Get Target sequence
# BCL6-LCR coordinates: chr3:187605559-187854096 (provided by Raj, email Dec 20 2016)
target_region <- "BCL6-LCR"
chr <- "chr3"
start <- 187605559
end <- 187854096
lcr <- Views(BSgenome.Hsapiens.UCSC.hg19[[chr]],start=start,end=end)
# region length:
cat((end-start)/1000," kb")
## 248.537 kb
Identify all potential PAMs and gRNAs
# Find all gRNAs + PAMs in plus sequence
PAM <- DNAString(paste(c(rep("N",21),"GG"),collapse = ""))
gRNA.p <- matchPattern(PAM,lcr,fixed = F)
names(gRNA.p)[1:length(gRNA.p)] <- "+"
# Find all gRNAs + PAMs in minus sequence
PAM.rc <- reverseComplement(PAM)
gRNA.m <- matchPattern(PAM.rc,lcr,fixed = F)
names(gRNA.m)[1:length(gRNA.m)] <- "-"
# combine + and - strand:
gRNA <- c(gRNA.p,gRNA.m)
df.gRNA <- data.frame(id=1:length(gRNA),
target_seq_plus=gRNA,
target_chr=chr,
target_start=start(gRNA),
target_end=end(gRNA),
strand=names(gRNA))
# transform select columns from factors to characters:
df.gRNA$target_chr <- as.character(df.gRNA$target_chr)
# reverseComplement - strand target_seq
df.gRNA$target_seq <- NA
df.gRNA$target_seq[df.gRNA$strand=="+"] <- df.gRNA$target_seq_plus[df.gRNA$strand=="+"]
df.gRNA$target_seq[df.gRNA$strand=="-"] <- as.character(reverseComplement(DNAStringSet(df.gRNA$target_seq_plus[df.gRNA$strand=="-"])))
# add genome browser input:
df.gRNA$genome_browser <- paste0(df.gRNA$target_chr,":",df.gRNA$target_start,"-",df.gRNA$target_end)
# add base position after cut:
# note that Cas9 cuts between -3 and -4 from PAM
# thus, for + strand gRNAs, the position AFTER cut will be -5 from target_end
# for - strand gRNAs, the position AFTER cut will be +6 from target_start
df.gRNA$base_after_cut <- NA
df.gRNA$base_after_cut[df.gRNA$strand=="+"] <- df.gRNA$target_end[df.gRNA$strand=="+"]-5
df.gRNA$base_after_cut[df.gRNA$strand=="-"] <- df.gRNA$target_start[df.gRNA$strand=="-"]+6
df.gRNA$base_after_cut <- as.integer(df.gRNA$base_after_cut)
### isolate gRNA sequence (i.e. target sequence without the PAM)
df.gRNA$gRNA <- NA
df.gRNA$gRNA <- as.character(subseq(DNAStringSet(df.gRNA$target_seq),start = 1,width = 20))
# # write initial / all gRNAs to BED:
# bed <- data.frame(chrom=df.gRNA$target_chr,chromStart=df.gRNA$target_start,chromEnd=df.gRNA$target_end,name=df.gRNA$id,score=1,strand=df.gRNA$strand)
# filename <- paste0(target_region,"_initial_gRNAs.bed")
# write('track name="initial gRNAs (pre filter)" description="inital gRNAs (pre filter)" visibility=1 colorByStrand="255,0,0 0,0,255"',file=filename,append = F)
# write.table(bed,file=filename,quote = F,col.names = F,row.names = F,append = T)
#
# # save as Rda file:
# filename <- paste0(target_region,"_initial_gRNAs.Rda")
# save(df.gRNA,file = filename)
Initial library stats
# total number of potential gRNAs (inital)
dim(df.gRNA)[1]
## [1] 26728
# calculate median distance between cut sites etc.:
l <- dim(df.gRNA)[1]
df.dist <- data.frame(cut=sort(df.gRNA$base_after_cut)[1:(l-1)])
df.dist$next_cut <- sort(df.gRNA$base_after_cut)[2:l]
df.dist$dist <- df.dist$next_cut-df.dist$cut
summary(df.dist$dist)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 5.000 9.298 13.000 219.000
Filter gRNAs
remove duplicates
# find all duplicates:
dup.f <- which(duplicated(df.gRNA$gRNA,fromLast=F))
dup.r <- which(duplicated(df.gRNA$gRNA,fromLast=T))
dup <- union(dup.r,dup.f)
if (length(dup)>=1){
print("Found >=1 duplicate gRNAs")
# # write duplicated gRNAs to BED:
# bed <- data.frame(chrom=df.gRNA$target_chr[dup],chromStart=df.gRNA$target_start[dup],chromEnd=df.gRNA$target_end[dup],
# ame=df.gRNA$id[dup],score=1,strand=df.gRNA$strand[dup])
# filename <- paste0(target_region,"_duplicated_gRNAs.bed")
# write('track name="duplicated gRNAs" description="duplicated gRNAs" visibility=1 colorByStrand="255,0,0
# 0,0,255"',file=filename,append = F)
# write.table(bed,file=filename,quote = F,col.names = F,row.names = F,append = T)
# delete all duplicates:
df.gRNA <- df.gRNA[-dup,]
# number of duplicates (removed):
length(dup)
# number of remaining gRNAs:
dim(df.gRNA)[1]
}else{print("Found no duplicate gRNAs")}
## [1] "Found >=1 duplicate gRNAs"
## [1] 25880
remove gRNAs containing potential transcriptional terminators
term <- grep("TTTT",df.gRNA$gRNA,fixed = F)
df.gRNA <- df.gRNA[-term,]
# number of polyT-gRNAs (removed):
length(term)
## [1] 2502
# number of remaining gRNAs:
dim(df.gRNA)[1]
## [1] 23378
remove gRNAs containing polyA/C/G stretches
polyA <- grep("AAAAA",df.gRNA$gRNA,fixed = F)
df.gRNA <- df.gRNA[-polyA,]
polyC <- grep("CCCCC",df.gRNA$gRNA,fixed = F)
df.gRNA <- df.gRNA[-polyC,]
polyG <- grep("GGGGG",df.gRNA$gRNA,fixed = F)
df.gRNA <- df.gRNA[-polyG,]
length(c(polyA,polyC,polyG))
## [1] 1386
# number of polyA/C/G-gRNAs (removed):
length(c(polyA,polyC,polyG))
## [1] 1386
# number of remaining gRNAs:
dim(df.gRNA)[1]
## [1] 21992
remove all gRNAs containing RE-sites
# this library will be cloned into pLKO5-gRNA vectors using BsmBI
RE.sense <- grep("CGTCTC",df.gRNA$gRNA,fixed = F)
if (length(RE.sense)>=1){
df.gRNA <- df.gRNA[-RE.sense,]}
RE.antisense <- grep("GAGACG",df.gRNA$gRNA,fixed = F)
if (length(RE.antisense)>=1){
df.gRNA <- df.gRNA[-RE.antisense,]}
# number of RE-containing gRNAs (removed):
length(c(RE.sense,RE.antisense))
## [1] 35
# number of remaining gRNAs:
dim(df.gRNA)[1]
## [1] 21957
Off-Target analysis and filtering
# Strategy: we take a simplistic approach whereby we identify all gRNAs that have a perfect off-target match (mm0) to any genomic locus other than the target region. The number of off-target hits is not taken into account and will presumably range from very few to abundant. Furthermore, non-perfect off-target hits are not taken into account and may have to be considered later on.
# load genome omitting random/unfinished chromosomes and additional haplotypes (i.e. limiting data to chr 1-24)
genome <- as.list(BSgenome.Hsapiens.UCSC.hg19)[1:24]
seqnames <- names(genome)
# exclude BCL6 LCR from mismatch analysis by masking chr3 (which produces an XString view)
chr3.masked <- mask(genome$chr3,start=start,end=end)
# mask genome by deleting old chr3 and adding masked chr3
genome.masked <- c(as.list(genome)[-3],as.list(chr3.masked))
genome.masked.set <- DNAStringSet(as.list(genome.masked))
### Pattern matching
# Note on strategy: searching for gRNA sequence + NGG is more precise than searching for the entire target sequence as PAM sequence can vary
# There are two options to enable inexact matching (i.e. using a wildcard / IUPAC ambiguity codes for NGG),
# 1.) the pattern library can be left un-processed (i.e. not using the pre-processing with pdict). However, vwichPDict seems to be incredibly slow with a non-processed dictionary.
# 2.) define a trusted band in the pattern library. The head and tail can then include IUPAC ambiguity codes.
# See Biostrings Reference manual matchPdict example 'D. USING A NON-PREPROCESSED DICTIONARY' and 'matchPDict-inexact' chapter for details
# Note on validity check: in the Addendum, I have manually specified each PAM and checked the vwchiPDict results for chr1 against the results of the code below. The results were identical.
# Note on vwichPDict: vwhichPDict only calcualtes which patterns in the input dictionary have at least one match in the subject / set of reference sequences (without counting number of matches or retrieving positions of matches)
### + strand
pdict.p <- DNAStringSet(paste0(df.gRNA$gRNA,"NGG"))
pdict.prepr.p <- PDict(pdict.p,tb.start = 1,tb.width = 20)
wpd.p <- vwhichPDict(pdict.prepr.p,genome.masked.set,max.mismatch = 0,fixed = "subject")
mm0.p <- unique(unlist(wpd.p))
### - strand
pdict.m <- reverseComplement(DNAStringSet(paste0(df.gRNA$gRNA,"NGG")))
pdict.prepr.m <- PDict(pdict.m,tb.start = 4,tb.width = 20)
wpd.m <- vwhichPDict(pdict.prepr.m,genome.masked.set,max.mismatch = 0,fixed="subject")
mm0.m <- unique(unlist(wpd.m))
length(mm0.m)
## [1] 2286
# make union of + and - strand off-target search results
mm0 <- union(mm0.p,mm0.m)
# # write gRNAs with perfect matches (0 mismatch / mm0) to off-targets (>1) to BED file:
# bed <- data.frame(chrom=df.gRNA$target_chr[mm0],chromStart=df.gRNA$target_start[mm0],chromEnd=df.gRNA$target_end[mm0],name=df.gRNA$id[mm0],score=1,strand=df.gRNA$strand[mm0])
# filename <- paste0(target_region,"_mm0-off-targets_gRNAs.bed")
# write('track name="off-target gRNAs (mm0)" description="off-target gRNAs (mm0)" visibility=1 colorByStrand="255,0,0 0,0,255"',file=filename,append = F)
# write.table(bed,file=filename,quote = F,col.names = F,row.names = F,append = T)
# delete gRNAs with 0-mismatch off-targets:
df.gRNA.offTarget.save <- df.gRNA
df.gRNA <- df.gRNA[-mm0,]
# number of gRNAs perfectly matching any off-target (removed)
# plus:
length(mm0.p)
## [1] 2274
# minus:
length(mm0.m)
## [1] 2286
# union:
length(mm0)
## [1] 2436
# number of remaining gRNAs:
dim(df.gRNA)[1]
## [1] 19521
Final library stats
# Total number of gRNAs:
dim(df.gRNA)[1]
## [1] 19521
# Total number of targeted cut sites:
length(unique(df.gRNA$base_after_cut))
## [1] 19086
# Number of gRNAs on each strand:
table(df.gRNA$strand)
##
## - +
## 10011 9510
# calculate distance between cut sites:
l <- dim(df.gRNA)[1]
df.dist <- data.frame(cut=sort(df.gRNA$base_after_cut)[1:(l-1)])
df.dist$next_cut <- sort(df.gRNA$base_after_cut)[2:l]
df.dist$dist <- df.dist$next_cut-df.dist$cut
# Summary of distance between cut sites:
summary(df.dist$dist)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 1.00 6.00 12.73 15.00 3420.00
# How often is the distance between cut sites 0,1,2,3,4... bp?
table(df.dist$dist)[1:100]
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 435 5391 496 1332 1056 1004 922 828 549 474 427 380 544 389 385
## 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## 336 279 285 266 223 239 191 189 148 127 152 149 116 102 102
## 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
## 91 100 87 78 63 64 60 73 49 56 41 45 44 48 50
## 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
## 53 44 28 32 35 35 42 37 32 26 17 20 26 23 22
## 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
## 31 16 18 14 16 12 16 11 18 15 13 16 13 8 8
## 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
## 11 10 7 7 12 3 13 12 5 9 14 7 4 7 4
## 90 91 92 93 94 95 96 97 98 99
## 4 7 8 3 1 2 5 4 5 4
## Warning: Removed 1913 rows containing non-finite values (stat_bin).
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)
## Warning: Removed 1913 rows containing non-finite values (stat_bin).
final library data files
# write final selection of gRNAs to BED:
bed <- data.frame(chrom=df.gRNA$target_chr,chromStart=df.gRNA$target_start,chromEnd=df.gRNA$target_end,name=df.gRNA$id,score=1,strand=df.gRNA$strand)
filename <- paste0(target_region,"_final_gRNAs_gRNAPos.bed")
write('track name="final gRNAs" description="final gRNAs" visibility=1 colorByStrand="255,0,0 0,0,255"',file=filename,append = F)
write.table(bed,file=filename,quote = F,col.names = F,row.names = F,append = T)
# write final selection of gRNAs to BED - cut positions only:
bed <- data.frame(chrom=df.gRNA$target_chr,chromStart=df.gRNA$base_after_cut,chromEnd=df.gRNA$base_after_cut,name=df.gRNA$id,score=1,strand=df.gRNA$strand)
filename <- paste0(target_region,"_final_gRNAs_cutPos.bed")
write('track name="final gRNAs cutPos" description="final gRNAs cutPos" visibility=1 colorByStrand="255,0,0 0,0,255"',file=filename,append = F)
write.table(bed,file=filename,quote = F,col.names = F,row.names = F,append = T)
# save gRNA dataset:
filename <- paste0(target_region,"_final_gRNAs.Rda")
save(df.gRNA,file = filename)