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#### create [R] object of class "proteome"
setClass("proteome",representation(fasta="list", AAratios="matrix",seq="list",names="ANY",cleaved="list",coverage="ANY",coveragevec="ANY",summary="ANY"))
###### read in your favorite proteome
### anyName<-read.proteome(input="[path to your favoritefasta file]")
paste(getwd(),"/Scervis.fasta",sep="")
Scerevis<-read.proteome(input=paste(getwd(),"/Scervis.fasta",sep=""),pie=TRUE,cleave=F)
#### MED-FASP simulation - see function details for options
#### LysC only
lysC<-medfasp(object=cere,proteases=c("lysC"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### trypsin only
try<-medfasp(object=cere,proteases=c("trypsin"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### gluC only
gluC<-medfasp(object=cere,proteases=c("gluC"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### argC only
argC<-medfasp(object=cere,proteases=c("argC"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### aspN only
aspN<-medfasp(object=cere,proteases=c("aspN"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### CNBr only
CNBr<-medfasp(object=cere,proteases=c("CNBr"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### NTCB only
NTCB<-medfasp(object=cere,proteases=c("NTCB"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### TrpC only
TrpC<-medfasp(object=cere,proteases=c("trpC"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### lysc then trypsin
lysCtryp<-medfasp(object=cere,proteases=c("lysC","trypsin"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### gluc then trypsin
gluC_tryp<-medfasp(object=cere,proteases=c("gluC","trypsin"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### CNBr then lysc then trypsin
cnbr_lysC_tryp<-medfasp(object=cere,proteases=c("CNBr","lysC","trypsin"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### NTCB then CNBr then lysc then trypsin
ntcb_cnbr_lysC_tryp<-medfasp(object=cere,proteases=c("NTCB","CNBr","lysC","trypsin"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### TrpC, NTCB, CNBr, ArgC, GluC, Trypsin
trpC_NTCB_CNBr_ArgC_GluC_trypsin<-medfasp(object=cere,proteases=c("trpC","NTCB","CNBr","argC","gluC","trypsin"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### TrpC, NTCB, CNBr, ArgC, AspN, GluC, Trypsin
trpC_NTCB_CNBr_ArgC_AspN_GluC_trypsin<-medfasp(object=cere,proteases=c("trpC","NTCB","CNBr","argC","aspN","gluC","trypsin"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#### Trypsin, GluC, AspN, ArgC, CNBr, NTCB, TrpC (reverse)
reverse<-medfasp(object=cere,missed.propensity=c(0.02),proteases=c("trypsin","gluC","aspN","argC","CNBr","NTCB","trpC"),LENcutoff=c(7,35),MWcutoff=5000,filterMW=TRUE,filterLEN=FALSE)
#####################################################################################################
#### Example of using cut predict with missed cleavages
#### These lines of code were used to produce supplemental figure 1
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n1
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n2
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n3
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n4
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n5
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n6
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n7
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n8
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n9
cut.predict.test(object=pom, missed.propensity=0.01)->testmissed0pt01n10
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n1
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n2
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n3
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n4
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n5
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n6
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n7
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n8
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n9
cut.predict.test(object=pom, missed.propensity=0.1)->testmissed0pt1n10
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n1
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n2
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n3
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n4
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n5
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n6
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n7
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n8
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n9
cut.predict.test(object=pom, missed.propensity=0.5)->testmissed0pt5n10
hist(nchar(testmissed0pt01n1@cleaved$protease),breaks=c(0:10000))->hist11
hist(nchar(testmissed0pt01n2@cleaved$protease),breaks=c(0:10000))->hist12
hist(nchar(testmissed0pt01n3@cleaved$protease),breaks=c(0:10000))->hist13
hist(nchar(testmissed0pt01n4@cleaved$protease),breaks=c(0:10000))->hist14
hist(nchar(testmissed0pt01n5@cleaved$protease),breaks=c(0:10000))->hist15
hist(nchar(testmissed0pt01n6@cleaved$protease),breaks=c(0:10000))->hist16
hist(nchar(testmissed0pt01n7@cleaved$protease),breaks=c(0:10000))->hist17
hist(nchar(testmissed0pt01n8@cleaved$protease),breaks=c(0:10000))->hist18
hist(nchar(testmissed0pt01n9@cleaved$protease),breaks=c(0:10000))->hist19
hist(nchar(testmissed0pt01n10@cleaved$protease),breaks=c(0:10000))->hist110
hist(nchar(testmissed0pt1n1@cleaved$protease),breaks=c(0:10000))->hist21
hist(nchar(testmissed0pt1n2@cleaved$protease),breaks=c(0:10000))->hist22
hist(nchar(testmissed0pt1n3@cleaved$protease),breaks=c(0:10000))->hist23
hist(nchar(testmissed0pt1n4@cleaved$protease),breaks=c(0:10000))->hist24
hist(nchar(testmissed0pt1n5@cleaved$protease),breaks=c(0:10000))->hist25
hist(nchar(testmissed0pt1n6@cleaved$protease),breaks=c(0:10000))->hist26
hist(nchar(testmissed0pt1n7@cleaved$protease),breaks=c(0:10000))->hist27
hist(nchar(testmissed0pt1n8@cleaved$protease),breaks=c(0:10000))->hist28
hist(nchar(testmissed0pt1n9@cleaved$protease),breaks=c(0:10000))->hist29
hist(nchar(testmissed0pt1n10@cleaved$protease),breaks=c(0:10000))->hist210
hist(nchar(testmissed0pt5n1@cleaved$protease),breaks=c(0:10000))->hist31
hist(nchar(testmissed0pt5n2@cleaved$protease),breaks=c(0:10000))->hist32
hist(nchar(testmissed0pt5n3@cleaved$protease),breaks=c(0:10000))->hist33
hist(nchar(testmissed0pt5n4@cleaved$protease),breaks=c(0:10000))->hist34
hist(nchar(testmissed0pt5n5@cleaved$protease),breaks=c(0:10000))->hist35
hist(nchar(testmissed0pt5n6@cleaved$protease),breaks=c(0:10000))->hist36
hist(nchar(testmissed0pt5n7@cleaved$protease),breaks=c(0:10000))->hist37
hist(nchar(testmissed0pt5n8@cleaved$protease),breaks=c(0:10000))->hist38
hist(nchar(testmissed0pt5n9@cleaved$protease),breaks=c(0:10000))->hist39
hist(nchar(testmissed0pt5n10@cleaved$protease),breaks=c(0:10000))->hist310
postscript(file="suppFig1.ps",width=8, height=6)
par(cex=1.5)
plot(hist11$density, xlim=c(5,40),ylim=c(0.0,0.05),type="l", ylab="fraction of all peptides produced", xlab="length",main="comparison of missed cleavage propensities, n=10")
lines(hist12$density, col="black")
lines(hist13$density, col="black")
lines(hist14$density, col="black")
lines(hist15$density, col="black")
lines(hist16$density, col="black")
lines(hist17$density, col="black")
lines(hist18$density, col="black")
lines(hist19$density, col="black")
lines(hist110$density, col="black")
lines(hist21$density, col="red")
lines(hist22$density, col="red")
lines(hist23$density, col="red")
lines(hist24$density, col="red")
lines(hist25$density, col="red")
lines(hist26$density, col="red")
lines(hist27$density, col="red")
lines(hist28$density, col="red")
lines(hist29$density, col="red")
lines(hist210$density, col="red")
lines(hist31$density, col="blue")
lines(hist32$density, col="blue")
lines(hist33$density, col="blue")
lines(hist34$density, col="blue")
lines(hist35$density, col="blue")
lines(hist36$density, col="blue")
lines(hist37$density, col="blue")
lines(hist38$density, col="blue")
lines(hist39$density, col="blue")
lines(hist310$density, col="blue")
legend(x=25, y=0.04,legend=c("1% missed","10% missed", "50% missed"), lty=c(1,1,1),col=c("black","red","blue"))
dev.off()