.0.0                           
 [37] DOSE_3.8.0                             
 [38] DBI_1.0.0                              
 [39] plotrix_3.7-4                          
 [40] viridisLite_0.3.0                      
 [41] progress_1.2.0                         
 [42] units_0.6-1                            
 [43] gridGraphics_0.3-0                     
 [44] bit_1.1-14                             
 [45] europepmc_0.3                          
 [46] httr_1.3.1                             
 [47] fgsea_1.8.0                            
 [48] gplots_3.0.1                           
 [49] RColorBrewer_1.1-2                     
 [50] pkgconfig_2.0.2                        
 [51] XML_3.98-1.16                          
 [52] R.methodsS3_1.7.1                      
 [53] farver_1.1.0                           
 [54] ggplotify_0.0.3                        
 [55] tidyselect_0.2.5                       
 [56] labeling_0.3                           
 [57] rlang_0.3.1                            
 [58] munsell_0.5.0                          
 [59] tools_3.5.3                            
 [60] RSQLite_2.1.1                          
 [61] ggridges_0.5.1                         
 [62] evaluate_0.12                          
 [63] stringr_1.3.1                          
 [64] yaml_2.2.0                             
 [65] knitr_1.20                             
 [66] bit64_0.9-7                            
 [67] caTools_1.17.1.1                       
 [68] purrr_0.2.5                            
 [69] ggraph_1.0.2                           
 [70] nlme_3.1-137                           
 [71] whisker_0.3-2                          
 [72] R.oo_1.22.0                            
 [73] DO.db_2.9                              
 [74] xml2_1.2.0                             
 [75] biomaRt_2.38.0                         
 [76] compiler_3.5.3                         
 [77] tibble_2.0.1                           
 [78] tweenr_1.0.0                           
 [79] stringi_1.2.4                          
 [80] lattice_0.20-38                        
 [81] Matrix_1.2-15                          
 [82] vegan_2.5-3                            
 [83] permute_0.9-4                          
 [84] pillar_1.3.1                           
 [85] triebeard_0.3.0                        
 [86] data.table_1.11.8                      
 [87] cowplot_0.9.3                          
 [88] bitops_1.0-6                           
 [89] qvalue_2.14.0                          
 [90] R6_2.3.0                               
 [91] vcfR_1.8.0                             
 [92] KernSmooth_2.23-15                     
 [93] gridExtra_2.3                          
 [94] codetools_0.2-16                       
 [95] boot_1.3-20                            
 [96] MASS_7.3-51.1                          
 [97] gtools_3.8.1                           
 [98] assertthat_0.2.0                       
 [99] rprojroot_1.3-2                        
[100] withr_2.1.2                            
[101] pinfsc50_1.1.0                         
[102] GenomeInfoDbData_1.2.0                 
[103] mgcv_1.8-26                            
[104] hms_0.4.2                              
[105] rmarkdown_1.10                         
[106] rvcheck_0.1.1                          
[107] git2r_0.23.0                           
[108] ggforce_0.1.3

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