###number of reads per sample (figS3)
barnames<-c('pstI','EcoRI','MS1.1 10 cyc.','MS1.2 10 cyc.','MS2.1 10 cyc.','MS2.2 10 cyc.','MS1.1 20 cyc.','MS1.2 20 cyc.'
,'MS2.1 20 cyc.','MS2.2 20 cyc.','MS1.1 30 cyc.','MS1.2 30 cyc.','MS2.1 30 cyc.','MS3.2 30 cyc.','MS1.1 40 cyc.','MS1.2 40 cyc.'
,'MS2.1 40 cyc.','MS2.2 40 cyc.')
barnames<-c('pstI','EcoRI','C1.1 10 cyc.','C1.2 10 cyc.','C2.1 10 cyc.','C2.2 10 cyc.','C1.1 20 cyc.','C1.2 20 cyc.'
,'C2.1 20 cyc.','C2.2 20 cyc.','C1.1 30 cyc.','C1.2 30 cyc.','C2.1 30 cyc.','C2.2 30 cyc.','C1.1 40 cyc.','C1.2 40 cyc.'
,'C2.1 40 cyc.','C2.2 40 cyc.')
readnrs<-unlist(lapply(gogo,length))
reads_sorted<-readnrs[c(1,2,3,5,4,6,7,9,8,10,11,13,12,14,15,17,16,18)]
par(las=2)
barplot(reads_sorted,names.arg=barnames,cex.names=.7,ylab='Nr. reads',cex.axis=.8)
################################################
###summarize densities
###30x2
rp1<-ret.perc(ecoRI_2x30_l,lms=2)
rp2<-ret.perc(c10_241_2x30_l,c10_242_2x30_l,lms=2)
rp4<-ret.perc(c20_241_2x30_l,c20_242_2x30_l,lms=2)
rp6<-ret.perc(c30_241_2x30_l,c30_242_2x30_l,lms=2)
rp8<-ret.perc(c40_241_2x30_l,c40_242_2x30_l,lms=2)
###20x4
rp1.2<-ret.perc(pstI_4x20_l,lms=4)
rp2.2<-ret.perc(c10_131_4x20_l,c10_132_4x20_l,lms=4)
rp4.2<-ret.perc(c20_131_4x20_l,c20_132_4x20_l,lms=4)
rp6.2<-ret.perc(c30_131_4x20_l,c30_132_4x20_l,lms=4)
rp8.2<-ret.perc(c40_131_4x20_l,c40_132_4x20_l,lms=4)
###10x4
rp1.3<-ret.perc(pstI_4x10_l,ecoRI_4x10_l,lms=4)
rp2.3<-ret.perc(c10_131_4x10_l,c10_132_4x10_l,c10_241_4x10_l,c10_242_4x10_l,lms=4)
rp4.3<-ret.perc(c20_131_4x10_l,c20_132_4x10_l,c20_241_4x10_l,c20_242_4x10_l,lms=4)
rp6.3<-ret.perc(c30_131_4x10_l,c30_132_4x10_l,c30_241_4x10_l,c30_242_4x10_l,lms=4)
rp8.3<-ret.perc(c40_131_4x10_l,c40_132_4x10_l,c40_241_4x10_l,c40_242_4x10_l,lms=4)

###Panel plot (Fig 1)
windows(8,4)
layout(matrix(c(1,1,1,2,3,4),ncol=3,byrow=T),heights=c(1,5,5,5))
layout.show(4)
par(mar=c(0,0,0,0))
plot(1:10,1:10,type='n',xlab='',ylab='',axes=F)
legend(2.5,8,legend=c('No PCR','10 cycles','20 cycles','30 cycles','40 cycles'),bg=colors()[250],ncol=5,pch=16,pt.cex=2,col=1:5,cex=1.5)
par(mar=c(4,4,1,1))
plot(rp1[1,],rp1[2,],xlim=c(38,70),pch=16,axes=F,type='n',xlab='Microsatellite length',ylab='Proportion of reads')
axis(2)
axis(1,at=seq(38,70,by=2))
abline(v=seq(38,70,by=4),lty=3,col=colors()[152])
abline(h=c(0,.1,.2,.3,.4,.5),lty=3,col=colors()[152])
abline(v=60,lty=2,col=1)
points(rp1[1,],rp1[2,],pch=16)
points(rp2[1,],rp2[2,],pch=16,col=2)
points(rp4[1,],rp4[2,],pch=16,col=3)
points(rp6[1,],rp6[2,],pch=16,col=4)
points(rp8[1,],rp8[2,],pch=16,col=5)
text(40,.55,'A',font=2,cex=1.5)
plot(rp1.2[1,],rp1.2[2,],xlim=c(50,100),pch=16,axes=F,type='n',xlab='Microsatellite length',ylab='Proportion of reads')
axis(2)
axis(1,at=seq(52,100,by=4))
abline(v=seq(52,100,by=8),lty=3,col=colors()[152])
abline(h=c(0,.2,.4,.6,.8),lty=3,col=colors()[152])
abline(v=80,lty=2,col=1)
points(rp1.2[1,],rp1.2[2,],pch=16,cex=1)
points(rp2.2[1,],rp2.2[2,],pch=16,col=2,cex=1)
points(rp4.2[1,],rp4.2[2,],pch=16,col=3,cex=1)
points(rp6.2[1,],rp6.2[2,],pch=16,col=4,cex=1)
points(rp8.2[1,],rp8.2[2,],pch=16,col=5,cex=1)
text(54,.89,'B',font=2,cex=1.5)
plot(rp1.3[1,],rp1.3[2,],xlim=c(24,52),pch=16,axes=F,type='n',xlab='Microsatellite length',ylab='Proportion of reads')
axis(2)
axis(1,at=seq(24,52,by=4))
abline(v=seq(24,52,by=8),lty=3,col=colors()[152])
abline(h=c(0,.2,.4,.6,.8,1),lty=3,col=colors()[152])
abline(v=40,lty=2,col=1)
points(rp1.3[1,],rp1.3[2,],pch=16,cex=1)
points(rp2.3[1,],rp2.3[2,],pch=16,col=2,cex=1)
points(rp4.3[1,],rp4.3[2,],pch=16,col=3,cex=1)
points(rp6.3[1,],rp6.3[2,],pch=16,col=4,cex=1)
points(rp8.3[1,],rp8.3[2,],pch=16,col=5,cex=1)
text(26,.99,'C',font=2,cex=1.5)
##########################################################
###summarize counts
###no pcr controls
np1<-table(c(pstI_4x10_l,ecoRI_4x10_l))/2
np1<-np1[np1>1]
np2<-table(pstI_4x20_l)
np2<-np2[np2>1]
np3<-table(ecoRI_2x30_l)
np3<-np3[np3>1]
###figure 2
windows(7,3)
par(mfrow=c(1,3))
par(cex.axis=1)
par(las=1)
par(mar=c(3,3,3,1))
plot(names(np3),np3,type='h',lwd=3,axes=F,main='A')
axis(2);axis(1,at=find.tix(np3,target=60))
plot(names(np2),np2,type='h',lwd=3,axes=F,main='B')
axis(2);axis(1,at=find.tix(np2,target=80))
plot(names(np1),np1,type='h',lwd=3,axes=F,main='C')
axis(2);axis(1,at=find.tix(np1,target=40))
###PCR treated
e1<-table(c(c10_241_2x30_l,c10_242_2x30_l))/2
e1<-e1[e1>1]
e2<-table(c(c10_131_4x20_l,c10_132_4x20_l))/2
e2<-e2[e2>1]
e3<-table(c(c10_131_4x10_l,c10_132_4x10_l,c10_241_4x10_l,c10_242_4x10_l))/4
e3<-e3[e3>1]
e4<-table(c(c20_241_2x30_l,c20_242_2x30_l))/2
e4<-e4[e4>1]
e5<-table(c(c20_131_4x20_l,c20_132_4x20_l))/2
e5<-e5[e5>1]
e6<-table(c(c20_131_4x10_l,c20_132_4x10_l,c20_241_4x10_l,c20_242_4x10_l))/4
e6<-e6[e6>1]
e7<-table(c(c30_241_2x30_l,c30_242_2x30_l))/2
e7<-e7[e7>1]
e8<-table(c(c30_131_4x20_l,c30_132_4x20_l))/2
e8<-e8[e8>1]
e9<-table(c(c30_131_4x10_l,c30_132_4x10_l,c30_241_4x10_l,c30_242_4x10_l))/4
e9<-e9[e9>1]
e10<-table(c(c40_241_2x30_l,c40_242_2x30_l))/2
e10<-e10[e10>1]
e11<-table(c(c40_131_4x20_l,c40_132_4x20_l))/2
e11<-e11[e11>1]
e12<-table(c(c40_131_4x10_l,c40_132_4x10_l,c40_241_4x10_l,c40_242_4x10_l))/4
e12<-e12[e12>1]
###figure 3
par(mfrow=c(4,3))
par(cex.axis=1)
par(las=0)
par(mar=c(3,3,2,1))
plot(names(e1),e1,type='h',lwd=3,axes=F,main='A')
axis(2);axis(1,at=find.tix(e1,int=4,target=60))
plot(names(e2),e2,type='h',lwd=3,axes=F,main='B')
axis(2);axis(1,at=find.tix(e2,target=80))
plot(names(e3),e3,type='h',lwd=3,axes=F,main='C')
axis(2);axis(1,at=find.tix(e3,target=40))
plot(names(e4),e4,type='h',lwd=3,axes=F,main='D')
axis(2);axis(1,at=find.tix(e4,int=4,target=60))
plot(names(e5),e5,type='h',lwd=3,axes=F,main='E')
axis(2);axis(1,at=find.tix(e5,int=4,target=80))
plot(names(e6),e6,type='h',lwd=3,axes=F,main='F')
axis(2);axis(1,at=find.tix(e6,target=40))
plot(names(e7),e7,type='h',lwd=3,axes=F,main='G')
axis(2);axis(1,at=find.tix(e7,int=4,target=60))
plot(names(e8),e8,type='h',lwd=3,axes=F,main='H')
axis(2);axis(1,at=find.tix(e8,int=4,target=80))
plot(names(e9),e9,type='h',lwd=3,axes=F,main='I')
axis(2);axis(1,at=find.tix(e9,target=40))
plot(names(e10),e10,type='h',lwd=3,axes=F,main='J')
axis(2);axis(1,at=find.tix(e10,int=4,target=60))
plot(names(e11),e11,type='h',lwd=3,axes=F,main='K')
axis(2);axis(1,at=find.tix(e11,int=4,target=80))
plot(names(e12),e12,type='h',lwd=3,axes=F,main='L')
axis(2);axis(1,at=find.tix(e12,target=40))
###########################################################
###absolute deviation from target length
a1<-fix.var(pstI_4x20_l,80)
a2m<-fix.var(c(c10_131_4x20_l,c10_132_4x20_l),80)
a2<-fix.var(c10_131_4x20_l,80)
a3<-fix.var(c10_132_4x20_l,80)
a4m<-fix.var(c(c20_131_4x20_l,c20_132_4x20_l),80)
a4<-fix.var(c20_131_4x20_l,80)
a5<-fix.var(c20_132_4x20_l,80)
a6m<-fix.var(c(c30_131_4x20_l,c30_132_4x20_l),80)
a6<-fix.var(c30_131_4x20_l,80)
a7<-fix.var(c30_132_4x20_l,80)
a8m<-fix.var(c(c40_131_4x20_l,c40_132_4x20_l),80)
a8<-fix.var(c40_131_4x20_l,80)
a9<-fix.var(c40_132_4x20_l,80)
facA<-c(rep('a',length(a1)),rep('b',length(a2m)),rep('c',length(a4m)),rep('d',length(a6m)),rep('e',length(a8m)))
nA<-c(rep(0,length(a1)),rep(10,length(a2m)),rep(20,length(a4m)),rep(30,length(a6m)),rep(40,length(a8m)))

A_ape<-c(a1,a2m,a4m,a6m,a8m)
nA_jitter<-jitter(nA,factor=2)[A_ape<=32]
A_ape<-A_ape[A_ape<=32]
a_jitter<-jitter(A_ape,factor=1)

wilcox.test(a1,a2m)
wilcox.test(a1,a4m)
wilcox.test(a1,a6m)
wilcox.test(a1,a8m)

###############
b1m<-fix.var(c(pstI_4x10_l,ecoRI_4x10_l),40)
b1<-fix.var(pstI_4x10_l,40)
b2<-fix.var(ecoRI_4x10_l,40)
b3m<-fix.var(c(c10_131_4x10_l,c10_132_4x10_l,c10_241_4x10_l,c10_242_4x10_l),40)
b3<-fix.var(c10_131_4x10_l,40)
b4<-fix.var(c10_132_4x10_l,40)
b5<-fix.var(c10_241_4x10_l,40)
b6<-fix.var(c10_242_4x10_l,40)
b7m<-fix.var(c(c20_131_4x10_l,c20_132_4x10_l,c20_241_4x10_l,c20_242_4x10_l),40)
b7<-fix.var(c20_131_4x10_l,40)
b8<-fix.var(c20_132_4x10_l,40)
b9<-fix.var(c20_241_4x10_l,40)
b10<-fix.var(c20_242_4x10_l,40)
b11m<-fix.var(c(c30_131_4x10_l,c30_132_4x10_l,c30_241_4x10_l,c30_242_4x10_l),40)
b11<-fix.var(c30_131_4x10_l,40)
b12<-fix.var(c30_132_4x10_l,40)
b13<-fix.var(c30_241_4x10_l,40)
b14<-fix.var(c30_242_4x10_l,40)
b15m<-fix.var(c(c40_131_4x10_l,c40_132_4x10_l,c40_241_4x10_l,c40_242_4x10_l),40)
b15<-fix.var(c40_131_4x10_l,40)
b16<-fix.var(c40_132_4x10_l,40)
b17<-fix.var(c40_241_4x10_l,40)
b18<-fix.var(c40_242_4x10_l,40)
facB<-c(rep('a',length(b1m)),rep('b',length(b3m)),rep('c',length(b7m)),rep('d',length(b11m)),rep('e',length(b15m)))
nB<-c(rep(0,length(b1m)),rep(10,length(b3m)),rep(20,length(b7m)),rep(30,length(b11m)),rep(40,length(b15m)))

wilcox.test(b1m,b3m)
wilcox.test(b1m,b7m)
wilcox.test(b1m,b11m)
wilcox.test(b1m,b15m)

B_ape<-c(b1m,b3m,b7m,b11m,b15m)
nB_jitter<-jitter(nB,factor=2)[B_ape<=16]
B_ape<-B_ape[B_ape<=16]
b_jitter<-jitter(B_ape,factor=1)


##################
c1<-fix.var(ecoRI_2x30_l,60)
c2m<-fix.var(c(c10_241_2x30_l,c10_242_2x30_l),60)
c2<-fix.var(c10_241_2x30_l,60)
c3<-fix.var(c10_242_2x30_l,60)
c4m<-fix.var(c(c20_241_2x30_l,c20_242_2x30_l),60)
c4<-fix.var(c20_241_2x30_l,60)
c5<-fix.var(c20_242_2x30_l,60)
c6m<-fix.var(c(c30_241_2x30_l,c30_242_2x30_l),60)
c6<-fix.var(c30_241_2x30_l,60)
c7<-fix.var(c30_242_2x30_l,60)
c8m<-fix.var(c(c40_241_2x30_l,c40_242_2x30_l),60)
c8<-fix.var(c40_241_2x30_l,60)
c9<-fix.var(c40_242_2x30_l,60)

facC<-c(rep('a',length(c1)),rep('b',length(c2)),rep('c',length(c4)),rep('d',length(c6)),rep('e',length(c8)))
nC<-c(rep(0,length(c1)),rep(10,length(c2m)),rep(20,length(c4m)),rep(30,length(c6m)),rep(40,length(c8m)))
nCm<-c(rep(0,length(c1)),rep(10,length(c2)),rep(10,length(c3)),rep(20,length(c4)),rep(20,length(c5)),
rep(30,length(c6)),rep(30,length(c7)),rep(40,length(c8)),rep(40,length(c9)))

wilcox.test(c1,c2m)
wilcox.test(c1,c4m)
wilcox.test(c1,c6m)
wilcox.test(c1,c8m)

C_ape<-c(c1,c2m,c4m,c6m,c8m)
length(C_ape)
nC_jitter<-jitter(nC,factor=2)[C_ape<=22]
C_ape<-C_ape[C_ape<=22]
c_jitter<-jitter(C_ape,factor=1)

###absolute error density plot (figS24)
windows(10,5)
par(mfrow=c(1,3))
par(mar=c(4,4,3,0.5))
plot(nC_jitter,c_jitter,pch=16,cex=.05,col=1,axes=F,xlab='# PCR cycles',ylab='Absolute error (bp)',main='A')
box();axis(1)
axis(2,at=seq(0,26,by=2))
lines(c(-4,4),c(mean(c1),mean(c1)),col=2,lwd=2)
lines(c(6,14),c(mean(c2m),mean(c2m)),col=2,lwd=2)
lines(c(16,24),c(mean(c4m),mean(c4m)),col=2,lwd=2)
lines(c(26,34),c(mean(c6m),mean(c6m)),col=2,lwd=2)
lines(c(36,44),c(mean(c8m),mean(c8m)),col=2,lwd=2)
plot(nA_jitter,a_jitter,pch=16,cex=.05,col=1,axes=F,xlab='# PCR cycles',ylab='Absolute error (bp)',main='B')
box();axis(1)
axis(2,at=seq(0,32,by=4))
lines(c(-4,4),c(mean(a1),mean(a1)),col=2,lwd=2)
lines(c(6,14),c(mean(a2m),mean(a2m)),col=2,lwd=2)
lines(c(16,24),c(mean(a4m),mean(a4m)),col=2,lwd=2)
lines(c(26,34),c(mean(a6m),mean(a6m)),col=2,lwd=2)
lines(c(36,44),c(mean(a8m),mean(a8m)),col=2,lwd=2)
plot(nB_jitter,b_jitter,pch=16,cex=.05,col=1,axes=F,xlab='# PCR cycles',ylab='Absolute error (bp)',main='C')
box();axis(1)
axis(2,at=seq(0,28,by=4))
lines(c(-4,4),c(mean(b1),mean(b1)),col=2,lwd=2)
lines(c(6,14),c(mean(b3m),mean(b3m)),col=2,lwd=2)
lines(c(16,24),c(mean(b7m),mean(b7m)),col=2,lwd=2)
lines(c(26,34),c(mean(b11m),mean(b11m)),col=2,lwd=2)
lines(c(36,44),c(mean(b15m),mean(b15m)),col=2,lwd=2)

###########################################
###shannon entropy
sa1<-shannon(pstI_4x20_l)
sa2<-shannon(c10_131_4x20_l)
sa3<-shannon(c10_132_4x20_l)
sa4<-shannon(c20_131_4x20_l)
sa5<-shannon(c20_132_4x20_l)
sa6<-shannon(c30_131_4x20_l)
sa7<-shannon(c30_132_4x20_l)
sa8<-shannon(c40_131_4x20_l)
sa9<-shannon(c40_132_4x20_l)

sb1<-shannon(pstI_4x10_l)
sb2<-shannon(ecoRI_4x10_l)
sb3<-shannon(c10_131_4x10_l)
sb4<-shannon(c10_132_4x10_l)
sb5<-shannon(c10_241_4x10_l)
sb6<-shannon(c10_242_4x10_l)
sb7<-shannon(c20_131_4x10_l)
sb8<-shannon(c20_132_4x10_l)
sb9<-shannon(c20_241_4x10_l)
sb10<-shannon(c20_242_4x10_l)
sb11<-shannon(c30_131_4x10_l)
sb12<-shannon(c30_132_4x10_l)
sb13<-shannon(c30_241_4x10_l)
sb14<-shannon(c30_242_4x10_l)
sb15<-shannon(c40_131_4x10_l)
sb16<-shannon(c40_132_4x10_l)
sb17<-shannon(c40_241_4x10_l)
sb18<-shannon(c40_242_4x10_l)

sc1<-shannon(ecoRI_2x30_l)
sc2<-shannon(c10_241_2x30_l)
sc3<-shannon(c10_242_2x30_l)
sc4<-shannon(c20_241_2x30_l)
sc5<-shannon(c20_242_2x30_l)
sc6<-shannon(c30_241_2x30_l)
sc7<-shannon(c30_242_2x30_l)
sc8<-shannon(c40_241_2x30_l)
sc9<-shannon(c40_242_2x30_l)

s_distr_4x20<-c(sa1,sa2,sa3,sa4,sa5,sa6,sa7,sa8,sa9)
cyc_4x20<-c(0,10,10,20,20,30,30,40,40)
s_distr_4x10<-c(sb1,sb2,sb3,sb4,sb5,sb6,sb7,sb8,sb9,sb10,sb11,sb12,sb13,sb14,sb15,sb16,sb17,sb18)
cyc_4x10<-c(0,0,rep(10,4),rep(20,4),rep(30,4),rep(40,4))
s_distr_2x30<-c(sc1,sc2,sc3,sc4,sc5,sc6,sc7,sc8,sc9)

###shannon vs pcr gams
library(mgcv)
gam1<-gam(s_distr_2x30~s(cyc_4x20,k=3))
summary(gam1)
gam1.2<-gam(s_distr_2x30[-1]~s(cyc_4x20[-1],k=3))
summary(gam1.2)

gam2<-gam(s_distr_4x20~s(cyc_4x20,k=3))
summary(gam2)
gam2.2<-gam(s_distr_4x20[-1]~s(cyc_4x20[-1],k=3))
summary(gam2.2)

gam3<-gam(s_distr_4x10~s(cyc_4x10,k=3))
summary(gam3)
gam3.2<-gam(s_distr_4x10[-c(1,2)]~s(cyc_4x10[-c(1,2)],k=3))
summary(gam3.2)

###Shannon entropy gam plots (fig S26)
windows(8,7)
par(mfrow=c(2,3))
par(mar=c(4,4,3,0.5))
plot(gam1,residuals=T,pch=16,shade=T,ylab='Shannon entropy',xlab='Nr. PCR cycles',shift=mean(s_distr_2x30),main='A',cex=1.5)
plot(gam2,residuals=T,pch=16,shade=T,ylab='Shannon entropy',xlab='Nr. PCR cycles',shift=mean(s_distr_4x20),main='B',cex=1.5)
plot(gam3,residuals=T,pch=16,shade=T,ylab='Shannon entropy',xlab='Nr. PCR cycles',shift=mean(s_distr_4x10),main='C',cex=1.5)
plot(gam1.2,residuals=T,pch=16,shade=T,ylab='Shannon entropy',xlab='Nr. PCR cycles',shift=mean(s_distr_2x30[-1]),main='D',cex=1.5)
plot(gam2.2,residuals=T,pch=16,shade=T,ylab='Shannon entropy',xlab='Nr. PCR cycles',shift=mean(s_distr_4x20[-1]),main='E',cex=1.5)
plot(gam3.2,residuals=T,pch=16,shade=T,ylab='Shannon entropy',xlab='Nr. PCR cycles',shift=mean(s_distr_4x10[-c(1,2)]),main='F',cex=1.5)

#######################################################
###percent correct allele size
pa1<-pct.corr(pstI_4x20_l,80)
pa2<-pct.corr(c10_131_4x20_l,80)
pa3<-pct.corr(c10_132_4x20_l,80)
pa4<-pct.corr(c20_131_4x20_l,80)
pa5<-pct.corr(c20_132_4x20_l,80)
pa6<-pct.corr(c30_131_4x20_l,80)
pa7<-pct.corr(c30_132_4x20_l,80)
pa8<-pct.corr(c40_131_4x20_l,80)
pa9<-pct.corr(c40_132_4x20_l,80)
p80<-c(pa1,pa2,pa3,pa4,pa5,pa6,pa7,pa8,pa9)

pb1<-pct.corr(pstI_4x10_l,40)
pb2<-pct.corr(ecoRI_4x10_l,40)
pb3<-pct.corr(c10_131_4x10_l,40)
pb4<-pct.corr(c10_132_4x10_l,40)
pb5<-pct.corr(c10_241_4x10_l,40)
pb6<-pct.corr(c10_242_4x10_l,40)
pb7<-pct.corr(c20_131_4x10_l,40)
pb8<-pct.corr(c20_132_4x10_l,40)
pb9<-pct.corr(c20_241_4x10_l,40)
pb10<-pct.corr(c20_242_4x10_l,40)
pb11<-pct.corr(c30_131_4x10_l,40)
pb12<-pct.corr(c30_132_4x10_l,40)
pb13<-pct.corr(c30_241_4x10_l,40)
pb14<-pct.corr(c30_242_4x10_l,40)
pb15<-pct.corr(c40_131_4x10_l,40)
pb16<-pct.corr(c40_132_4x10_l,40)
pb17<-pct.corr(c40_241_4x10_l,40)
pb18<-pct.corr(c40_242_4x10_l,40)
p40<-rbind(pb1,pb2,pb3,pb4,pb5,pb6,pb7,pb8,pb9,pb10,pb11,pb12,pb13,pb14,pb15,pb16,pb17,pb18)

pc1<-pct.corr(ecoRI_2x30_l,60)
pc2<-pct.corr(c10_241_2x30_l,60)
pc3<-pct.corr(c10_242_2x30_l,60)
pc4<-pct.corr(c20_241_2x30_l,60)
pc5<-pct.corr(c20_242_2x30_l,60)
pc6<-pct.corr(c30_241_2x30_l,60)
pc7<-pct.corr(c30_242_2x30_l,60)
pc8<-pct.corr(c40_241_2x30_l,60)
pc9<-pct.corr(c40_242_2x30_l,60)
p60<-rbind(pc1,pc2,pc3,pc4,pc5,pc6,pc7,pc8,pc9)

pgam1<-gam(p80~s(cyc_4x20,k=3))
summary(pgam1)
pgam2<-gam(p40~s(cyc_4x10,k=3))
summary(pgam2)
pgam3<-gam(p60~s(cyc_4x20,k=3))
summary(pgam3)
pgam1.2<-gam(p80[-1]~s(cyc_4x20[-1],k=3))
summary(pgam1.2)
pgam2.2<-gam(p40[-c(1,2)]~s(cyc_4x10[-c(1,2)],k=3))
summary(pgam2.2)
pgam3.2<-gam(p60[-1]~s(cyc_4x20[-1],k=3))
summary(pgam3.2)

###percent on target gam plots (Fig S25)
windows(8,7)
par(mfrow=c(2,3))
par(mar=c(4,4,3,0.5))
plot(pgam3,residuals=T,pch=16,shift=mean(p60),shade=T,cex=1.5,ylab='% target length',xlab='Nr. PCR cycles',main='A')
plot(pgam1,residuals=T,pch=16,shift=mean(p80),shade=T,cex=1.5,ylab='% target length',xlab='Nr. PCR cycles',main='B')
plot(pgam2,residuals=T,pch=16,shift=mean(p40),shade=T,cex=1.5,ylab='% target length',xlab='Nr. PCR cycles',main='C')
plot(pgam3.2,residuals=T,pch=16,shift=mean(p60),shade=T,cex=1.5,ylab='% target length',xlab='Nr. PCR cycles',main='D')
plot(pgam1.2,residuals=T,pch=16,shift=mean(p80),shade=T,cex=1.5,ylab='% target length',xlab='Nr. PCR cycles',main='E')
plot(pgam2.2,residuals=T,pch=16,shift=mean(p40),shade=T,cex=1.5,ylab='% target length',xlab='Nr. PCR cycles',main='F')
###############################################################
###pct on target and shannon entropy (Fig 4)
windows(8,5)
par(mfrow=c(1,2))
par(mar=c(4,4,3,0.5))
plot(cyc_4x20,p80,pch=16,ylim=c(35,100),cex=1,ylab='% target length',xlab='# PCR cycles',main='A')
points(cyc_4x10,p40,pch=16,col=2,cex=1)
points(cyc_4x20,p60,pch=16,col=3,cex=1)
abline(h=mean(p80),lty=2);abline(h=mean(p40),lty=2,col=2);abline(h=mean(p60),lty=2,col=3)
legend(25,60,legend=c('30 x CA','20 x ATCT','10 x ATCT'),col=c(3,1,2),pch=16,pt.cex=1,cex=.8)
plot(cyc_4x20,s_distr_4x20,pch=16,ylim=c(0,2),cex=1,ylab='Shannon entropy',xlab='# PCR cycles',main='B')
points(cyc_4x10,s_distr_4x10,pch=16,col=2,cex=1)
points(cyc_4x20,s_distr_2x30,pch=16,col=3,cex=1)
abline(h=mean(s_distr_4x20),lty=2);abline(h=mean(s_distr_4x10),lty=2,col=2);abline(h=mean(s_distr_2x30),lty=2,col=3)
legend(25,.3,legend=c('30 x CA','20 x ATCT','10 x ATCT'),col=c(3,1,2),pch=16,pt.cex=1,cex=.8)

###############################################################
###Single nucleotide slippage is more common forward than backward
fa1<-fwd.slip(pstI_4x20_l,80)
fa2<-fwd.slip(c10_131_4x20_l,80)
fa3<-fwd.slip(c10_132_4x20_l,80)
fa4<-fwd.slip(c20_131_4x20_l,80)
fa5<-fwd.slip(c20_132_4x20_l,80)
fa6<-fwd.slip(c30_131_4x20_l,80)
fa7<-fwd.slip(c30_132_4x20_l,80)
fa8<-fwd.slip(c40_131_4x20_l,80)
fa9<-fwd.slip(c40_132_4x20_l,80)
f80<-rbind(fa1,fa2,fa3,fa4,fa5,fa6,fa7,fa8,fa9)

wilcox.test(f80[,1],f80[,2],paired=T)

fb1<-fwd.slip(pstI_4x10_l,40)
fb2<-fwd.slip(ecoRI_4x10_l,40)
fb3<-fwd.slip(c10_131_4x10_l,40)
fb4<-fwd.slip(c10_132_4x10_l,40)
fb5<-fwd.slip(c10_241_4x10_l,40)
fb6<-fwd.slip(c10_242_4x10_l,40)
fb7<-fwd.slip(c20_131_4x10_l,40)
fb8<-fwd.slip(c20_132_4x10_l,40)
fb9<-fwd.slip(c20_241_4x10_l,40)
fb10<-fwd.slip(c20_242_4x10_l,40)
fb11<-fwd.slip(c30_131_4x10_l,40)
fb12<-fwd.slip(c30_132_4x10_l,40)
fb13<-fwd.slip(c30_241_4x10_l,40)
fb14<-fwd.slip(c30_242_4x10_l,40)
fb15<-fwd.slip(c40_131_4x10_l,40)
fb16<-fwd.slip(c40_132_4x10_l,40)
fb17<-fwd.slip(c40_241_4x10_l,40)
fb18<-fwd.slip(c40_242_4x10_l,40)
f40<-rbind(fb1,fb2,fb3,fb4,fb5,fb6,fb7,fb8,fb9,fb10,fb11,fb12,fb13,fb14,fb15,fb16,fb17,fb18)

wilcox.test(f40[,1],f40[,2],paired=T)

fc1<-fwd.slip(ecoRI_2x30_l,60)
fc2<-fwd.slip(c10_241_2x30_l,60)
fc3<-fwd.slip(c10_242_2x30_l,60)
fc4<-fwd.slip(c20_241_2x30_l,60)
fc5<-fwd.slip(c20_242_2x30_l,60)
fc6<-fwd.slip(c30_241_2x30_l,60)
fc7<-fwd.slip(c30_242_2x30_l,60)
fc8<-fwd.slip(c40_241_2x30_l,60)
fc9<-fwd.slip(c40_242_2x30_l,60)
f60<-rbind(fc1,fc2,fc3,fc4,fc5,fc6,fc7,fc8,fc9)

wilcox.test(f60[,1],f60[,2],paired=T)

###single nc slippage (figS29)
fs<-c(c(f60),c(f80),c(f40))
facs<-c(rep('a',9),rep('b',9),rep('c',9),rep('d',9),rep('e',18),rep('f',18))
windows(5,5)
boxplot(fs~facs,ylab='% of total reads',names=c('I','D','I','D','I','D'),col=c(3,3,'grey','grey',2,2))
legend(0.5,.5,legend=c('30 x CA','20 x ATCT','10 x ATCT'),fill=c(3,'grey',2),cex=.7)
######################################################
###forward vs backward slippage
fbc1.1<-fbc(ecoRI_2x30_l,60,2)
fbc1.2<-fbc(c10_241_2x30_l,60,2)
fbc1.3<-fbc(c10_242_2x30_l,60,2)
fbc1.4<-fbc(c20_241_2x30_l,60,2)
fbc1.5<-fbc(c20_242_2x30_l,60,2)
fbc1.6<-fbc(c30_241_2x30_l,60,2)
fbc1.7<-fbc(c30_242_2x30_l,60,2)
fbc1.8<-fbc(c40_241_2x30_l,60,2)
fbc1.9<-fbc(c40_242_2x30_l,60,2)

tot1<-rbind(fbc1.1[1,],fbc1.2[1,],fbc1.3[1,],fbc1.4[1,],fbc1.5[1,],fbc1.6[1,],fbc1.7[1,],fbc1.8[1,],fbc1.9[1,])
jump1<-rbind(fbc1.1[2,],fbc1.2[2,],fbc1.3[2,],fbc1.4[2,],fbc1.5[2,],fbc1.6[2,],fbc1.7[2,],fbc1.8[2,],fbc1.9[2,])
sing1<-rbind(fbc1.1[3,],fbc1.2[3,],fbc1.3[3,],fbc1.4[3,],fbc1.5[3,],fbc1.6[3,],fbc1.7[3,],fbc1.8[3,],fbc1.9[3,])
wilcox.test(tot1[,1],tot1[,2],paired=T)
wilcox.test(jump1[,1],jump1[,2],paired=T)
wilcox.test(sing1[,1],sing1[,2],paired=T)
###gams
fbmod1.f<-gam(sing1[,1]~s(cyc_4x20,k=3))
fbmod1.b<-gam(sing1[,2]~s(cyc_4x20,k=3))
summary(fbmod1.f)
summary(fbmod1.b)
fbmod1.fj<-gam(jump1[,1]~s(cyc_4x20,k=3))
fbmod1.bj<-gam(jump1[,2]~s(cyc_4x20,k=3))
summary(fbmod1.fj)
summary(fbmod1.bj)
fbmod1.fjnc<-gam(jump1[,1][-1]~s(cyc_4x20[-1],k=3))
fbmod1.bjnc<-gam(jump1[,2][-1]~s(cyc_4x20[-1],k=3))
summary(fbmod1.fjnc)
summary(fbmod1.bjnc)
###fwd to bwd ratios
mean(jump1[,2]/jump1[,1])
mean(sing1[,2]/sing1[,1])
mean(rowSums(jump1)/rowSums(sing1))
wilcox.test(rowSums(jump1),rowSums(sing1),paired=T)

####20x4
fbc2.1<-fbc(pstI_4x20_l,80,4)
fbc2.2<-fbc(c10_131_4x20_l,80,4)
fbc2.3<-fbc(c10_132_4x20_l,80,4)
fbc2.4<-fbc(c20_131_4x20_l,80,4)
fbc2.5<-fbc(c20_132_4x20_l,80,4)
fbc2.6<-fbc(c30_131_4x20_l,80,4)
fbc2.7<-fbc(c30_132_4x20_l,80,4)
fbc2.8<-fbc(c40_131_4x20_l,80,4)
fbc2.9<-fbc(c40_132_4x20_l,80,4)

tot2<-rbind(fbc2.1[1,],fbc2.2[1,],fbc2.3[1,],fbc2.4[1,],fbc2.5[1,],fbc2.6[1,],fbc2.7[1,],fbc2.8[1,],fbc2.9[1,])
jump2<-rbind(fbc2.1[2,],fbc2.2[2,],fbc2.3[2,],fbc2.4[2,],fbc2.5[2,],fbc2.6[2,],fbc2.7[2,],fbc2.8[2,],fbc2.9[2,])
sing2<-rbind(fbc2.1[3,],fbc2.2[3,],fbc2.3[3,],fbc2.4[3,],fbc2.5[3,],fbc2.6[3,],fbc2.7[3,],fbc2.8[3,],fbc2.9[3,])

wilcox.test(tot2[,1],tot2[,2],paired=T)
wilcox.test(jump2[,1],jump2[,2],paired=T)
wilcox.test(sing2[,1],sing2[,2],paired=T)
###gams
fbmod2.f<-gam(sing2[,1]~s(cyc_4x20,k=3))
fbmod2.b<-gam(sing2[,2]~s(cyc_4x20,k=3))
summary(fbmod2.f)
summary(fbmod2.b)
fbmod2.fj<-gam(jump2[,1]~s(cyc_4x20,k=3))
fbmod2.bj<-gam(jump2[,2]~s(cyc_4x20,k=3))
summary(fbmod2.fj)
summary(fbmod2.bj)
fbmod2.fjnc<-gam(jump2[,1][-1]~s(cyc_4x20[-1],k=3))
fbmod2.bjnc<-gam(jump2[,2][-1]~s(cyc_4x20[-1],k=3))
summary(fbmod2.fjnc)
summary(fbmod2.bjnc)
###ratios
mean(jump2[,2]/jump2[,1])
mean(sing2[,2]/sing2[,1])
mean(rowSums(jump2)/rowSums(sing2))
wilcox.test(rowSums(jump2),rowSums(sing2),paired=T)

###10x4
fbc3.1<-fbc(pstI_4x10_l,40,4)
fbc3.2<-fbc(ecoRI_4x10_l,40,4)
fbc3.3<-fbc(c10_131_4x10_l,40,4)
fbc3.4<-fbc(c10_132_4x10_l,40,4)
fbc3.5<-fbc(c10_241_4x10_l,40,4)
fbc3.6<-fbc(c10_242_4x10_l,40,4)
fbc3.7<-fbc(c20_131_4x10_l,40,4)
fbc3.8<-fbc(c20_132_4x10_l,40,4)
fbc3.9<-fbc(c20_241_4x10_l,40,4)
fbc3.10<-fbc(c20_242_4x10_l,40,4)
fbc3.11<-fbc(c30_131_4x10_l,40,4)
fbc3.12<-fbc(c30_132_4x10_l,40,4)
fbc3.13<-fbc(c30_241_4x10_l,40,4)
fbc3.14<-fbc(c30_242_4x10_l,40,4)
fbc3.15<-fbc(c40_131_4x10_l,40,4)
fbc3.16<-fbc(c40_132_4x10_l,40,4)
fbc3.17<-fbc(c40_241_4x10_l,40,4)
fbc3.18<-fbc(c40_242_4x10_l,40,4)
tot3<-rbind(fbc3.1[1,],fbc3.2[1,],fbc3.3[1,],fbc3.4[1,],fbc3.5[1,],fbc3.6[1,],fbc3.7[1,],fbc3.8[1,],fbc3.9[1,],
fbc3.10[1,],fbc3.11[1,],fbc3.12[1,],fbc3.13[1,],fbc3.14[1,],fbc3.15[1,],fbc3.16[1,],fbc3.17[1,],fbc3.18[1,])
jump3<-rbind(fbc3.1[2,],fbc3.2[2,],fbc3.3[2,],fbc3.4[2,],fbc3.5[2,],fbc3.6[2,],fbc3.7[2,],fbc3.8[2,],fbc3.9[2,],
fbc3.10[2,],fbc3.11[2,],fbc3.12[2,],fbc3.13[2,],fbc3.14[2,],fbc3.15[2,],fbc3.16[2,],fbc3.17[2,],fbc3.18[2,])
sing3<-rbind(fbc3.1[3,],fbc3.2[3,],fbc3.3[3,],fbc3.4[3,],fbc3.5[3,],fbc3.6[3,],fbc3.7[3,],fbc3.8[3,],fbc3.9[3,],
fbc3.10[3,],fbc3.11[3,],fbc3.12[3,],fbc3.13[3,],fbc3.14[3,],fbc3.15[3,],fbc3.16[3,],fbc3.17[3,],fbc3.18[3,])

wilcox.test(tot3[,1],tot3[,2],paired=T)
wilcox.test(jump3[,1],jump3[,2],paired=T)
wilcox.test(sing3[,1],sing3[,2],paired=T)
###gams
fbmod3.f<-gam(sing3[,1]~s(cyc_4x10,k=3))
fbmod3.b<-gam(sing3[,2]~s(cyc_4x10,k=3))
summary(fbmod3.f)
summary(fbmod3.b)
fbmod3.fj<-gam(jump3[,1]~s(cyc_4x10,k=3))
fbmod3.bj<-gam(jump3[,2]~s(cyc_4x10,k=3))
summary(fbmod3.fj)
summary(fbmod3.bj)
fbmod3.fjnc<-gam(jump3[,1][-c(1,2)]~s(cyc_4x10[-c(1,2)],k=3))
fbmod3.bjnc<-gam(jump3[,2][-c(1,2)]~s(cyc_4x10[-c(1,2)],k=3))
summary(fbmod3.fjnc)
summary(fbmod3.bjnc)
###ratios
mean(jump3[,2]/jump3[,1])
mean(sing3[,2]/sing3[,1])
mean(rowSums(jump3)/rowSums(sing3))
wilcox.test(rowSums(jump3),rowSums(sing3),paired=T)
###plots
###non-stepwise errors vs pcr regime (figS30)
windows(8,7)
par(mfrow=c(2,3))
par(mar=c(4,4,3,0.5))
plot(fbmod1.f,residuals=T,pch=16,shade=T,ylab='% of total reads',xlab='Nr. PCR cycles',shift=mean(sing1[,1]),main='A',cex=1.5)
plot(fbmod2.f,residuals=T,pch=16,shade=T,ylab='% of total reads',xlab='Nr. PCR cycles',shift=mean(sing2[,1]),main='B',cex=1.5)
plot(fbmod3.f,residuals=T,pch=16,shade=T,ylab='% of total reads',xlab='Nr. PCR cycles',shift=mean(sing3[,1]),main='C',cex=1.5)
plot(fbmod1.b,residuals=T,pch=16,shade=T,ylab='% of total reads',xlab='Nr. PCR cycles',shift=mean(sing1[,2]),main='D',cex=1.5)
plot(fbmod2.b,residuals=T,pch=16,shade=T,ylab='% of total reads',xlab='Nr. PCR cycles',shift=mean(sing2[,2]),main='E',cex=1.5)
plot(fbmod3.b,residuals=T,pch=16,shade=T,ylab='% of total reads',xlab='Nr. PCR cycles',shift=mean(sing3[,2]),main='F',cex=1.5)
###indel slippage vs. pcr regime including no-pcr control (figS27)
windows(8,7)
par(mfrow=c(2,3))
par(mar=c(4,4,3,0.5))
plot(fbmod1.bj,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump1[,2]),main='A',cex=1.5)
plot(fbmod2.bj,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump2[,2]),main='B',cex=1.5)
plot(fbmod3.bj,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump3[,2]),main='C',cex=1.5)
plot(fbmod1.fj,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump1[,1]),main='D',cex=1.5)
plot(fbmod2.fj,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump2[,1]),main='E',cex=1.5)
plot(fbmod3.fj,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump3[,1]),main='F',cex=1.5)
###indel slippage vs. pcr regime without no-pcr control (figS28)
windows(8,7)
par(mfrow=c(2,3))
par(mar=c(4,4,3,0.5))
plot(fbmod1.bjnc,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump1[,2][-1]),main='A',cex=1.5)
plot(fbmod2.bjnc,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump2[,2][-1]),main='B',cex=1.5)
plot(fbmod3.bjnc,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump3[,2][-c(1,2)]),main='C',cex=1.5)
plot(fbmod1.fjnc,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump1[,1][-1]),main='D',cex=1.5)
plot(fbmod2.fjnc,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump2[,1][-1]),main='E',cex=1.5)
plot(fbmod3.fjnc,residuals=T,pch=16,shade=T,ylab='% of reads',xlab='Nr. PCR cycles',shift=mean(jump3[,1][-c(1,2)]),main='F',cex=1.5)
##################################################################
###indel rates in spacers
###both constructs
id_rates_all<-rbind(ecoRI_s_id,pstI_s_id,
c10_131_s_id,c10_132_s_id,c10_241_s_id,c10_242_s_id,c20_131_s_id,c20_132_s_id,
c20_241_s_id,c20_242_s_id,c30_131_s_id,c30_132_s_id,c30_241_s_id,c30_242_s_id,
c40_131_s_id,c40_132_s_id,c40_241_s_id,c40_242_s_id)
###construct 1
id_rates_c1<-rbind(ecoRI_s_id,
c10_131_s_id,c10_132_s_id,c20_131_s_id,c20_132_s_id,
c30_131_s_id,c30_132_s_id,c40_131_s_id,c40_132_s_id)
###construct 2
id_rates_c2<-rbind(pstI_s_id,c10_241_s_id,c10_242_s_id,
c20_241_s_id,c20_242_s_id,c30_241_s_id,c30_242_s_id,c40_241_s_id,c40_242_s_id)
###indels vs pcr (figS31)
tot_cyc<-c(0,0,rep(10,4),rep(20,4),rep(30,4),rep(40,4))
windows()
plot(tot_cyc,id_rates_all[,3],ylab='% of total reads',xlab='Nr. PCR cycles',pch=16,cex=1.5)
tot_mod<-lm(id_rates_all[,3]~tot_cyc)
summary(tot_mod)
abline(tot_mod)
text(5,7.5,expression(paste(R^2,' = 0.35')))
text(5,7.3,expression(paste(beta,' = 0.04')))

###deletions vs insertions (Fig 5)
windows(8,5)
layout(matrix(c(1,2,3),ncol=3,byrow=T),widths=c(2,2,1))
layout.show(3)
par(las=2)
par(mar=c(6,4,2,1))
boxplot(c(jump1,jump2,jump3)~c(rep('a',9),rep('b',9),rep('c',9),rep('d',9),rep('e',18),rep('f',18)),ylab='% of total reads',main='A',names=c('30xCA-I','30xCA-D','20xATCT-I','20xATCT-D','10xATCT-I','10xATCT-D'))
boxplot(c(sing1,sing2,sing3)~c(rep('a',9),rep('b',9),rep('c',9),rep('d',9),rep('e',18),rep('f',18)),ylab='% of total reads',main='B',names=c('30xCA-I','30xCA-D','20xATCT-I','20xATCT-D','10xATCT-I','10xATCT-D'))
boxplot(id_rates_all[,1:2],ylab='% of total reads',names=c('Linker-I','Linker-D'),main='C')

###normalized for sequence length (figS32)
###indels per nucleotide
windows()
par(cex.axis=.8)
par(las=2)
boxplot(c(sing1/6000,sing2/8000,sing3/4000,c(id_rates_all[,4:5]))~c(rep('a',9),rep('b',9),rep('c',9),rep('d',9),rep('e',18),rep('f',18),rep('g',18),rep('h',18))
,ylab='Per base indel rate',names=c('30xCA-I','30xCA-D','20xATCT-I','20xATCT-D','10xATCT-I','10xATCT-D','Linker-I','Linker-D'))

#################################################################
###Full data barcharts (figs S4-S23)
#figS4
windows(4,4)
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.8)
barplot(table(ecoRI_2x30_l))
#figS5
windows(8,4)
par(mfrow=c(1,2))
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.8)
barplot(table(c10_241_2x30_l),main='A')
barplot(table(c10_242_2x30_l),main='B')
#figS6
barplot(table(c20_241_2x30_l),main='A')
barplot(table(c20_242_2x30_l),main='B')
#figS7
barplot(table(c30_241_2x30_l),main='A')
barplot(table(c30_242_2x30_l),main='B')
#figS8
barplot(table(c40_241_2x30_l),main='A')
barplot(table(c40_242_2x30_l),main='B')

#figS9
windows(4,4)
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.8)
barplot(table(pstI_4x20_l))
#figS10
windows(8,4)
par(mfrow=c(1,2))
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.8)
barplot(table(c10_131_4x20_l),main='A')
barplot(table(c10_132_4x20_l),main='B')
#figS11
windows(10,4)
par(mfrow=c(1,2))
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.7)
barplot(table(c20_131_4x20_l),main='A')
barplot(table(c20_132_4x20_l),main='B')
#figS12
windows(8,4)
par(mfrow=c(1,2))
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.7)
barplot(table(c30_131_4x20_l),main='A')
barplot(table(c30_132_4x20_l),main='B')
#figS13
barplot(table(c40_131_4x20_l),main='A')
barplot(table(c40_132_4x20_l),main='B')

#figS14
windows(4,4)
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.8)
barplot(table(pstI_4x10_l))
#figS15
windows(8,4)
par(mfrow=c(1,2))
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.8)
barplot(table(c10_131_4x10_l),main='A')
barplot(table(c10_132_4x10_l),main='B')
#figS16
barplot(table(c20_131_4x10_l),main='A')
barplot(table(c20_132_4x10_l),main='B')
#figS17
barplot(table(c30_131_4x10_l),main='A')
barplot(table(c30_132_4x10_l),main='B')
#figS18
barplot(table(c40_131_4x10_l),main='A')
barplot(table(c40_132_4x10_l),main='B')

#figS19
windows(4,4)
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.8)
barplot(table(ecoRI_4x10_l))
#figS20
windows(8,4)
par(mfrow=c(1,2))
par(las=2)
par(mar=c(3,3,3,1))
par(cex.axis=.8)
barplot(table(c10_241_4x10_l),main='A')
barplot(table(c10_242_4x10_l),main='B')
#figS21
barplot(table(c20_241_4x10_l),main='A')
barplot(table(c20_242_4x10_l),main='B')
#figS22
barplot(table(c30_241_4x10_l),main='A')
barplot(table(c30_242_4x10_l),main='B')
#figS23
barplot(table(c40_241_4x10_l),main='A')
barplot(table(c40_242_4x10_l),main='B')

###compare 10xATCT scoring fidelity between constructs
d1_1<-fix.var(c(c10_131_4x10_l,c10_132_4x10_l),40)
d1_4<-fix.var(c(c10_241_4x10_l,c10_242_4x10_l),40)
wilcox.test(d1_1,d1_4)

d2_1<-fix.var(c(c20_131_4x10_l,c20_132_4x10_l),40)
d2_4<-fix.var(c(c20_241_4x10_l,c20_242_4x10_l),40)
wilcox.test(d2_1,d2_4)

d3_1<-fix.var(c(c30_131_4x10_l,c30_132_4x10_l),40)
d3_4<-fix.var(c(c30_241_4x10_l,c30_242_4x10_l),40)
wilcox.test(d3_1,d3_4)

d4_1<-fix.var(c(c40_131_4x10_l,c40_132_4x10_l),40)
d4_4<-fix.var(c(c40_241_4x10_l,c40_242_4x10_l),40)
wilcox.test(d4_1,d4_4)

###Look for PCR saturation
###Qbit x3. x100 dilution. concentrations in ng/ml. Construct 1.
x10pcr_1<-c(231,269,263)*0.1
x20pcr_1<-c(407,401,446)*0.1
x30pcr_1<-c(496,510,510)*0.1
x40pcr_1<-c(530,520,500)*0.1
c1_pcr<-rbind(x10pcr_1,x20pcr_1,x30pcr_1,x40pcr_1)
c1_pcr_m<-rowMeans(c1_pcr)
c1_pcr_se<-apply(c1_pcr,1,function(x)sd(x)/sqrt(3))
pcr_cyc<-c(10,20,30,40)
windows(8,4)
par(mfrow=c(1,2))
plot(pcr_cyc,c1_pcr_m,ylab="Concentration (ng/ul)",xlab="# PCR cycles",pch=16,ylim=c(23,53),cex=1,main='A')
for(i in 1:4){
lines(c(pcr_cyc[i],pcr_cyc[i]),c(c1_pcr_m[i]-c1_pcr_se[i],c1_pcr_m[i]+c1_pcr_se[i]))
}

###Qbit x3. x100 dilution. concentrations in ng/ml. Construct 2.
x10pcr_2<-c(65,67,61)*0.1
x20pcr_2<-c(117,118,128)*0.1
x30pcr_2<-c(363,383,379)*0.1
x40pcr_2<-c(425,422,444)*0.1

c2_pcr<-rbind(x10pcr_2,x20pcr_2,x30pcr_2,x40pcr_2)
c2_pcr_m<-rowMeans(c2_pcr)
c2_pcr_se<-apply(c2_pcr,1,function(x)sd(x)/sqrt(3))
plot(pcr_cyc,c2_pcr_m,ylab="Concentration (ng/ul)",xlab="# PCR cycles",pch=16,cex=1,main='B')
for(i in 1:4){
lines(c(pcr_cyc[i],pcr_cyc[i]),c(c2_pcr_m[i]-c2_pcr_se[i],c2_pcr_m[i]+c2_pcr_se[i]))
}

######################################################
###single nc slippage (figS29) updated
fs<-c(c(f60),c(f80),c(f40),c(id_rates_all[,7:8]))
facs<-c(rep('a',9),rep('b',9),rep('c',9),rep('d',9),rep('e',18),rep('f',18),rep('g',18),rep('h',18))
windows(5,5)
boxplot(fs~facs,ylab='% of total reads',names=c('I','D','I','D','I','D','I','D'),col=c(3,3,'grey','grey',2,2,4,4))
legend(0.5,5,legend=c('30 x CA','20 x ATCT','10 x ATCT','Linker'),fill=c(3,'grey',2,4),cex=.7)

wilcox.test(id_rates_all[,7],id_rates_all[,8],paired=T)

#########################################
####Figures 1-3 with minimal legends
###Panel plot (Fig 1)
windows(8,4)
layout(matrix(c(1,1,1,2,3,4),ncol=3,byrow=T),heights=c(1,5,5,5))
layout.show(4)
par(mar=c(0,0,0,0))
plot(1:10,1:10,type='n',xlab='',ylab='',axes=F)
legend(2.5,8,legend=c('No PCR','10 cycles','20 cycles','30 cycles','40 cycles'),bg=colors()[250],ncol=5,pch=16,pt.cex=2,col=1:5,cex=1.5)
par(mar=c(4,4,1,1))
plot(rp1[1,],rp1[2,],xlim=c(38,70),pch=16,axes=F,type='n',xlab='Microsatellite length',ylab='Proportion of reads')
axis(2)
axis(1,at=seq(38,70,by=2))
abline(v=seq(38,70,by=4),lty=3,col=colors()[152])
abline(h=c(0,.1,.2,.3,.4,.5),lty=3,col=colors()[152])
abline(v=60,lty=2,col=1)
points(rp1[1,],rp1[2,],pch=16)
points(rp2[1,],rp2[2,],pch=16,col=2)
points(rp4[1,],rp4[2,],pch=16,col=3)
points(rp6[1,],rp6[2,],pch=16,col=4)
points(rp8[1,],rp8[2,],pch=16,col=5)
text(40,.55,'A',font=2,cex=1.5)
text(42,.45,'30xCA',font=2)
plot(rp1.2[1,],rp1.2[2,],xlim=c(50,100),pch=16,axes=F,type='n',xlab='Microsatellite length',ylab='Proportion of reads')
axis(2)
axis(1,at=seq(52,100,by=4))
abline(v=seq(52,100,by=8),lty=3,col=colors()[152])
abline(h=c(0,.2,.4,.6,.8),lty=3,col=colors()[152])
abline(v=80,lty=2,col=1)
points(rp1.2[1,],rp1.2[2,],pch=16,cex=1)
points(rp2.2[1,],rp2.2[2,],pch=16,col=2,cex=1)
points(rp4.2[1,],rp4.2[2,],pch=16,col=3,cex=1)
points(rp6.2[1,],rp6.2[2,],pch=16,col=4,cex=1)
points(rp8.2[1,],rp8.2[2,],pch=16,col=5,cex=1)
text(54,.89,'B',font=2,cex=1.5)
text(58,.7,'20xATCT',font=2)
plot(rp1.3[1,],rp1.3[2,],xlim=c(24,52),pch=16,axes=F,type='n',xlab='Microsatellite length',ylab='Proportion of reads')
axis(2)
axis(1,at=seq(24,52,by=4))
abline(v=seq(24,52,by=8),lty=3,col=colors()[152])
abline(h=c(0,.2,.4,.6,.8,1),lty=3,col=colors()[152])
abline(v=40,lty=2,col=1)
points(rp1.3[1,],rp1.3[2,],pch=16,cex=1)
points(rp2.3[1,],rp2.3[2,],pch=16,col=2,cex=1)
points(rp4.3[1,],rp4.3[2,],pch=16,col=3,cex=1)
points(rp6.3[1,],rp6.3[2,],pch=16,col=4,cex=1)
points(rp8.3[1,],rp8.3[2,],pch=16,col=5,cex=1)
text(26,.99,'C',font=2,cex=1.5)
text(30,.7,'10xATCT',font=2)
##########################################################
###figure 2
windows(7,3)
par(mfrow=c(1,3))
par(cex.axis=1)
par(las=1)
par(mar=c(3,3,3,1))
plot(names(np3),np3,type='h',lwd=3,axes=F,main='A')
axis(2);axis(1,at=find.tix(np3,target=60))
text(55,1300,'30xCA')
plot(names(np2),np2,type='h',lwd=3,axes=F,main='B')
axis(2);axis(1,at=find.tix(np2,target=80))
text(72,2250,'20xATCT')
plot(names(np1),np1,type='h',lwd=3,axes=F,main='C')
axis(2);axis(1,at=find.tix(np1,target=40))
text(37.5,2250,'10xATCT')
###figure 3
par(mfrow=c(4,3))
par(cex.axis=1)
par(las=0)
par(mar=c(3,3,2,1))
plot(names(e1),e1,type='h',lwd=3,axes=F,main='A')
axis(2);axis(1,at=find.tix(e1,int=4,target=60))
text(53,250,'30xCA');text(53,200,'10 cyc.')
plot(names(e2),e2,type='h',lwd=3,axes=F,main='B')
axis(2);axis(1,at=find.tix(e2,target=80))
text(62,300,'20xATCT');text(62,250,'10 cyc.')
plot(names(e3),e3,type='h',lwd=3,axes=F,main='C')
axis(2);axis(1,at=find.tix(e3,target=40))
text(34,450,'10xATCT');text(34,380,'10 cyc.')
plot(names(e4),e4,type='h',lwd=3,axes=F,main='D')
axis(2);axis(1,at=find.tix(e4,int=4,target=60))
text(51,600,'30xCA');text(51,500,'20 cyc.')
plot(names(e5),e5,type='h',lwd=3,axes=F,main='E')
axis(2);axis(1,at=find.tix(e5,int=4,target=80))
text(56,2500,'20xATCT');text(56,2000,'20 cyc.')
plot(names(e6),e6,type='h',lwd=3,axes=F,main='F')
axis(2);axis(1,at=find.tix(e6,target=40))
text(35,2300,'10xATCT');text(35,1900,'20 cyc.')
plot(names(e7),e7,type='h',lwd=3,axes=F,main='G')
axis(2);axis(1,at=find.tix(e7,int=4,target=60))
text(45,500,'30xCA');text(45,400,'30 cyc.')
plot(names(e8),e8,type='h',lwd=3,axes=F,main='H')
axis(2);axis(1,at=find.tix(e8,int=4,target=80))
text(62,600,'20xATCT');text(62,500,'30 cyc.')
plot(names(e9),e9,type='h',lwd=3,axes=F,main='I')
axis(2);axis(1,at=find.tix(e9,target=40))
text(35,1000,'10xATCT');text(35,850,'30 cyc.')
plot(names(e10),e10,type='h',lwd=3,axes=F,main='J')
axis(2);axis(1,at=find.tix(e10,int=4,target=60))
text(53,200,'30xCA');text(53,165,'40 cyc.')
plot(names(e11),e11,type='h',lwd=3,axes=F,main='K')
axis(2);axis(1,at=find.tix(e11,int=4,target=80))
text(72,400,'20xATCT');text(72,340,'40 cyc.')
plot(names(e12),e12,type='h',lwd=3,axes=F,main='L')
axis(2);axis(1,at=find.tix(e12,target=40))
text(37.5,500,'10xATCT');text(37.5,400,'40 cyc.')