SUFFIX = 'HCSS_Afilt32m70_01'

Correlate genetic and epigenetic distances¶

For all sites¶

pwd

/scratch/t.cri.ksilliman/CommonG_cp2/2019_Mapping/ANGSD_run/HCSS

head ../../paper-oly-mbdbs-gen/analyses/methylation-filtered/dist.manhat.csv

"SeqNum.row","SeqNum.col","dist.manh","SampNum.row","SampNum.col"
1,1,0,"hc1_2","hc1_2"
1,15,273985.441785306,"ss3_15","hc1_2"
1,3,290189.490642819,"hc2_15","hc1_2"
1,17,285476.927245925,"ss3_20","hc1_2"
1,4,281520.711361473,"hc2_17","hc1_2"
1,2,298303.029739484,"hc1_4","hc1_2"
1,7,298537.38668563,"hc3_7","hc1_2"
1,16,284208.486947856,"ss3_16","hc1_2"
1,14,281305.331808996,"ss3_14","hc1_2"

#read in epigenetic Manhattan distance, 10 coverage 
library(spaa)
ep10 <-read.csv("../../paper-oly-mbdbs-gen/analyses/methylation-filtered/dist.manhat.csv",header = T)
ep10 <- ep10[,c("SampNum.row","SampNum.col","dist.manh")]
ep10 <- as.matrix(list2dist(ep10))
mbdorder = c("hc1_2","hc1_4","hc2_15","hc2_17","hc3_1","hc3_5","hc3_7","hc3_10","hc3_11",
        "ss2_9","ss2_14","ss2_18","ss3_3","ss3_14","ss3_15","ss3_16","ss3_20","ss5_18")
ep10 <- ep10[mbdorder, mbdorder]
ep10
Dep10 <- as.dist(ep10)

%expand
# read in genetic distances
gen <- read.table("Results/{SUFFIX}_mbd.dist", row.names = 1, header=T)
gen
Dgen <- as.dist(gen)

#library(MASS)
#dens <- kde2d(Dgeo,DgenN, n=300)
#myPal <- colorRampPalette(c("white","blue","gold", "orange", "red"))
plot(Dgen, Dep10, pch=20,cex=1,xlab = "Genetic distance", ylab = "MBD Manhattan distance")
summary(lm(Dep10~Dgen),)
R2 = round(summary(lm(Dep10~Dgen))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(Dep10~Dgen))
text(0.13,320000,label=paste("R^2=",R2))
text(0.13,310000,label="r=0.31")

Call:
lm(formula = Dep10 ~ Dgen)

Residuals:
   Min     1Q Median     3Q    Max 
-53094 -17259   -937  18870  54443 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   229505      15591  14.720  < 2e-16 ***
Dgen          299862      75420   3.976 0.000108 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 24320 on 151 degrees of freedom
Multiple R-squared:  0.09477,	Adjusted R-squared:  0.08877 
F-statistic: 15.81 on 1 and 151 DF,  p-value: 0.0001084

cor.test( ~ Dep10 + Dgen,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ Dep10 + Dgen, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  Dep10 and Dgen
t = 3.9759, df = 151, p-value = 0.0001084
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.1568240 0.4447926
sample estimates:
      cor 
0.3078416

	Spearman's rank correlation rho

data:  Dep10 and Dgen
S = 444614, p-value = 0.001505
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.2551332

For DMLs¶

#read in epigenetic Manhattan distance, 10 coverage 
library(spaa)
ep10 <-read.csv("../../paper-oly-mbdbs-gen/analyses/methylation-filtered/dist.manhat.DMLs.csv",header = T)
ep10 <- ep10[,c("SampNum.row","SampNum.col","dist.manh")]
ep10 <- as.matrix(list2dist(ep10))
mbdorder = c("hc1_2","hc1_4","hc2_15","hc2_17","hc3_1","hc3_5","hc3_7","hc3_10","hc3_11",
        "ss2_9","ss2_14","ss2_18","ss3_3","ss3_14","ss3_15","ss3_16","ss3_20","ss5_18")
ep10 <- ep10[mbdorder, mbdorder]
ep10
Dep10 <- as.dist(ep10)

#library(MASS)
#dens <- kde2d(Dgeo,DgenN, n=300)
#myPal <- colorRampPalette(c("white","blue","gold", "orange", "red"))
plot(Dgen, Dep10, pch=20,cex=1,xlab = "Genetic distance", ylab = "MBD Manhattan distance, DMLs")
summary(lm(Dep10~Dgen),)
R2 = round(summary(lm(Dep10~Dgen))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(Dep10~Dgen))
text(0.13,16000,label=paste("R^2=",R2))
text(0.13,15000,label="r=0.68")

Call:
lm(formula = Dep10 ~ Dgen)

Residuals:
   Min     1Q Median     3Q    Max 
 -5032  -1730    -81   1508   4874 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -3343       1374  -2.433   0.0161 *  
Dgen           75762       6646  11.400   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2143 on 151 degrees of freedom
Multiple R-squared:  0.4625,	Adjusted R-squared:  0.459 
F-statistic:   130 on 1 and 151 DF,  p-value: < 2.2e-16

cor.test( ~ Dep10 + Dgen,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ Dep10 + Dgen, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  Dep10 and Dgen
t = 11.529, df = 151, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5895371 0.7603386
sample estimates:
      cor 
0.6842097

	Spearman's rank correlation rho

data:  Dep10 and Dgen
S = 209532, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.6489687

Correlate Pst and Fst¶

For overlapping genes¶

All genes with methylation data¶

head ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop.tab
wc -l ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop.tab

"Quant_Varia"	"contig_gene"	"start_gene_2kb"	"end_gene_2kb"	"Pst_Values"
"Contig0_10497_95068"	"Contig0"	"10497"	"95068"	0.019332886629221
"Contig100_48322_58076"	"Contig100"	"48322"	"58076"	0.067008101478262
"Contig100188_1_4123"	"Contig100188"	"1"	"4123"	0.734962005060689
"Contig100199_5411_27437"	"Contig100199"	"5411"	"27437"	0.0682501114385254
"Contig100396_1_4908"	"Contig100396"	"1"	"4908"	0.185205868587485
"Contig100499_508_4044"	"Contig100499"	"508"	"4044"	0.150423262664518
"Contig1006_1_13779"	"Contig1006"	"1"	"13779"	0.0880097439836617
"Contig100691_1_8538"	"Contig100691"	"1"	"8538"	0.0188795032121047
"Contig10074_2260_6621"	"Contig10074"	"2260"	"6621"	0.251312849756899
3755 ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop.tab

# read in Pst data
pst <- read.csv("../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop.tab", sep="\t",header=T,stringsAsFactors = F)
colnames(pst) <- c("id","contig","start","end","pst")
head(pst)

wc -l SFS/HCSS_sfsm70_PerGeneFst.csv

1387 SFS/HCSS_sfsm70_PerGeneFst.csv

list.of.packages <- c("reshape2","dplyr", "tidyr", "readr", "stringr", "plotly","tidyverse","lfmm") #add new libraries here 
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

# Load all libraries 
lapply(list.of.packages, FUN = function(X) {
  do.call("require", list(X)) 
})

Loading required package: reshape2
Error: package or namespace load failed for 'reshape2' in dyn.load(file, DLLpath = DLLpath, ...):
 unable to load shared object '/home/t.cri.ksilliman/miniconda3/lib/R/library/stringi/libs/stringi.so':
  libicui18n.so.58: cannot open shared object file: No such file or directory
Loading required package: dplyr

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: tidyr
Loading required package: readr
Loading required package: stringr
Error: package or namespace load failed for 'stringr' in dyn.load(file, DLLpath = DLLpath, ...):
 unable to load shared object '/home/t.cri.ksilliman/miniconda3/lib/R/library/stringi/libs/stringi.so':
  libicui18n.so.58: cannot open shared object file: No such file or directory
Loading required package: plotly
Loading required package: ggplot2

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

Loading required package: tidyverse
Error: package or namespace load failed for 'tidyverse' in dyn.load(file, DLLpath = DLLpath, ...):
 unable to load shared object '/home/t.cri.ksilliman/miniconda3/lib/R/library/stringi/libs/stringi.so':
  libicui18n.so.58: cannot open shared object file: No such file or directory
Loading required package: lfmm
Error: package or namespace load failed for 'lfmm' in dyn.load(file, DLLpath = DLLpath, ...):
 unable to load shared object '/home/t.cri.ksilliman/miniconda3/lib/R/library/Matrix/libs/Matrix.so':
  libRlapack.so: cannot open shared object file: No such file or directory

genes <- 
  read_delim(file = "SFS/HCSS_sfsm70_PerGeneFst.csv", delim = "\t", col_names = TRUE)
head(genes)

Parsed with column specification:
cols(
  id = col_character(),
  contig = col_character(),
  start = col_double(),
  end = col_double(),
  notes_gene = col_character(),
  fst01 = col_double()
)

length(intersect(pst$id,genes$id))

both = merge(pst,genes, by ="id")
head(both)

3755 genes with Pst value, 1387 genes with Fst value, 237 overlapping genes¶

#library(MASS)
#dens <- kde2d(Dgeo,DgenN, n=300)
#myPal <- colorRampPalette(c("white","blue","gold", "orange", "red"))
plot(both$fst01, both$pst, pch=20,cex=1,xlab = "Fst", ylab = "Pst")
title("Pst vs Fst, 2kbslop all genes w/meth data, 237 genes")
summary(lm(both$pst~both$fst01),)
R2 = round(summary(lm(both$pst~both$fst01))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(both$pst~both$fst01))
text(0.4,0.8,label=paste("R^2=",R2))
text(0.4,0.75,label="r= 0.018")

Call:
lm(formula = both$pst ~ both$fst01)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.27839 -0.19593 -0.06751  0.14782  0.66175 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.26639    0.01780  14.965   <2e-16 ***
both$fst01   0.04315    0.14978   0.288    0.774    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2282 on 235 degrees of freedom
Multiple R-squared:  0.0003531,	Adjusted R-squared:  -0.003901 
F-statistic: 0.08301 on 1 and 235 DF,  p-value: 0.7735

cor.test( ~ both$pst + both$fst01,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ both$pst + both$fst01, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  both$pst and both$fst01
t = 0.28812, df = 235, p-value = 0.7735
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1088997  0.1458724
sample estimates:
       cor 
0.01879138

Warning message in cor.test.default(x = c(0.019332886629221, 0.251312849756899, :
"Cannot compute exact p-value with ties"

	Spearman's rank correlation rho

data:  both$pst and both$fst01
S = 2264910, p-value = 0.7494
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.02085675

Get gene annotations for these overlaps¶

Hih Pst, low Fst

both[which(both$fst01 <0.01 & both$pst >0.5),c(1,5,10,9)]

Low Pst, high Fst

both[which(both$fst01 >0.3 & both$pst <0.1),c(1,5,10,9)]

High Fst, high Pst

both[which(both$fst01 >0.3 & both$pst >0.3),c(1,5,10,9)]

Get 1st and last 10% of genes as bed file¶

sfsSuf = "SFS/HCSS_sfsm70"

module load gcc/6.2.0
module load xz/5.2.2 
module load bzip2/1.0.6
module load bedtools/2.29.0

# extracting gene regions out of genome annotations file (gff3)
cat ../../paper-oly-mbdbs-gen/genome-features/Olurida_v081-20190709.gene.gff | cut -f 1,4,5,9 > gene_regions.tab

%expand
# reading gene regions
genes=read.table("gene_regions.tab",sep="\t")
names(genes)=c("contig","start","end","notes")

genes$pct15 <- as.integer((genes$end - genes$start)*0.15)
genes$start15 <- genes$start +genes$pct15
genes$end15 <- genes$end - genes$pct15
head(genes)

pct15genes <- genes[,c("contig","start","start15","end15", "end")]
head(pct15genes)

write.table(pct15genes,"pct15genes.tab",sep="\t",quote = F,row.names = F)

%expand
fst01=read.table("SFS/HCSS_sfsm80_fst2pop.fst")
names(fst01)=c("contig","pos","a","b")

fst01$contig=as.character(fst01$contig)

# removing zero-only (invariant) bases
fst01[,3:4]=round(fst01[,3:4],3)
ch01=apply(fst01[,3:4],1,sum)
chh=(ch01>0)
table(chh)

fst01=fst01[chh,]
head(fst01)

chh
 FALSE   TRUE 
328905   5252

# computing weighted Fst per gene segment (1)
i=1;gfst01=c();ns01=0;snp=c()
pb=txtProgressBar(0,nrow(pct15genes))
for (i in 1:nrow(pct15genes)) {
	setTxtProgressBar(pb,i)
	sub=subset(fst01,contig==pct15genes$contig[i] & ((pos>=pct15genes$start[i] & pos<=pct15genes$start15[i]) | (pos>=pct15genes$end15[i] & pos<=pct15genes$end[i])))
    if (is.null(sub[1,1]) | sum(sub$b)==0) { 
		gfst01=append(gfst01,NA)
	} else {
        gfst01=append(gfst01,sum(sub$a)/sum(sub$b))
		ns01=ns01+nrow(sub)
        snp=append(snp,paste(sub$contig,sub$pos,sep="_"))
	}
}

================================================================================

185 SNPs found in the 1st or last 15% of gene¶

length(snp)

# density plots of per-gene Fst
plot(density(na.omit(gfst01)))

%expand
# adding results to genes table, saving
pct15genes$fst01=gfst01
id <- paste(pct15genes$contig,pct15genes$start,pct15genes$end,sep="_")
pct15genes <- cbind(id,pct15genes)
head(pct15genes[!is.na(pct15genes$fst01),])
save(pct15genes,file="{sfsSuf}_PerGeneFst.RData")

%expand
# saving table of per-gene Fst for GO_MWU analysis
f01=pct15genes[!is.na(pct15genes$fst01),]
nrow(f01)
write.table(f01,file="{sfsSuf}_15pctGeneFst.csv",quote=F,row.names=F,sep="\t")

5 methylated sites per gene (old)¶

grep "Contig20413" Results/fstpst_2kbslop.genes

Contig20413	maker	gene	486	15467	.	-	.	ID=OLUR_00010706;Name=OLUR_00010706;Alias=maker-Contig20413-snap-gene-0.3;Note=Similar to sll1388: Universal stress protein Sll1388 (Synechocystis sp. (strain PCC 6803 / Kazusa) OX%3D1111708);Dbxref=CDD:cd00293,Coils:Coil,Gene3D:G3DSA:3.40.50.620,InterPro:IPR006015,InterPro:IPR006016,InterPro:IPR014729,PRINTS:PR01438,Pfam:PF00582,SUPERFAMILY:SSF52402;	Contig20413	485	15467	0.472081218274112_0.0207245979022085

head ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop.tab
wc -l ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop.tab

"Quant_Varia"	"contig_gene"	"start_gene_2kb"	"end_gene_2kb"	"Pst_Values"
"Contig0_10497_95068"	"Contig0"	"10497"	"95068"	0.019332886629221
"Contig100188_1_4123"	"Contig100188"	"1"	"4123"	0.734962005060689
"Contig100396_1_4908"	"Contig100396"	"1"	"4908"	0.185205868587485
"Contig100994_1_8602"	"Contig100994"	"1"	"8602"	0.701143568206082
"Contig1013_14071_19514"	"Contig1013"	"14071"	"19514"	0.0172219569940594
"Contig1013_16204_21663"	"Contig1013"	"16204"	"21663"	0.0395933279259626
"Contig101333_3371_8675"	"Contig101333"	"3371"	"8675"	0.712270890827424
"Contig10144_1_8748"	"Contig10144"	"1"	"8748"	0.229975030080655
"Contig101661_1_5354"	"Contig101661"	"1"	"5354"	0.345600927439141
1394 ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop.tab

# read in Pst data
pst <- read.csv("../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop.tab", sep="\t",header=T,stringsAsFactors = F)
colnames(pst) <- c("id","contig","start","end","pst")
head(pst)

wc -l SFS/HCSS_sfsm70_PerGeneFst.csv

1387 SFS/HCSS_sfsm70_PerGeneFst.csv

# read in Fst data
fst <- read.csv("SFS/HCSS_sfsm70_PerGeneFst.csv",stringsAsFactors = F)
head(fst)

length(intersect(pst$id,fst$id))

1394 genes with Pst values, 1387 genes with Fst values (including those with Fst=0), only 64 overlapping genes¶

both = merge(pst,fst, by ="id")
both <- both[,c("id","pst","fst01")]
head(both)

#library(MASS)
#dens <- kde2d(Dgeo,DgenN, n=300)
#myPal <- colorRampPalette(c("white","blue","gold", "orange", "red"))
plot(both$fst, both$pst, pch=20,cex=1,xlab = "Fst", ylab = "Pst")
title("Pst vs Fst, 2kbslop_5loci, 64 genes")
summary(lm(both$pst~both$fst),)
R2 = round(summary(lm(both$pst~both$fst))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(both$pst~both$fst))
text(0.4,0.8,label=paste("R^2=",R2))
text(0.4,0.75,label="r= 0.08")

Call:
lm(formula = both$pst ~ both$fst)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.29403 -0.17988 -0.07144  0.12928  0.60292 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.24004    0.03382   7.098 1.45e-09 ***
both$fst     0.18060    0.28676   0.630    0.531    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2252 on 62 degrees of freedom
Multiple R-squared:  0.006357,	Adjusted R-squared:  -0.009669 
F-statistic: 0.3967 on 1 and 62 DF,  p-value: 0.5311

cor.test( ~ both$pst + both$fst,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ both$pst + both$fst, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  both$pst and both$fst
t = 0.62981, df = 62, p-value = 0.5311
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1693982  0.3192828
sample estimates:
       cor 
0.07973106

Warning message in cor.test.default(x = c(0.019332886629221, 0.205308806060111, :
"Cannot compute exact p-value with ties"

	Spearman's rank correlation rho

data:  both$pst and both$fst
S = 42830, p-value = 0.8787
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
0.01946097

3 methylated sites per gene¶

head ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop_3loci.tab
wc -l ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop_3loci.tab

"Quant_Varia"	"contig_gene"	"start_gene_2kb"	"end_gene_2kb"	"Pst_Values"
"Contig0_10497_95068"	"Contig0"	"10497"	"95068"	0.019332886629221
"Contig100188_1_4123"	"Contig100188"	"1"	"4123"	0.734962005060689
"Contig100396_1_4908"	"Contig100396"	"1"	"4908"	0.185205868587485
"Contig100743_7752_12849"	"Contig100743"	"7752"	"12849"	0.513177756897547
"Contig100994_1_8602"	"Contig100994"	"1"	"8602"	0.701143568206082
"Contig10117_48_10916"	"Contig10117"	"48"	"10916"	0.0512787574778887
"Contig1013_14071_19514"	"Contig1013"	"14071"	"19514"	0.0172219569940594
"Contig1013_16204_21663"	"Contig1013"	"16204"	"21663"	0.0395933279259626
"Contig101333_3371_8675"	"Contig101333"	"3371"	"8675"	0.712270890827424
2178 ../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop_3loci.tab

# read in Pst data
pst <- read.csv("../../paper-oly-mbdbs-gen/analyses/DMGs/Pst_gene_2kbslop_3loci.tab", sep="\t",header=T,stringsAsFactors = F)
colnames(pst) <- c("id","contig","start","end","pst")
head(pst)

wc -l SFS/HCSS_sfsm70_PerGeneFst.csv

1387 SFS/HCSS_sfsm70_PerGeneFst.csv

# read in Fst data
fst <- read.csv("SFS/HCSS_sfsm70_PerGeneFst.csv",stringsAsFactors = F)
head(fst)

length(intersect(pst$id,fst$id))

2178 genes with Pst values, 1387 genes with Fst values (including those with Fst=0), only 96 overlapping genes¶

both = merge(pst,fst, by ="id")
both <- both[,c("id","pst","fst01")]
head(both)

#library(MASS)
#dens <- kde2d(Dgeo,DgenN, n=300)
#myPal <- colorRampPalette(c("white","blue","gold", "orange", "red"))
plot(both$fst, both$pst, pch=20,cex=1,xlab = "Fst", ylab = "Pst")
title("Pst vs Fst, 2kbslop_3loci, 96 genes")
summary(lm(both$pst~both$fst),)
R2 = round(summary(lm(both$pst~both$fst))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(both$pst~both$fst))
text(0.4,0.8,label=paste("R^2=",R2))
text(0.4,0.75,label="r= 0.076")

Call:
lm(formula = both$pst ~ both$fst)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.27744 -0.17026 -0.05201  0.11454  0.60272 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.24024    0.02442   9.838 4.05e-16 ***
both$fst     0.14612    0.19709   0.741     0.46    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.205 on 94 degrees of freedom
Multiple R-squared:  0.005813,	Adjusted R-squared:  -0.004763 
F-statistic: 0.5496 on 1 and 94 DF,  p-value: 0.4603

cor.test( ~ both$pst + both$fst,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ both$pst + both$fst, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  both$pst and both$fst
t = 0.74136, df = 94, p-value = 0.4603
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1261713  0.2725629
sample estimates:
       cor 
0.07624322

Warning message in cor.test.default(x = c(0.019332886629221, 0.205308806060111, :
"Cannot compute exact p-value with ties"

	Spearman's rank correlation rho

data:  both$pst and both$fst
S = 154628, p-value = 0.6371
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.04875152

Pst vs Fst for random sites in genome¶

1000 bp bins¶

sfsSuf = "SFS/HCSS_sfsm70"

module load gcc/6.2.0
module load xz/5.2.2 
module load bzip2/1.0.6
module load bedtools/2.29.0

bedtools makewindows -h

Tool: bedtools makewindows
Version: v2.29.0
Summary: Makes adjacent or sliding windows across a genome or BED file.

Usage: bedtools makewindows [OPTIONS] [-g <genome> OR -b <bed>]
 [ -w <window_size> OR -n <number of windows> ]

Input Options: 
	-g <genome>
		Genome file size (see notes below).
		Windows will be created for each chromosome in the file.

	-b <bed>
		BED file (with chrom,start,end fields).
		Windows will be created for each interval in the file.

Windows Output Options: 
	-w <window_size>
		Divide each input interval (either a chromosome or a BED interval)
		to fixed-sized windows (i.e. same number of nucleotide in each window).
		Can be combined with -s <step_size>

	-s <step_size>
		Step size: i.e., how many base pairs to step before
		creating a new window. Used to create "sliding" windows.
		- Defaults to window size (non-sliding windows).

	-n <number_of_windows>
		Divide each input interval (either a chromosome or a BED interval)
		to fixed number of windows (i.e. same number of windows, with
		varying window sizes).

	-reverse
		 Reverse numbering of windows in the output, i.e. report 
		 windows in decreasing order

ID Naming Options: 
	-i src|winnum|srcwinnum
		The default output is 3 columns: chrom, start, end .
		With this option, a name column will be added.
		 "-i src" - use the source interval's name.
		 "-i winnum" - use the window number as the ID (e.g. 1,2,3,4...).
		 "-i srcwinnum" - use the source interval's name with the window number.
		See below for usage examples.

Notes: 
	(1) The genome file should tab delimited and structured as follows:
	 <chromName><TAB><chromSize>

	For example, Human (hg19):
	chr1	249250621
	chr2	243199373
	...
	chr18_gl000207_random	4262

Tips: 
	One can use the UCSC Genome Browser's MySQL database to extract
	chromosome sizes. For example, H. sapiens:

	mysql --user=genome --host=genome-mysql.cse.ucsc.edu -A -e \
	"select chrom, size from hg19.chromInfo" > hg19.genome

Examples: 
 # Divide the human genome into windows of 1MB:
 $ bedtools makewindows -g hg19.txt -w 1000000
 chr1 0 1000000
 chr1 1000000 2000000
 chr1 2000000 3000000
 chr1 3000000 4000000
 chr1 4000000 5000000
 ...

 # Divide the human genome into sliding (=overlapping) windows of 1MB, with 500KB overlap:
 $ bedtools makewindows -g hg19.txt -w 1000000 -s 500000
 chr1 0 1000000
 chr1 500000 1500000
 chr1 1000000 2000000
 chr1 1500000 2500000
 chr1 2000000 3000000
 ...

 # Divide each chromosome in human genome to 1000 windows of equal size:
 $ bedtools makewindows -g hg19.txt -n 1000
 chr1 0 249251
 chr1 249251 498502
 chr1 498502 747753
 chr1 747753 997004
 chr1 997004 1246255
 ...

 # Divide each interval in the given BED file into 10 equal-sized windows:
 $ cat input.bed
 chr5 60000 70000
 chr5 73000 90000
 chr5 100000 101000
 $ bedtools makewindows -b input.bed -n 10
 chr5 60000 61000
 chr5 61000 62000
 chr5 62000 63000
 chr5 63000 64000
 chr5 64000 65000
 ...

 # Add a name column, based on the window number: 
 $ cat input.bed
 chr5  60000  70000 AAA
 chr5  73000  90000 BBB
 chr5 100000 101000 CCC
 $ bedtools makewindows -b input.bed -n 3 -i winnum
 chr5        60000   63334   1
 chr5        63334   66668   2
 chr5        66668   70000   3
 chr5        73000   78667   1
 chr5        78667   84334   2
 chr5        84334   90000   3
 chr5        100000  100334  1
 chr5        100334  100668  2
 chr5        100668  101000  3
 ...

 # Reverse window numbers: 
 $ cat input.bed
 chr5  60000  70000 AAA
 chr5  73000  90000 BBB
 chr5 100000 101000 CCC
 $ bedtools makewindows -b input.bed -n 3 -i winnum -reverse
 chr5        60000   63334   3
 chr5        63334   66668   2
 chr5        66668   70000   1
 chr5        73000   78667   3
 chr5        78667   84334   2
 chr5        84334   90000   1
 chr5        100000  100334  3
 chr5        100334  100668  2
 chr5        100668  101000  1
 ...

 # Add a name column, based on the source ID + window number: 
 $ cat input.bed
 chr5  60000  70000 AAA
 chr5  73000  90000 BBB
 chr5 100000 101000 CCC
 $ bedtools makewindows -b input.bed -n 3 -i srcwinnum
 chr5        60000   63334   AAA_1
 chr5        63334   66668   AAA_2
 chr5        66668   70000   AAA_3
 chr5        73000   78667   BBB_1
 chr5        78667   84334   BBB_2
 chr5        84334   90000   BBB_3
 chr5        100000  100334  CCC_1
 chr5        100334  100668  CCC_2
 chr5        100668  101000  CCC_3
 ...

# use previously created genome fai file as the bedfile input
awk 'FS=OFS="\t"{print $1, 0, $2}' ../../Olurida_v081.fa.fai \
| bedtools makewindows -b - -w 1000 > Olurida_v081_1kbIntervals.bed

head Olurida_v081_1kbIntervals.bed

Contig0	0	1000
Contig0	1000	2000
Contig0	2000	3000
Contig0	3000	4000
Contig0	4000	5000
Contig0	5000	6000
Contig0	6000	7000
Contig0	7000	8000
Contig0	8000	9000
Contig0	9000	10000

# reading regions
regions=read.table("Olurida_v081_1kbIntervals.bed")
names(regions)=c("contig","start","end")

# read in fst data
fst01=read.table("SFS/HCSS_sfsm70_fst2pop.fst")
names(fst01)=c("contig","pos","a","b")

fst01$contig=as.character(fst01$contig)

# removing zero-only (invariant) bases
fst01[,3:4]=round(fst01[,3:4],3)
ch01=apply(fst01[,3:4],1,sum)
chh=(ch01>0)
table(chh)

fst01=fst01[chh,]
head(fst01)

chh
 FALSE   TRUE 
363405   5882

fst

# computing weighted Fst per region
i=1;gfst01=c();ns01=0
pb=txtProgressBar(0,nrow(regions))
for (i in 1:nrow(regions)) {
	setTxtProgressBar(pb,i)
	sub=subset(fst01,contig==regions$contig[i] & pos>=regions$start[i] & pos<=regions$end[i])
	if (is.null(sub[1,1]) | sum(sub$b)==0) { 
		gfst01=append(gfst01,NA)
	} else {
		gfst01=append(gfst01,sum(sub$a)/sum(sub$b))
		ns01=ns01+nrow(sub)
	}
}

================================================================================

# density plots of per-region Fst
plot(density(na.omit(gfst01)))

%expand
#adding results to regions table, saving
regions$fst01=gfst01
regionsNA <- regions[!is.na(regions$fst01),]
head(regionsNA)
write.csv(regionsNA,file="{sfsSuf}_Per1kbFst.csv",quote=F,row.names=F)
#save bed file of intervals only with fst data

nrow(regionsNA)

%expand
filtbed <- regionsNA[,c("contig","start","end")]
write.table(filtbed, "{sfsSuf}_1kb.bed",quote=F,row.names=F,col.names = F)

%expand
head {sfsSuf}_1kb.bed

Contig0 74000 75000
Contig0 109000 110000
Contig1 40000 41000
Contig1 42000 43000
Contig2 10000 11000
Contig2 136000 137000
Contig3 14000 15000
Contig4 51000 52000
Contig8 75000 76000
Contig9 17000 18000

Compare with overlappin Pst bins¶

%expand
regionsNA <- read.csv("{sfsSuf}_Per1kbFst.csv")
id <- paste(regionsNA$contig,regionsNA$start,regionsNA$end, sep = "_")
regionsNA <- cbind(id,regionsNA)
head(regionsNA)

# read in Pst data
pst1kb <- read.csv("../../paper-oly-mbdbs-gen/analyses/methylation-filtered/Pst_bins_1kb.tab", sep="\t",header=T,stringsAsFactors = F)
colnames(pst1kb) <- c("id","contig","start","end","pst","pst.lowCI","pst.highCI")
head(pst1kb)
nrow(pst1kb)

71 overlapping bins¶

both = merge(pst1kb,regionsNA, by ="id")
#both <- both[,c("id","pst","fst01")]
head(both)

#library(MASS)
#dens <- kde2d(Dgeo,DgenN, n=300)
#myPal <- colorRampPalette(c("white","blue","gold", "orange", "red"))
plot(both$fst01, both$pst, pch=20,cex=1,xlab = "Fst", ylab = "Pst")
title("Pst vs Fst, 1kb bins, 71 bins")
summary(lm(both$pst~both$fst01),)
R2 = round(summary(lm(both$pst~both$fst01))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(both$pst~both$fst01))
text(0.4,0.8,label=paste("R^2=",R2))
text(0.4,0.75,label="r= -0.035")

Call:
lm(formula = both$pst ~ both$fst01)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.28231 -0.19942 -0.02921  0.14795  0.52908 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.28394    0.03259   8.712 9.98e-13 ***
both$fst01  -0.10975    0.37560  -0.292    0.771    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2266 on 69 degrees of freedom
Multiple R-squared:  0.001236,	Adjusted R-squared:  -0.01324 
F-statistic: 0.08537 on 1 and 69 DF,  p-value: 0.771

cor.test( ~ both$pst + both$fst01,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ both$pst + both$fst01, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  both$pst and both$fst01
t = -0.29219, df = 69, p-value = 0.771
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2662733  0.1997889
sample estimates:
        cor 
-0.03515334

Warning message in cor.test.default(x = c(0.251312849756899, 0.343114484406881, :
"Cannot compute exact p-value with ties"

	Spearman's rank correlation rho

data:  both$pst and both$fst01
S = 58217, p-value = 0.8435
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
0.02385219

Get gene annotations for these overlaps¶

module load gcc/6.2.0
module load bedtools/2.29.0

chr <- as.character(both$contig.x)
start <- both$startc +2000
end <- both$end.x-2000
fst <- paste(both$fst01, both$pst, sep="_")
bed <- cbind(chr,start,end,fst)

write.table(bed,"Results/fstpst_2kbslop.tab",row.names = F, col.names = F,quote = F, sep = "\t")

intersectBed \
  -wb \
  -a "../../paper-oly-mbdbs-gen/genome-features/Olurida_v081-20190709.gene.gff" \
  -b "Results/fstpst_2kbslop.tab" \
  > "Results/fstpst_2kbslop.genes"

both[which(both$fst01 >0.4),]

Low Pst, high Fst

grep "Contig20413" Results/fstpst_2kbslop.genes

Contig20413	maker	gene	486	15467	.	-	.	ID=OLUR_00010706;Name=OLUR_00010706;Alias=maker-Contig20413-snap-gene-0.3;Note=Similar to sll1388: Universal stress protein Sll1388 (Synechocystis sp. (strain PCC 6803 / Kazusa) OX%3D1111708);Dbxref=CDD:cd00293,Coils:Coil,Gene3D:G3DSA:3.40.50.620,InterPro:IPR006015,InterPro:IPR006016,InterPro:IPR014729,PRINTS:PR01438,Pfam:PF00582,SUPERFAMILY:SSF52402;	Contig20413	485	15467	0.472081218274112_0.0207245979022085

grep "Contig50657" Results/fstpst_2kbslop.genes

Contig50657	maker	gene	113	9990	.	+	.	ID=OLUR_00017738;Name=OLUR_00017738;Alias=maker-Contig50657-snap-gene-0.3;Note=Similar to KDM3B: Lysine-specific demethylase 3B (Homo sapiens OX%3D9606);Dbxref=Gene3D:G3DSA:2.60.120.650,InterPro:IPR003347,MobiDBLite:mobidb-lite,Pfam:PF02373,ProSiteProfiles:PS51184,SMART:SM00558,SUPERFAMILY:SSF51197;	Contig50657	112	9990	0.486821705426357_0.0339309902311326

wc -l SFS/HCSS_sfsm70_slidingwindow

2773 SFS/HCSS_sfsm70_slidingwindow

10,000 bp bins¶

# use previously created genome fai file as the bedfile input
awk 'FS=OFS="\t"{print $1, 0, $2}' ../../Olurida_v081.fa.fai \
| bedtools makewindows -b - -w 10000 > Olurida_v081_10kbIntervals.bed

head Olurida_v081_10kbIntervals.bed

Contig0	0	10000
Contig0	10000	20000
Contig0	20000	30000
Contig0	30000	40000
Contig0	40000	50000
Contig0	50000	60000
Contig0	60000	70000
Contig0	70000	80000
Contig0	80000	90000
Contig0	90000	100000

# reading regions
regions=read.table("Olurida_v081_10kbIntervals.bed")
names(regions)=c("contig","start","end")

# read in fst data
fst01=read.table("SFS/HCSS_sfsm70_fst2pop.fst")
names(fst01)=c("contig","pos","a","b")

fst01$contig=as.character(fst01$contig)

# removing zero-only (invariant) bases
fst01[,3:4]=round(fst01[,3:4],3)
ch01=apply(fst01[,3:4],1,sum)
chh=(ch01>0)
table(chh)

fst01=fst01[chh,]
head(fst01)

chh
 FALSE   TRUE 
363405   5882

# computing weighted Fst per region
i=1;gfst01=c();ns01=0
pb=txtProgressBar(0,nrow(regions))
for (i in 1:nrow(regions)) {
	setTxtProgressBar(pb,i)
	sub=subset(fst01,contig==regions$contig[i] & pos>=regions$start[i] & pos<=regions$end[i])
	if (is.null(sub[1,1]) | sum(sub$b)==0) { 
		gfst01=append(gfst01,NA)
	} else {
		gfst01=append(gfst01,sum(sub$a)/sum(sub$b))
		ns01=ns01+nrow(sub)
	}
}

================================================================================

# density plots of per-region Fst
plot(density(na.omit(gfst01)))

%expand
#adding results to regions table, saving
regions$fst01=gfst01
regionsNA <- regions[!is.na(regions$fst01),]
head(regionsNA)
write.csv(regionsNA,file="{sfsSuf}_Per10kbFst.csv",quote=F,row.names=F)
#save bed file of intervals only with fst data

nrow(regionsNA)

%expand
filtbed <- regionsNA[,c("contig","start","end")]
write.table(filtbed, "{sfsSuf}_10kb.bed",quote=F,row.names=F,col.names = F)

Compare with overlappin Pst bins¶

%expand
regionsNA <- read.csv("{sfsSuf}_Per10kbFst.csv")
id <- paste(regionsNA$contig,regionsNA$start,regionsNA$end, sep = "_")
regionsNA <- cbind(id,regionsNA)
head(regionsNA)

# read in Pst data
pst10kb <- read.csv("../../paper-oly-mbdbs-gen/analyses/methylation-filtered/Pst_bins_10kb.tab", sep="\t",header=T,stringsAsFactors = F)
colnames(pst10kb) <- c("id","contig","start","end","pst","pst.lowCI","pst.highCI")
head(pst10kb)
nrow(pst10kb)

271 overlapping bins¶

both = merge(pst10kb,regionsNA, by ="id")
#both <- both[,c("id","pst","fst01")]
head(both)

#library(MASS)
#dens <- kde2d(Dgeo,DgenN, n=300)
#myPal <- colorRampPalette(c("white","blue","gold", "orange", "red"))
plot(both$fst01, both$pst, pch=20,cex=1,xlab = "Fst", ylab = "Pst")
title("Pst vs Fst, 10kb bins, 271 bins")
summary(lm(both$pst~both$fst01),)
R2 = round(summary(lm(both$pst~both$fst01))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(both$pst~both$fst01))
text(0.4,0.8,label=paste("R^2=",R2))
text(0.4,0.75,label="r= -0.004")

Call:
lm(formula = both$pst ~ both$fst01)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.27271 -0.19651 -0.02384  0.14066  0.72989 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.272758   0.016211  16.826   <2e-16 ***
both$fst01  -0.009332   0.143242  -0.065    0.948    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.22 on 269 degrees of freedom
Multiple R-squared:  1.578e-05,	Adjusted R-squared:  -0.003702 
F-statistic: 0.004245 on 1 and 269 DF,  p-value: 0.9481

cor.test( ~ both$pst + both$fst01,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ both$pst + both$fst01, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  both$pst and both$fst01
t = -0.065151, df = 269, p-value = 0.9481
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1230691  0.1152373
sample estimates:
         cor 
-0.003972307

Warning message in cor.test.default(x = c(0.613630567871362, 0.251312849756899, :
"Cannot compute exact p-value with ties"

	Spearman's rank correlation rho

data:  both$pst and both$fst01
S = 3376696, p-value = 0.7682
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
-0.0179847

Correlating PC scores for genetic and epigenetic¶

All methylation data¶

# Read in PC scores from epigenetics
ep <- read.table("../../paper-oly-mbdbs-gen/analyses/methylation-filtered/PC-scores-filtered-methylation.tab",
                 header=T, sep="\t",row.names = "sample")

Add sample names to PC score matrix

key = read.csv("../../paper-oly-mbdbs-gen/data/sample-key.csv",colClasses = c("character","character"))

samples = rownames(ep)
mapdf <- data.frame(old=key$X...MBD.FILENAME,new=key$SAMPLE)
rownames(ep) <- mapdf$new[match(samples,mapdf$old)]

ep

%expand
gen <- read.table("Results/{SUFFIX}pca_covMat.tsv",)
mbdorder = c("hc1_2","hc1_4","hc2_15","hc2_17","hc3_1","hc3_10","hc3_11","hc3_5","hc3_7",
        "ss2_14","ss2_18","ss2_9","ss3_14","ss3_15","ss3_16","ss3_20","ss3_3","ss5_18")
rownames(gen) <- mbdorder
gen
#make sure ep and gen in same order
ep <- ep[mbdorder,]

plot(ep$PC2,gen$PC1, pch=20,cex=1,xlab = "PC2 methylation", ylab = "PC1 SNPs")
title("Filtered methylated sites (PC2) vs filtered SNPs (PC1)")
summary(lm(gen$PC1~ep$PC2),)
R2 = round(summary(lm(gen$PC1~ep$PC2))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(gen$PC1~ep$PC2))
text(30,0.3,label=paste("R^2=",R2))
text(ep$PC2,gen$PC1, labels=rownames(ep), cex=0.9, font=2)

Call:
lm(formula = gen$PC1 ~ ep$PC2)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.212094 -0.088553 -0.005015  0.076451  0.264273 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.018408   0.032040   0.575    0.574    
ep$PC2      -0.011094   0.001805  -6.147  1.4e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1359 on 16 degrees of freedom
Multiple R-squared:  0.7025,	Adjusted R-squared:  0.6839 
F-statistic: 37.79 on 1 and 16 DF,  p-value: 1.404e-05

cor.test( ~ ep$PC2 + gen$PC1,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ ep$PC2 + gen$PC1, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  ep$PC2 and gen$PC1
t = -6.1473, df = 16, p-value = 1.404e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9379921 -0.6100194
sample estimates:
       cor 
-0.8381768

	Spearman's rank correlation rho

data:  ep$PC2 and gen$PC1
S = 1802, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
-0.8596491

DMLs¶

# Read in PC scores from epigenetics
ep <- read.table("../../paper-oly-mbdbs-gen/analyses/DMLs/PC-scores-DMLs.tab",
                 header=T, sep="\t",row.names = "sample")

Add sample names to PC score matrix

key = read.csv("../../paper-oly-mbdbs-gen/data/sample-key.csv",colClasses = c("character","character"))

samples = rownames(ep)
mapdf <- data.frame(old=key$X...MBD.FILENAME,new=key$SAMPLE)
rownames(ep) <- mapdf$new[match(samples,mapdf$old)]

ep

%expand
#make sure ep and gen in same order
ep <- ep[mbdorder,]

plot(ep$PC1,gen$PC1, pch=20,cex=1,xlab = "PC1 methylation", ylab = "PC1 SNPs")
title("DMLs (PC1) vs filtered SNPs (PC1)")
summary(lm(gen$PC1~ep$PC1),)
R2 = round(summary(lm(gen$PC1~ep$PC1))$r.squared, 4)
#image(dens, col=transp(myPal(300),.7), add=TRUE)
abline(lm(gen$PC1~ep$PC1))
text(3,0.3,label=paste("R^2=",R2))
text(ep$PC1,gen$PC1, labels=rownames(ep), cex=0.9, font=2)

Call:
lm(formula = gen$PC1 ~ ep$PC1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.148275 -0.052397  0.007205  0.045458  0.180223 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.018408   0.020769   0.886    0.389    
ep$PC1      -0.069865   0.006601 -10.583 1.24e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.08812 on 16 degrees of freedom
Multiple R-squared:  0.875,	Adjusted R-squared:  0.8672 
F-statistic:   112 on 1 and 16 DF,  p-value: 1.241e-08

cor.test( ~ ep$PC1 + gen$PC1,
         method = "pearson",
         conf.level = 0.95)
cor.test( ~ ep$PC1 + gen$PC1, 
         method = "spearman",
         continuity = FALSE,
         conf.level = 0.95)

	Pearson's product-moment correlation

data:  ep$PC1 and gen$PC1
t = -10.583, df = 16, p-value = 1.241e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9760352 -0.8318183
sample estimates:
       cor 
-0.9354179

	Spearman's rank correlation rho

data:  ep$PC1 and gen$PC1
S = 1768, p-value = 2.01e-05
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
-0.8245614

	hc1_2	hc1_4	hc2_15	hc2_17	hc3_1	hc3_5	hc3_7	hc3_10	hc3_11	ss2_9	ss2_14	ss2_18	ss3_3	ss3_14	ss3_15	ss3_16	ss3_20	ss5_18
hc1_2	0.0	298303.0	290189.5	281520.7	323694.5	288274.1	298537.4	276634.8	311367.0	297793.0	280529.9	334528.1	284554.6	281305.3	273985.4	284208.5	285476.9	297048.1
hc1_4	298303.0	0.0	288401.4	272689.0	314054.6	308425.8	290930.3	292157.4	313295.9	306377.3	291403.7	334752.1	291934.5	287946.6	285750.2	292011.5	290794.1	299482.5
hc2_15	290189.5	288401.4	0.0	267996.3	312934.8	306314.1	276217.9	283512.3	309629.3	296792.4	273417.4	339043.0	280972.0	277302.7	270960.4	281246.4	283754.6	297864.8
hc2_17	281520.7	272689.0	267996.3	0.0	314787.3	299622.6	275369.7	267637.3	306306.9	287060.1	263776.1	339119.1	274036.3	268751.4	256317.1	272144.4	273361.0	290238.7
hc3_1	323694.5	314054.6	312934.8	314787.3	0.0	334641.8	299112.8	325937.0	324463.8	321179.5	320997.4	335280.4	315988.8	313767.2	321554.2	317127.8	317475.6	315139.7
hc3_5	288274.1	308425.8	306314.1	299622.6	334641.8	0.0	315610.7	288781.8	325942.9	316722.4	306762.5	346143.9	305665.1	303050.4	296746.5	308046.6	304834.4	313945.2
hc3_7	298537.4	290930.3	276217.9	275369.7	299112.8	315610.7	0.0	292111.6	315363.0	297745.1	282259.6	341069.9	284236.7	279628.7	277165.4	285466.0	287941.8	299663.9
hc3_10	276634.8	292157.4	283512.3	267637.3	325937.0	288781.8	292111.6	0.0	317230.3	297372.5	267563.6	345360.9	279921.4	274370.7	257816.0	277769.7	274299.4	294286.7
hc3_11	311367.0	313295.9	309629.3	306306.9	324463.8	325942.9	315363.0	317230.3	0.0	306749.7	303226.0	330648.3	295962.1	294657.6	302097.9	297720.5	300676.0	304098.3
ss2_9	297793.0	306377.3	296792.4	287060.1	321179.5	316722.4	297745.1	297372.5	306749.7	0.0	253556.8	313685.4	255231.0	253726.0	268911.5	256477.3	273957.8	278320.1
ss2_14	280529.9	291403.7	273417.4	263776.1	320997.4	306762.5	282259.6	267563.6	303226.0	253556.8	0.0	326677.5	241072.4	240035.5	237976.3	237509.6	258380.9	271803.4
ss2_18	334528.1	334752.1	339043.0	339119.1	335280.4	346143.9	341069.9	345360.9	330648.3	313685.4	326677.5	0.0	300672.3	306406.7	328384.0	317824.0	324077.2	300108.0
ss3_3	284554.6	291934.5	280972.0	274036.3	315988.8	305665.1	284236.7	279921.4	295962.1	255231.0	241072.4	300672.3	0.0	238988.6	251167.6	238332.2	262145.0	253721.5
ss3_14	281305.3	287946.6	277302.7	268751.4	313767.2	303050.4	279628.7	274370.7	294657.6	253726.0	240035.5	306406.7	238988.6	0.0	244038.7	235816.2	258085.7	257354.3
ss3_15	273985.4	285750.2	270960.4	256317.1	321554.2	296746.5	277165.4	257816.0	302097.9	268911.5	237976.3	328384.0	251167.6	244038.7	0.0	246521.3	253941.4	276798.6
ss3_16	284208.5	292011.5	281246.4	272144.4	317127.8	308046.6	285466.0	277769.7	297720.5	256477.3	237509.6	317824.0	238332.2	235816.2	246521.3	0.0	258804.6	267488.5
ss3_20	285476.9	290794.1	283754.6	273361.0	317475.6	304834.4	287941.8	274299.4	300676.0	273957.8	258380.9	324077.2	262145.0	258085.7	253941.4	258804.6	0.0	280486.1
ss5_18	297048.1	299482.5	297864.8	290238.7	315139.7	313945.2	299663.9	294286.7	304098.3	278320.1	271803.4	300108.0	253721.5	257354.3	276798.6	267488.5	280486.1	0.0

	hc1_2	hc1_4	hc2_15	hc2_17	hc3_1	hc3_5	hc3_7	hc3_10	hc3_11	ss2_9	ss2_14	ss2_18	ss3_3	ss3_14	ss3_15	ss3_16	ss3_20	ss5_18
hc1_2	0.0000000	0.2155436	0.2054988	0.2245761	0.2140475	0.1251035	0.2137833	0.1941731	0.2196581	0.2191748	0.2201363	0.2251679	0.2293540	0.2303018	0.2273960	0.2267536	0.2241723	0.2393679
hc1_4	0.2155436	0.0000000	0.1844240	0.1693271	0.1811331	0.2071129	0.1608409	0.2090009	0.2157563	0.2186305	0.2188330	0.2146309	0.2291905	0.2247412	0.2257357	0.2190871	0.2157204	0.2274161
hc2_15	0.2054988	0.1844240	0.0000000	0.2041950	0.1854690	0.2021604	0.1823913	0.1592689	0.2168135	0.2041132	0.2085476	0.1910094	0.2042863	0.2045106	0.2061360	0.2040459	0.2013346	0.2098602
hc2_17	0.2245761	0.1693271	0.2041950	0.0000000	0.1884048	0.2163634	0.1724402	0.2179260	0.2265039	0.2268210	0.2268209	0.2283067	0.2379735	0.2322090	0.2385235	0.2269364	0.2294361	0.2372695
hc3_1	0.2140475	0.1811331	0.1854690	0.1884048	0.0000000	0.2084380	0.1141350	0.2070880	0.2092909	0.2195993	0.2160866	0.2155464	0.2254828	0.2196778	0.2258590	0.2205749	0.2200920	0.2156870
hc3_5	0.1251035	0.2071129	0.2021604	0.2163634	0.2084380	0.0000000	0.2104185	0.1869989	0.2143060	0.2202222	0.2148287	0.2214425	0.2216537	0.2194587	0.2223524	0.2192765	0.2221298	0.2263948
hc3_7	0.2137833	0.1608409	0.1823913	0.1724402	0.1141350	0.2104185	0.0000000	0.2098760	0.2154145	0.2211945	0.2233552	0.2236631	0.2265407	0.2248371	0.2269251	0.2224353	0.2260581	0.2286600
hc3_10	0.1941731	0.2090009	0.1592689	0.2179260	0.2070880	0.1869989	0.2098760	0.0000000	0.2158190	0.2072541	0.2123815	0.2048036	0.2057382	0.2078799	0.1916720	0.2139469	0.2081097	0.2118349
hc3_11	0.2196581	0.2157563	0.2168135	0.2265039	0.2092909	0.2143060	0.2154145	0.2158190	0.0000000	0.2204729	0.2206785	0.2201360	0.2362094	0.2252514	0.2307265	0.2282604	0.2200907	0.2270442
ss2_9	0.2191748	0.2186305	0.2041132	0.2268210	0.2195993	0.2202222	0.2211945	0.2072541	0.2204729	0.0000000	0.1482022	0.1643140	0.1565610	0.1642693	0.2091080	0.1664055	0.2066793	0.1738464
ss2_14	0.2201363	0.2188330	0.2085476	0.2268209	0.2160866	0.2148287	0.2233552	0.2123815	0.2206785	0.1482022	0.0000000	0.1741098	0.1484094	0.1731242	0.2053117	0.1555210	0.2028654	0.1736163
ss2_18	0.2251679	0.2146309	0.1910094	0.2283067	0.2155464	0.2214425	0.2236631	0.2048036	0.2201360	0.1643140	0.1741098	0.0000000	0.1311476	0.1613894	0.2121693	0.1856383	0.1988239	0.1271048
ss3_3	0.2293540	0.2291905	0.2042863	0.2379735	0.2254828	0.2216537	0.2265407	0.2057382	0.2362094	0.1565610	0.1484094	0.1311476	0.0000000	0.1692117	0.2093890	0.1462554	0.2056096	0.1244475
ss3_14	0.2303018	0.2247412	0.2045106	0.2322090	0.2196778	0.2194587	0.2248371	0.2078799	0.2252514	0.1642693	0.1731242	0.1613894	0.1692117	0.0000000	0.2101518	0.1645175	0.2069574	0.1486115
ss3_15	0.2273960	0.2257357	0.2061360	0.2385235	0.2258590	0.2223524	0.2269251	0.1916720	0.2307265	0.2091080	0.2053117	0.2121693	0.2093890	0.2101518	0.0000000	0.2127640	0.2091865	0.2141706
ss3_16	0.2267536	0.2190871	0.2040459	0.2269364	0.2205749	0.2192765	0.2224353	0.2139469	0.2282604	0.1664055	0.1555210	0.1856383	0.1462554	0.1645175	0.2127640	0.0000000	0.2047189	0.1671745
ss3_20	0.2241723	0.2157204	0.2013346	0.2294361	0.2200920	0.2221298	0.2260581	0.2081097	0.2200907	0.2066793	0.2028654	0.1988239	0.2056096	0.2069574	0.2091865	0.2047189	0.0000000	0.2039022
ss5_18	0.2393679	0.2274161	0.2098602	0.2372695	0.2156870	0.2263948	0.2286600	0.2118349	0.2270442	0.1738464	0.1736163	0.1271048	0.1244475	0.1486115	0.2141706	0.1671745	0.2039022	0.0000000

	hc1_2	hc1_4	hc2_15	hc2_17	hc3_1	hc3_5	hc3_7	hc3_10	hc3_11	ss2_9	ss2_14	ss2_18	ss3_3	ss3_14	ss3_15	ss3_16	ss3_20	ss5_18
hc1_2	0.000	10545.532	10351.204	9385.573	10610.214	6363.300	10611.443	8551.481	9068.337	14788.300	14887.002	13635.256	14606.952	14484.494	11693.997	14072.044	11702.18	13190.286
hc1_4	10545.532	0.000	9064.440	7984.184	8727.395	8905.577	8699.207	10736.499	10026.701	17346.810	16626.609	15169.867	16480.535	16347.990	14253.680	15345.568	13291.64	15195.551
hc2_15	10351.204	9064.440	0.000	8032.958	9008.169	9760.334	7750.867	10319.292	11194.320	16995.519	16120.010	15450.882	16418.206	16417.512	13740.327	15822.143	13357.00	15649.929
hc2_17	9385.573	7984.184	8032.958	0.000	9827.633	8017.600	8785.571	9408.603	10454.902	16400.962	16108.636	15381.091	15743.180	15586.173	13138.397	14668.390	12158.79	14422.239
hc3_1	10610.214	8727.395	9008.169	9827.633	0.000	8808.350	6567.108	10436.642	10554.475	16222.809	16014.164	13934.951	15248.147	14915.306	12793.947	14521.674	12503.21	13721.460
hc3_5	6363.300	8905.577	9760.334	8017.600	8808.350	0.000	9189.478	7834.229	9119.644	15202.564	15975.183	13292.572	15959.683	15121.119	11756.756	15334.174	12440.56	14534.806
hc3_7	10611.443	8699.207	7750.867	8785.571	6567.108	9189.478	0.000	10443.443	10804.160	17348.604	16705.442	14868.123	16044.562	16375.903	13350.965	16117.654	13837.97	15293.315
hc3_10	8551.481	10736.499	10319.292	9408.603	10436.642	7834.229	10443.443	0.000	10148.679	15486.501	14988.730	13497.210	15209.257	14489.565	12354.985	14441.498	11705.99	14281.504
hc3_11	9068.337	10026.701	11194.320	10454.902	10554.475	9119.644	10804.160	10148.679	0.000	15102.614	14709.279	13012.517	14188.195	14113.226	11779.373	13574.423	12187.15	13623.710
ss2_9	14788.300	17346.810	16995.519	16400.962	16222.809	15202.564	17348.604	15486.501	15102.614	0.000	9317.558	8747.628	8413.878	9119.600	10149.293	8682.160	11387.52	8899.327
ss2_14	14887.002	16626.609	16120.010	16108.636	16014.164	15975.183	16705.442	14988.730	14709.279	9317.558	0.000	10653.366	9278.006	9505.802	10936.765	8530.524	11322.26	10281.724
ss2_18	13635.256	15169.867	15450.882	15381.091	13934.951	13292.572	14868.123	13497.210	13012.517	8747.628	10653.366	0.000	7351.714	8692.706	10026.944	10471.814	11078.75	7483.073
ss3_3	14606.952	16480.535	16418.206	15743.180	15248.147	15959.683	16044.562	15209.257	14188.195	8413.878	9278.006	7351.714	0.000	8503.102	9899.829	8283.191	11215.38	6997.712
ss3_14	14484.494	16347.990	16417.512	15586.173	14915.306	15121.119	16375.903	14489.565	14113.226	9119.600	9505.802	8692.706	8503.102	0.000	10568.719	9230.876	11030.21	8055.097
ss3_15	11693.997	14253.680	13740.327	13138.397	12793.947	11756.756	13350.965	12354.985	11779.373	10149.293	10936.765	10026.944	9899.829	10568.719	0.000	10193.024	10433.14	9796.022
ss3_16	14072.044	15345.568	15822.143	14668.390	14521.674	15334.174	16117.654	14441.498	13574.423	8682.160	8530.524	10471.814	8283.191	9230.876	10193.024	0.000	10436.72	8725.894
ss3_20	11702.183	13291.645	13357.002	12158.790	12503.211	12440.562	13837.969	11705.987	12187.148	11387.520	11322.262	11078.749	11215.385	11030.208	10433.139	10436.720	0.00	10959.480
ss5_18	13190.286	15195.551	15649.929	14422.239	13721.460	14534.806	15293.315	14281.504	13623.710	8899.327	10281.724	7483.073	6997.712	8055.097	9796.022	8725.894	10959.48	0.000

id	contig	start	end	notes_gene	fst01
Contig103346_4131_18776	Contig103346	4131	18776	ID=OLUR_00011851;Name=OLUR_00011851;Alias=maker-Contig103346-snap-gene-0.4;Note=Similar to HSPA12B: Heat shock 70 kDa protein 12B (Homo sapiens OX%3D9606);Dbxref=CDD:cd10229,Gene3D:G3DSA:3.30.420.40,Gene3D:G3DSA:3.90.640.10,InterPro:IPR013126,PRINTS:PR00301,Pfam:PF00012,SUPERFAMILY:SSF53067;	0.069637883
Contig60108_1_13731	Contig60108	1	13731	ID=OLUR_00014779;Name=OLUR_00014779;Alias=maker-Contig60108-snap-gene-0.2;Note=Similar to MLH3: DNA mismatch repair protein Mlh3 (Homo sapiens OX%3D9606);Dbxref=Gene3D:G3DSA:3.30.230.10,Gene3D:G3DSA:3.30.565.10,InterPro:IPR013507,InterPro:IPR014721,InterPro:IPR020568,InterPro:IPR036890,MobiDBLite:mobidb-lite,Pfam:PF01119,SMART:SM01340,SUPERFAMILY:SSF54211,SUPERFAMILY:SSF55874;Ontology_term=GO:0005524,GO:0006298,GO:0030983;	0.121951220
Contig27784_1_12761	Contig27784	1	12761	ID=OLUR_00013246;Name=OLUR_00013246;Alias=maker-Contig27784-snap-gene-0.2;Note=Similar to Fbxl6: F-box/LRR-repeat protein 6 (Mus musculus OX%3D10090);Dbxref=Gene3D:G3DSA:3.80.10.10,InterPro:IPR032675,SUPERFAMILY:SSF52047;	0.041095890
Contig38648_3515_20578	Contig38648	3515	20578	ID=OLUR_00010999;Name=OLUR_00010999;Alias=maker-Contig38648-snap-gene-0.2;Note=Similar to Rnf8: E3 ubiquitin-protein ligase RNF8 (Mus musculus OX%3D10090);Dbxref=CDD:cd16535,Coils:Coil,Gene3D:G3DSA:3.30.40.10,InterPro:IPR001841,InterPro:IPR013083,InterPro:IPR017907,MobiDBLite:mobidb-lite,Pfam:PF13923,ProSitePatterns:PS00518,ProSiteProfiles:PS50089,SMART:SM00184,SUPERFAMILY:SSF57850;	0.047858942
Contig282_38634_44741	Contig282	38634	44741	ID=OLUR_00002451;Name=OLUR_00002451;Alias=snap_masked-Contig282-processed-gene-0.4;Note=Similar to Cry2: Cryptochrome-2 (Rattus norvegicus OX%3D10116);Dbxref=Gene3D:G3DSA:3.40.50.620,InterPro:IPR006050,InterPro:IPR014729,InterPro:IPR036155,Pfam:PF00875,ProSiteProfiles:PS51645,SUPERFAMILY:SSF52425;	0.001798561
Contig282_26273_42182	Contig282	26273	42182	ID=OLUR_00002450;Name=OLUR_00002450;Alias=maker-Contig282-snap-gene-0.10;Note=Similar to API5: Apoptosis inhibitor 5 (Pongo abelii OX%3D9601);Dbxref=Coils:Coil,InterPro:IPR008383,InterPro:IPR016024,MobiDBLite:mobidb-lite,Pfam:PF05918,SUPERFAMILY:SSF48371;	0.001644737

id	contig.x	start.x	end.x	pst	contig.y	start.y	end.y	notes_gene	fst01
Contig0_10497_95068	Contig0	10497	95068	0.01933289	Contig0	10497	95068	ID=OLUR_00000039;Name=OLUR_00000039;Alias=maker-Contig0-snap-gene-0.8;Note=Similar to WDR87: WD repeat-containing protein 87 (Homo sapiens OX%3D9606);Dbxref=Coils:Coil,Gene3D:G3DSA:1.25.10.10,Gene3D:G3DSA:2.130.10.10,InterPro:IPR001680,InterPro:IPR011989,InterPro:IPR015943,InterPro:IPR016024,InterPro:IPR017986,InterPro:IPR036322,MobiDBLite:mobidb-lite,Pfam:PF00400,ProSiteProfiles:PS50082,ProSiteProfiles:PS50294,SMART:SM00320,SUPERFAMILY:SSF48371,SUPERFAMILY:SSF50978;Ontology_term=GO:0005515;	0.000000000
Contig10074_2260_6621	Contig10074	2260	6621	0.25131285	Contig10074	2260	6621	ID=OLUR_00025622;Name=OLUR_00025622;Alias=maker-Contig10074-snap-gene-0.2;Note=Similar to V-RMIL: Serine/threonine-protein kinase-transforming protein Rmil (Avian rous-associated virus type 1 OX%3D11950);Dbxref=Gene3D:G3DSA:1.10.510.10;	0.192019950
Contig107541_1926_7324	Contig107541	1926	7324	0.66627497	Contig107541	1926	7324	ID=OLUR_00024691;Name=OLUR_00024691;Alias=maker-Contig107541-snap-gene-0.2;Note=Protein of unknown function;Dbxref=Gene3D:G3DSA:3.30.710.10,InterPro:IPR000210,InterPro:IPR011333,MobiDBLite:mobidb-lite,Pfam:PF00651,ProSiteProfiles:PS50097,SUPERFAMILY:SSF54695;Ontology_term=GO:0005515;	0.045112782
Contig109827_1765_6591	Contig109827	1765	6591	0.66755413	Contig109827	1765	6591	ID=OLUR_00025663;Name=OLUR_00025663;Alias=maker-Contig109827-snap-gene-0.2;Note=Similar to Unc80: Protein unc-80 homolog (Mus musculus OX%3D10090);	0.150627615
Contig112833_1_6669	Contig112833	1	6669	0.68067953	Contig112833	1	6669	ID=OLUR_00025551;Name=OLUR_00025551;Alias=maker-Contig112833-snap-gene-0.2;Note=Similar to Gls2: Glutaminase liver isoform%2C mitochondrial (Mus musculus OX%3D10090);Dbxref=CDD:cd00204,Gene3D:G3DSA:1.25.40.20,Gene3D:G3DSA:3.40.710.10,InterPro:IPR002110,InterPro:IPR012338,InterPro:IPR015868,InterPro:IPR020683,InterPro:IPR036770,Pfam:PF04960,Pfam:PF12796,ProSiteProfiles:PS50088,ProSiteProfiles:PS50297,SMART:SM00248,SUPERFAMILY:SSF48403,SUPERFAMILY:SSF56601;Ontology_term=GO:0004359,GO:0005515,GO:0006541;	0.005154639
Contig129_9110_46084	Contig129	9110	46084	0.06717008	Contig129	9110	46084	ID=OLUR_00001506;Name=OLUR_00001506;Alias=maker-Contig129-snap-gene-0.10;Note=Similar to Ints3: Integrator complex subunit 3 (Mus musculus OX%3D10090);Dbxref=InterPro:IPR019333,MobiDBLite:mobidb-lite,Pfam:PF10189;	0.100917431

	id	pst	fst01	notes_gene
5	Contig112833_1_6669	0.6806795	0.005154639	ID=OLUR_00025551;Name=OLUR_00025551;Alias=maker-Contig112833-snap-gene-0.2;Note=Similar to Gls2: Glutaminase liver isoform%2C mitochondrial (Mus musculus OX%3D10090);Dbxref=CDD:cd00204,Gene3D:G3DSA:1.25.40.20,Gene3D:G3DSA:3.40.710.10,InterPro:IPR002110,InterPro:IPR012338,InterPro:IPR015868,InterPro:IPR020683,InterPro:IPR036770,Pfam:PF04960,Pfam:PF12796,ProSiteProfiles:PS50088,ProSiteProfiles:PS50297,SMART:SM00248,SUPERFAMILY:SSF48403,SUPERFAMILY:SSF56601;Ontology_term=GO:0004359,GO:0005515,GO:0006541;
35	Contig18485_27692_52779	0.7325620	0.000000000	ID=OLUR_00001082;Name=OLUR_00001082;Alias=maker-Contig18485-snap-gene-0.8;Note=Similar to RREB1: Ras-responsive element-binding protein 1 (Gallus gallus OX%3D9031);Dbxref=Gene3D:G3DSA:3.30.160.60,InterPro:IPR013087,InterPro:IPR036236,MobiDBLite:mobidb-lite,Pfam:PF00096,Pfam:PF13894,Pfam:PF13909,Pfam:PF13912,ProSitePatterns:PS00028,ProSiteProfiles:PS50157,SMART:SM00355,SUPERFAMILY:SSF57667;Ontology_term=GO:0003676;
52	Contig206988_1_7220	0.5694211	0.000000000	ID=OLUR_00024466;Name=OLUR_00024466;Alias=snap_masked-Contig206988-processed-gene-0.0;Note=Similar to Wdr54: WD repeat-containing protein 54 (Mus musculus OX%3D10090);Dbxref=InterPro:IPR036322,SUPERFAMILY:SSF50978;Ontology_term=GO:0005515;
66	Contig2254_1_11569	0.5354929	-0.004008016	ID=OLUR_00019191;Name=OLUR_00019191;Alias=maker-Contig2254-snap-gene-0.1;Note=Similar to VVA0006: Riboflavin biosynthesis protein VVA0006 (Vibrio vulnificus (strain YJ016) OX%3D196600);Dbxref=CDD:cd15457,Coils:Coil,Gene3D:G3DSA:1.10.357.40,InterPro:IPR012816,InterPro:IPR037238,MobiDBLite:mobidb-lite,Pfam:PF08719,SUPERFAMILY:SSF143990,TIGRFAM:TIGR02464;
86	Contig24745_17400_35588	0.6093125	0.000000000	ID=OLUR_00002446;Name=OLUR_00002446;Alias=maker-Contig24745-snap-gene-0.7;Note=Protein of unknown function;Dbxref=Coils:Coil;
95	Contig26170_5945_21684	0.6814634	-0.004629630	ID=OLUR_00010285;Name=OLUR_00010285;Alias=maker-Contig26170-snap-gene-0.3;Note=Similar to Stk31: Serine/threonine-protein kinase 31 (Mus musculus OX%3D10090);Dbxref=CDD:cd00180,Coils:Coil,Gene3D:G3DSA:1.10.510.10,InterPro:IPR011009,MobiDBLite:mobidb-lite,SUPERFAMILY:SSF56112;
130	Contig32566_4290_23883	0.8429597	0.000000000	ID=OLUR_00002794;Name=OLUR_00002794;Alias=snap_masked-Contig32566-processed-gene-0.0;Note=Protein of unknown function;Dbxref=MobiDBLite:mobidb-lite;
131	Contig32566_9055_42575	0.7889923	-0.004415011	ID=OLUR_00002795;Name=OLUR_00002795;Alias=maker-Contig32566-snap-gene-0.7;Note=Similar to HECTD1: E3 ubiquitin-protein ligase HECTD1 (Homo sapiens OX%3D9606);Dbxref=CDD:cd00204,Coils:Coil,Gene3D:G3DSA:1.25.40.20,Gene3D:G3DSA:2.30.30.920,Gene3D:G3DSA:2.60.120.260,InterPro:IPR002110,InterPro:IPR008979,InterPro:IPR010606,InterPro:IPR012919,InterPro:IPR020683,InterPro:IPR036770,InterPro:IPR037252,MobiDBLite:mobidb-lite,Pfam:PF06701,Pfam:PF07738,Pfam:PF13857,ProSiteProfiles:PS50088,ProSiteProfiles:PS50297,ProSiteProfiles:PS51416,SMART:SM00248,SUPERFAMILY:SSF159034,SUPERFAMILY:SSF48403,SUPERFAMILY:SSF49785;Ontology_term=GO:0004842,GO:0005515,GO:0016567,GO:0046872;
134	Contig32910_2664_19377	0.7010187	0.003144654	ID=OLUR_00011811;Name=OLUR_00011811;Alias=maker-Contig32910-snap-gene-0.4;Note=Similar to Spata6: Spermatogenesis-associated protein 6 (Mus musculus OX%3D10090);Dbxref=Coils:Coil,InterPro:IPR032732,Pfam:PF14909;
155	Contig37588_11392_33472	0.7551651	-0.003696858	ID=OLUR_00003309;Name=OLUR_00003309;Alias=maker-Contig37588-snap-gene-0.5;Note=Protein of unknown function;Dbxref=Coils:Coil,MobiDBLite:mobidb-lite;
160	Contig38_57109_82031	0.7901919	-0.006535948	ID=OLUR_00000267;Name=OLUR_00000267;Alias=maker-Contig38-snap-gene-0.14;Note=Similar to KIF16B: Kinesin-like protein KIF16B (Homo sapiens OX%3D9606);Dbxref=CDD:cd00060,Coils:Coil,Gene3D:G3DSA:2.60.200.20,Gene3D:G3DSA:3.30.1520.10,Gene3D:G3DSA:3.40.850.10,InterPro:IPR000253,InterPro:IPR001683,InterPro:IPR001752,InterPro:IPR008984,InterPro:IPR019821,InterPro:IPR027417,InterPro:IPR036871,InterPro:IPR036961,MobiDBLite:mobidb-lite,PRINTS:PR00380,Pfam:PF00225,Pfam:PF00498,Pfam:PF00787,ProSitePatterns:PS00411,ProSiteProfiles:PS50067,ProSiteProfiles:PS50195,SMART:SM00129,SMART:SM00312,SUPERFAMILY:SSF49879,SUPERFAMILY:SSF52540,SUPERFAMILY:SSF64268;Ontology_term=GO:0003777,GO:0005515,GO:0005524,GO:0007018,GO:0008017,GO:0035091;
169	Contig408_19615_27287	0.5061239	0.000000000	ID=OLUR_00006735;Name=OLUR_00006735;Alias=maker-Contig408-snap-gene-0.4;Note=Protein of unknown function;
175	Contig42610_7960_46080	0.5826824	0.000000000	ID=OLUR_00000735;Name=OLUR_00000735;Alias=maker-Contig42610-snap-gene-0.9;Note=Similar to Togaram1: TOG array regulator of axonemal microtubules protein 1 (Mus musculus OX%3D10090);Dbxref=Coils:Coil,Gene3D:G3DSA:1.25.10.10,InterPro:IPR011989,InterPro:IPR016024,InterPro:IPR034085,MobiDBLite:mobidb-lite,SMART:SM01349,SUPERFAMILY:SSF48371;
210	Contig58479_956_12381	0.5113152	0.000000000	ID=OLUR_00016278;Name=OLUR_00016278;Alias=maker-Contig58479-snap-gene-0.3;Note=Protein of unknown function;Dbxref=MobiDBLite:mobidb-lite;

	id	pst	fst01	notes_gene
49	Contig20413_1_17467	0.020724598	0.4720812	ID=OLUR_00010706;Name=OLUR_00010706;Alias=maker-Contig20413-snap-gene-0.3;Note=Similar to sll1388: Universal stress protein Sll1388 (Synechocystis sp. (strain PCC 6803 / Kazusa) OX%3D1111708);Dbxref=CDD:cd00293,Coils:Coil,Gene3D:G3DSA:3.40.50.620,InterPro:IPR006015,InterPro:IPR006016,InterPro:IPR014729,PRINTS:PR01438,Pfam:PF00582,SUPERFAMILY:SSF52402;
143	Contig34298_13092_51114	0.004027313	0.3714286	ID=OLUR_00001738;Name=OLUR_00001738;Alias=maker-Contig34298-snap-gene-0.4;Note=Similar to BIRC6: Baculoviral IAP repeat-containing protein 6 (Homo sapiens OX%3D9606);Dbxref=CDD:cd00022,Gene3D:G3DSA:1.10.1170.10,InterPro:IPR001370,InterPro:IPR019775,InterPro:IPR036322,MobiDBLite:mobidb-lite,Pfam:PF00653,ProSitePatterns:PS00678,ProSiteProfiles:PS50143,SMART:SM00238,SUPERFAMILY:SSF50978,SUPERFAMILY:SSF57924;Ontology_term=GO:0005515;
197	Contig50657_1_11990	0.033930990	0.4868217	ID=OLUR_00017738;Name=OLUR_00017738;Alias=maker-Contig50657-snap-gene-0.3;Note=Similar to KDM3B: Lysine-specific demethylase 3B (Homo sapiens OX%3D9606);Dbxref=Gene3D:G3DSA:2.60.120.650,InterPro:IPR003347,MobiDBLite:mobidb-lite,Pfam:PF02373,ProSiteProfiles:PS51184,SMART:SM00558,SUPERFAMILY:SSF51197;

	id	pst	fst01	notes_gene
19	Contig16916_3259_40767	0.5218304	0.3822314	ID=OLUR_00001636;Name=OLUR_00001636;Alias=maker-Contig16916-snap-gene-0.8;Note=Similar to Med13: Mediator of RNA polymerase II transcription subunit 13 (Mus musculus OX%3D10090);Dbxref=InterPro:IPR009401,InterPro:IPR021643,MobiDBLite:mobidb-lite,Pfam:PF06333,Pfam:PF11597;Ontology_term=GO:0003712,GO:0006357,GO:0016592;
85	Contig24693_2452_15381	0.3410517	0.5007496	ID=OLUR_00007682;Name=OLUR_00007682;Alias=maker-Contig24693-snap-gene-0.4;Note=Protein of unknown function;Dbxref=Gene3D:G3DSA:1.10.533.10,InterPro:IPR000488,InterPro:IPR011029,MobiDBLite:mobidb-lite,ProSiteProfiles:PS50017,SUPERFAMILY:SSF47986;Ontology_term=GO:0005515,GO:0007165;
108	Contig28902_528_13774	0.3112797	0.4283427	ID=OLUR_00000830;Name=OLUR_00000830;Alias=maker-Contig28902-snap-gene-0.12;Note=Similar to SPON1: Spondin-1 (Homo sapiens OX%3D9606);Dbxref=Gene3D:G3DSA:2.20.100.10,Gene3D:G3DSA:2.60.40.2130,InterPro:IPR000884,InterPro:IPR009465,InterPro:IPR036383,InterPro:IPR038678,MobiDBLite:mobidb-lite,Pfam:PF00090,Pfam:PF06468,ProSiteProfiles:PS50092,ProSiteProfiles:PS51020,SMART:SM00209,SUPERFAMILY:SSF82895;
227	Contig77382_1559_12918	0.4155037	0.6633523	ID=OLUR_00016056;Name=OLUR_00016056;Alias=snap_masked-Contig77382-processed-gene-0.0;Note=Similar to Socs5: Suppressor of cytokine signaling 5 (Mus musculus OX%3D10090);Dbxref=Gene3D:G3DSA:3.30.505.10,InterPro:IPR000980,InterPro:IPR036860,MobiDBLite:mobidb-lite,Pfam:PF00017,SUPERFAMILY:SSF55550;

contig	start	end	notes	pct15	start15	end15
Contig61093	7493	7946	ID=OLUR_00020575;Name=OLUR_00020575;Alias=maker-Contig61093-snap-gene-0.2;Note=Protein of unknown function;	67	7560	7879
Contig1111	24968	28696	ID=OLUR_00006628;Name=OLUR_00006628;Alias=maker-Contig1111-snap-gene-0.1;Note=Similar to Spag6: Sperm-associated antigen 6 (Mus musculus OX%3D10090);Dbxref=Gene3D:G3DSA:1.25.10.10,InterPro:IPR000225,InterPro:IPR000357,InterPro:IPR011989,InterPro:IPR016024,Pfam:PF02985,SMART:SM00185,SUPERFAMILY:SSF48371;Ontology_term=GO:0005515;	559	25527	28137
Contig214118	201	926	ID=OLUR_00032161;Name=OLUR_00032161;Alias=maker-Contig214118-snap-gene-0.0;Note=Protein of unknown function;Dbxref=Gene3D:G3DSA:3.10.450.10;	108	309	818
Contig58217	9736	11541	ID=OLUR_00019127;Name=OLUR_00019127;Alias=snap_masked-Contig58217-processed-gene-0.2;Note=Protein of unknown function;Dbxref=MobiDBLite:mobidb-lite;	270	10006	11271
Contig2046	2295	18394	ID=OLUR_00011450;Name=OLUR_00011450;Alias=maker-Contig2046-snap-gene-0.5;Note=Protein of unknown function;	2414	4709	15980
Contig9540	4303	10179	ID=OLUR_00018391;Name=OLUR_00018391;Alias=maker-Contig9540-snap-gene-0.1;Note=Protein of unknown function;Dbxref=Gene3D:G3DSA:2.170.300.10;	881	5184	9298

	contig	pos	a	b
64	Contig0	74432	0.000	0.009
82	Contig0	109103	0.033	0.457
98	Contig0	109119	0.003	0.052
102	Contig0	109123	0.002	0.067
114	Contig1	40362	0.000	0.009
169	Contig1	42880	0.003	0.078

	id	contig	start	start15	end15	end	fst01
12	Contig103346_6131_16776	Contig103346	6131	7727	15180	16776	0.114973262
35	Contig27784_397_10761	Contig27784	397	1951	9207	10761	0.041095890
121	Contig282_28273_40182	Contig282	28273	30059	38396	40182	0.001795332
227	Contig28993_906_8396	Contig28993	906	2029	7273	8396	0.081395349
272	Contig92_19276_42960	Contig92	19276	22828	39408	42960	0.125000000
370	Contig42157_2034_10762	Contig42157	2034	3343	9453	10762	0.028571429

id	pst	fst01
Contig0_10497_95068	0.01933289	0.00000000
Contig1433_1_13800	0.20530881	0.02343096
Contig15905_1_15190	0.05149559	0.00000000
Contig15905_1_35236	0.05149559	0.00000000
Contig16063_563_35013	0.12577787	0.06563707
Contig16203_1_18094	0.11331736	0.04923077

	contig	start	end	fst01
75	Contig0	74000	75000	0.00000000
110	Contig0	109000	110000	0.06770833
158	Contig1	40000	41000	0.00000000
160	Contig1	42000	43000	0.03846154
216	Contig2	10000	11000	0.02127660
342	Contig2	136000	137000	0.00000000

id	contig	start	end	fst01
Contig0_74000_75000	Contig0	74000	75000	0.00000000
Contig0_109000_110000	Contig0	109000	110000	0.06770833
Contig1_40000_41000	Contig1	40000	41000	0.00000000
Contig1_42000_43000	Contig1	42000	43000	0.03846154
Contig2_10000_11000	Contig2	10000	11000	0.02127660
Contig2_136000_137000	Contig2	136000	137000	0.00000000

id	contig	start	end	pst	pst.lowCI	pst.highCI
Contig10074_5000_6000	Contig10074	5000	6000	0.25131285	0.0008842925	0.8672152
Contig100771_1000_1852	Contig100771	1000	1852	0.34311448	0.0015001933	0.8588288
Contig126977_2000_3000	Contig126977	2000	3000	0.62651934	0.0210034318	0.8771075
Contig128060_1000_1680	Contig128060	1000	1680	0.01810252	0.0005849085	0.8363022
Contig129509_0_1000	Contig129509	0	1000	0.24973351	0.0015760069	0.8427798
Contig132115_1000_1816	Contig132115	1000	1816	0.21746108	0.0009032690	0.8652226

id	contig	start	end	pst
Contig0_10497_95068	Contig0	10497	95068	0.01933289
Contig100_48322_58076	Contig100	48322	58076	0.06700810
Contig100188_1_4123	Contig100188	1	4123	0.73496201
Contig100199_5411_27437	Contig100199	5411	27437	0.06825011
Contig100396_1_4908	Contig100396	1	4908	0.18520587
Contig100499_508_4044	Contig100499	508	4044	0.15042326

id	contig	start	end	pst
Contig0_10497_95068	Contig0	10497	95068	0.01933289
Contig100188_1_4123	Contig100188	1	4123	0.73496201
Contig100396_1_4908	Contig100396	1	4908	0.18520587
Contig100994_1_8602	Contig100994	1	8602	0.70114357
Contig1013_14071_19514	Contig1013	14071	19514	0.01722196
Contig1013_16204_21663	Contig1013	16204	21663	0.03959333

id	contig	start	end	pst
Contig0_10497_95068	Contig0	10497	95068	0.01933289
Contig100188_1_4123	Contig100188	1	4123	0.73496201
Contig100396_1_4908	Contig100396	1	4908	0.18520587
Contig100743_7752_12849	Contig100743	7752	12849	0.51317776
Contig100994_1_8602	Contig100994	1	8602	0.70114357
Contig10117_48_10916	Contig10117	48	10916	0.05127876

	id	contig.x	start.x	end.x	pst	contig.y	start.y	end.y	fst01	startc
35	Contig20413_1_17467	Contig20413	1	17467	0.02072460	Contig20413	1	17467	0.4720812	-1515
61	Contig24693_2452_15381	Contig24693	2452	15381	0.34105167	Contig24693	2452	15381	0.5007496	2452
75	Contig28902_528_13774	Contig28902	528	13774	0.31127971	Contig28902	528	13774	0.4283427	528
133	Contig50657_1_11990	Contig50657	1	11990	0.03393099	Contig50657	1	11990	0.4868217	-1888
149	Contig77382_1559_12918	Contig77382	1559	12918	0.41550369	Contig77382	1559	12918	0.6633523	1559

	contig	start	end	fst01
8	Contig0	70000	80000	0.00000000
11	Contig0	100000	110000	0.06770833
17	Contig1	40000	50000	0.03448276
23	Contig2	10000	20000	0.02127660
35	Contig2	130000	139250	0.00000000
37	Contig3	10000	20000	0.25287356

id	contig	start	end	fst01
Contig0_70000_80000	Contig0	70000	80000	0.00000000
Contig0_100000_110000	Contig0	100000	110000	0.06770833
Contig1_40000_50000	Contig1	40000	50000	0.03448276
Contig2_10000_20000	Contig2	10000	20000	0.02127660
Contig2_130000_139250	Contig2	130000	139250	0.00000000
Contig3_10000_20000	Contig3	10000	20000	0.25287356

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12	PC13	PC14	PC15	PC16	PC17	PC18
hc1_2	-6.692850	4.371347	17.3591858	-9.77823057	0.03392218	-29.86492405	35.5278752	30.6193584	-4.9343922	1.6809432	-0.33319001	3.635139	-7.4010422	-1.7594517	3.1929463	0.78578319	-4.4636713	-2.304060e-14
hc1_4	-1.070833	15.671612	-0.3928227	-1.70710015	41.23235880	35.77341278	2.2218207	22.6387392	9.6957465	-0.1030809	-0.86444586	7.793098	2.1127411	-1.9011238	1.0629236	2.77676877	0.2719266	-4.127601e-14
hc2_15	-12.299954	10.306976	-8.1557636	-2.70502528	18.69976530	-31.90777310	-18.5266121	-11.0064755	32.4791342	-12.9709720	-12.42688061	6.839118	-5.7880514	-0.1922841	2.8387897	0.27435155	2.0688240	-2.724470e-14
hc2_17	-20.447126	9.462505	-2.1141525	4.20049318	11.22313158	-0.77622031	0.7696665	-5.0742263	-2.9998595	-1.6355382	15.84724957	-44.577822	-11.9680384	6.4587427	-9.2348469	-8.24820025	-1.5929793	1.984524e-14
hc3_1	39.034523	48.607826	-14.8380393	24.54435834	-20.88827041	3.21539341	13.3872950	-6.7302612	5.4916718	-3.3313800	0.04068923	1.333991	0.3918315	1.5024459	-0.5308704	2.08230580	-0.6792729	-1.282481e-14
hc3_5	-4.158491	15.894780	56.6155610	-2.33521782	-19.01246614	7.46016914	-25.9280178	3.7768436	0.2410256	-3.1401753	1.99055907	2.008911	-3.5426766	-0.3045020	0.8488112	-0.11514156	1.4576012	1.117509e-14
hc3_7	-3.864274	15.873938	-20.7614296	2.15581022	7.59235360	-16.98380550	-28.4280712	11.2734120	-33.2551537	19.2619936	4.18859370	7.355386	7.3000877	-5.3908788	-0.3526813	-0.96549984	1.6868523	-3.011653e-14
hc3_10	-26.775638	5.285006	16.3128676	3.29961637	11.01994934	-0.50438638	19.8221442	-30.5733695	-9.8219832	4.1390918	-10.16929815	3.537457	30.0290917	-6.4407773	-8.5153142	0.40346723	4.6974825	-2.846074e-14
hc3_11	27.358771	4.769939	-8.5306352	-59.71357725	-3.74778573	4.63932148	1.8642351	-13.3416913	-4.0485879	-6.2794531	8.25416810	-1.321221	1.6583165	-1.7369624	3.7856504	-0.97944952	0.8468419	-1.080733e-14
ss2_9	6.465812	-14.429699	-9.6526527	-1.47541437	-13.23682631	4.77846200	-6.5708353	18.3475636	-5.9277610	-17.3833320	-36.74039835	-16.780138	16.3704203	11.1439572	-1.2996767	-0.69204759	-0.3559637	4.914472e-15
ss2_14	-18.922299	-13.051948	-11.7284274	9.37222473	-14.48802057	6.02618124	-0.3432883	3.7470212	3.5191786	-14.7538726	12.47806816	-5.926382	5.2032870	-34.8767482	21.5234972	8.01383124	0.9745638	-3.814744e-14
ss2_18	57.203399	-31.515497	16.0043428	19.96257323	23.19173716	-9.36772786	-1.5263979	-8.2383146	-9.8901491	-9.6078438	7.31227535	-1.022245	-1.2873794	-2.3582088	1.3820464	-0.01808849	-0.3010683	-3.171205e-14
ss3_3	-1.586950	-13.016356	-4.4951886	2.37429362	-8.74625932	4.22362495	-2.2857498	-0.2209097	12.4528948	11.1203493	5.21846830	8.206049	7.9088060	-1.0529871	-4.8831301	-25.60237746	-34.1337760	-1.407739e-14
ss3_14	-6.869877	-13.758143	-5.7509940	-0.03503271	-7.97145122	0.09865517	-2.9408401	0.7549669	6.9849426	4.4075935	13.18907492	3.068868	3.8584631	12.8126462	-17.4943374	38.04138920	-11.8529642	-3.098823e-14
ss3_15	-23.724292	-6.297303	-6.2630517	7.77628176	-2.50136889	6.03173553	4.4649768	-8.9514123	-11.4648707	-11.7542464	9.96107454	14.292394	-5.7570909	31.8160870	26.2366619	-4.61349919	2.7657943	-1.370778e-14
ss3_16	-6.222188	-15.256037	-9.9880900	2.74993534	-12.89661904	3.47663653	2.1522917	8.3846073	5.2531997	-8.6952416	12.08045896	14.386765	-3.5246216	-1.1906847	-28.0187811	-15.53426597	26.2991427	1.160877e-14
ss3_20	-8.409625	-7.667093	-4.3401787	0.84692428	-2.47756877	11.84000340	4.2955124	-15.2525170	-13.0036782	7.6647033	-25.18073705	6.730737	-37.6991146	-11.2709100	-5.8189887	4.19794007	-5.2303088	9.542605e-16
ss5_18	10.981891	-15.251853	0.7194687	0.46708707	-7.02658154	1.84124156	2.0439948	-0.1533349	19.2286419	41.3804613	-4.84572988	-9.560104	2.1349702	4.7416398	15.2773000	0.19273281	17.5409751	-2.729078e-14

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12	PC13	PC14	PC15	PC16	PC17	PC18	Pop
hc1_2	-0.169157009	0.32979333	-0.0925416296	0.50520867	-0.294133348	0.26493543	-0.06140564	-0.006872229	0.04954539	-0.03079583	0.16689447	-0.019328195	-0.35883554	0.29791521	-0.122308974	-0.111055481	-0.375556625	0.144280831	HC
hc1_4	-0.271757005	-0.34897316	-0.2228580558	0.07179135	0.140243789	0.05948359	0.23457074	-0.431635637	0.58806689	-0.31398495	-0.05176609	0.072893855	-0.08775922	-0.04412887	0.022912601	-0.146603418	0.022401047	-0.020879124	HC
hc2_15	-0.071626776	-0.08966038	-0.0397213904	-0.09019719	0.008315516	-0.08914973	-0.13072719	-0.577920092	-0.10440079	0.71562241	-0.01947412	0.022977518	-0.18519793	0.22058976	0.071916895	-0.041643594	0.064886645	0.004739740	HC
hc2_17	-0.298667880	-0.46065814	0.1709509477	0.50626010	0.477551173	-0.14189869	-0.12229645	0.248062189	-0.22116443	0.09107086	0.10440924	-0.118132992	-0.02734497	0.03632917	0.021668054	-0.059933906	-0.003101467	0.013541656	HC
hc3_1	-0.256597468	-0.26360918	-0.1601116901	-0.25708967	-0.429531489	-0.01684808	-0.01589535	0.228308475	-0.20952811	-0.03248925	0.06665936	-0.095151431	-0.09836574	0.02562810	0.010099832	-0.650256453	0.209656174	-0.009993740	HC
hc3_10	-0.065071796	0.29433533	-0.1021351748	0.05611128	0.049239062	-0.72913326	0.52168553	-0.007792869	-0.08157511	-0.05036670	0.21913731	0.012004485	-0.11929153	0.11770265	0.002913704	-0.032029642	0.047016400	-0.024292181	HC
hc3_11	-0.125488142	0.01373902	0.8988541987	-0.11639476	-0.143160319	-0.01167964	0.05355827	-0.120221710	0.11441735	-0.16274347	0.03271447	-0.005991747	-0.19713438	0.15824428	0.058861658	-0.028844206	0.117987901	-0.042665776	HC
hc3_5	-0.143120769	0.25833924	-0.0773359071	0.39861545	-0.166206946	0.13312088	-0.01994485	0.001502115	0.08777540	0.06530467	-0.01659665	-0.105096992	0.07948560	-0.09322778	0.197338624	0.138111410	0.761771924	-0.159821780	HC
hc3_7	-0.318831783	-0.31672402	-0.1685526135	-0.20871865	-0.302968493	-0.06214955	-0.05833547	0.105436774	-0.07224308	-0.10760250	0.15323289	-0.041115290	-0.18920247	0.18938030	-0.009664227	0.707551090	0.006904790	-0.038549729	HC
ss2_14	0.243532694	-0.05325667	-0.0112504003	0.02660362	0.010415915	0.05821443	-0.07413337	-0.389989744	-0.30450396	-0.25018469	0.43696327	-0.402573092	-0.25713079	-0.44736762	-0.003418302	0.017336869	0.024482783	0.021291751	SS
ss2_18	0.286774500	-0.18692917	0.0607321050	0.07293295	-0.096864786	-0.06099770	-0.01245880	0.204805726	0.33236915	0.26832962	0.32740659	0.276267253	-0.19667989	-0.18134206	-0.581828420	0.009179303	0.205992957	0.002802899	SS
ss2_9	0.217164350	-0.13668071	-0.0359391218	0.12504185	-0.034491284	0.03055297	-0.09330715	-0.158611623	-0.28458837	-0.26532185	0.23254672	0.712236517	0.07543774	0.19810597	0.262751244	-0.030782937	0.152386118	0.174226060	SS
ss3_14	0.284005355	-0.13343295	-0.0453763001	0.14950017	-0.064156555	-0.06947412	0.06337588	0.096367552	-0.11549845	-0.04674180	-0.50853613	0.140984955	-0.58760720	-0.12835534	0.161687478	0.015612647	-0.044728107	-0.413190543	SS
ss3_15	0.008320234	0.25839268	-0.1403021067	-0.25629503	0.376373342	-0.17472840	-0.59718904	0.111137946	0.22635953	-0.19824638	0.09764560	-0.041632615	-0.32747893	0.23209975	0.054120731	-0.092662237	0.176367549	-0.032818887	SS
ss3_16	0.252509868	-0.11857098	-0.0431228268	0.08965063	0.019221920	-0.02240088	0.04005040	-0.180833165	-0.20228145	-0.25125342	-0.34995704	-0.255014779	0.06612093	0.46156279	-0.516495088	0.009494130	0.266183507	0.174456979	SS
ss3_20	0.037809434	0.04616455	-0.0589178291	-0.24481162	0.392205366	0.53899828	0.49733542	0.176921496	-0.11761826	0.10437955	0.19390864	-0.015883140	-0.25973585	0.22674915	0.052554708	0.009970900	0.158389076	-0.025277071	SS
ss3_3	0.372932794	-0.18422417	-0.0197498927	0.09594917	-0.116801908	-0.01000246	-0.01186712	0.018885450	0.18829491	0.01801898	0.31317672	-0.239693297	0.26625812	0.40494020	0.193512101	-0.069335517	-0.127424254	-0.564333793	SS
ss5_18	0.348617184	-0.18623575	-0.0002393696	0.05522893	-0.120708591	-0.10013944	0.07737120	0.187529089	0.27635145	0.11537599	-0.04890382	-0.260466163	-0.13365163	0.06453839	0.438908477	0.035969106	0.047603910	0.629948894	SS

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12	PC13	PC14	PC15	PC16	PC17	PC18
hc1_2	2.58520529	2.52082478	-1.2085058	0.42322452	0.54325748	0.1039631	-0.12694825	0.76376345	-0.96942144	0.26802083	0.28865161	-1.51780782	0.445506029	-0.12625313	0.1374234782	-0.405429804	-0.004794839	3.330669e-16
hc1_4	3.37080780	-2.03165236	0.8702686	-1.29204441	1.91824968	-0.4996976	0.47911154	0.31219593	0.35561796	0.80971097	0.02913808	0.74960408	0.446178287	0.07594788	-0.0002381671	-0.673094503	-0.135790637	-2.498002e-16
hc2_15	3.86829394	-1.33681409	0.8650964	0.78709933	-1.26146005	0.1721864	0.56342577	-0.88795865	-0.37425513	-0.80642763	0.80413379	0.04505579	0.379095401	-0.90594140	0.3619052557	0.223859298	-0.273138785	1.026956e-15
hc2_17	3.38228858	-0.79641793	-1.4876656	-1.25664040	1.33311484	0.6280170	0.11835032	0.50985459	0.59488917	-1.38658333	-0.59549147	-0.19059968	0.066777983	-0.30390921	-0.9218117044	0.217071952	0.093162128	7.632783e-16
hc3_1	1.81393178	-0.67259628	2.9870456	-0.40833186	-0.96025334	-0.8174120	-1.02180183	0.91534553	-0.92598761	0.98324685	-0.64844127	-0.10305661	-0.135350626	-0.55412374	-0.2333078766	0.277394323	0.218305193	-4.163336e-17
hc3_5	3.15464787	1.72341264	-1.1378371	-0.05172669	1.01491175	-0.8237016	0.87441578	0.89248770	-0.20670025	0.26927195	0.92423115	0.80427163	0.124159134	0.40792642	0.3863396250	0.562323232	0.195555875	-4.059253e-16
hc3_7	3.03723117	-2.21797016	0.5758374	0.82823062	-2.29779396	-0.1390859	0.01737066	0.38127916	0.42014818	-0.85970261	0.16518251	-0.31089880	-0.278629464	0.98636768	0.2374751890	-0.291878869	0.210038774	-4.128642e-16
hc3_10	3.12657476	2.05163630	-1.4785213	2.64148283	-0.56936840	0.1551002	-0.50077137	-0.37443424	0.85983088	0.36632514	-0.67663699	0.71482050	-0.818042865	-0.47403996	0.0124800260	-0.267051183	0.003900828	6.938894e-17
hc3_11	1.90514384	-0.10053224	-0.2313423	-0.02410457	1.00028093	-1.0818482	-1.08862894	-2.84220098	-0.68173469	0.30161141	-0.22254763	-0.25552827	-0.009977519	0.67586623	-0.2296772481	0.210401103	-0.042430308	5.412337e-16
ss2_9	-3.29604933	-0.36696617	-1.1240478	-1.64678552	-0.03140524	-1.1444626	-2.12193257	0.06073731	1.59177805	-0.14269409	0.03296847	-0.21700577	0.298625486	-0.34382530	0.7520590765	0.067221294	0.038251681	-1.665335e-16
ss2_14	-4.62761978	0.32016538	0.1023013	1.33200011	-0.43756652	0.8884132	-1.70058349	0.26035659	-0.41445441	-0.16073162	1.10376867	0.65106642	0.598388311	0.08450745	-0.7660601303	-0.167222788	0.034365840	-1.290634e-15
ss2_18	-3.15964800	-2.94944995	-2.4536016	1.41571219	-0.45780374	-0.4007593	0.56658560	1.03593882	-0.16075042	0.95973890	-0.35935370	-0.32331375	-0.066483322	0.22004619	-0.1606234117	0.315810332	-0.260083443	-9.992007e-16
ss3_3	-4.51647335	-1.63423052	-1.3518178	-0.43540364	0.43983796	1.2242943	1.19036209	-1.07786890	-1.10902628	0.03895321	-0.09456160	0.13664437	-0.205271840	-0.48921408	0.3702596694	-0.207845136	0.352042417	-1.415534e-15
ss3_14	-3.49585065	1.44235134	1.8117922	1.39008328	-0.13611863	-0.6455329	1.87460770	-0.59488368	1.39795831	0.09007089	-0.53640239	-0.27075052	0.968970471	0.05005773	-0.1640301250	0.072471797	0.089704849	-1.000935e-15
ss3_15	-0.50826754	-0.06622783	1.9832653	0.06545791	1.59826625	3.0244050	-0.16521858	0.01091822	0.98652309	0.48815728	0.51502137	-0.51785758	-0.760809159	0.19425483	0.1575905249	0.221552299	-0.070317115	-7.077672e-16
ss3_16	-2.89036430	1.04776489	1.2228544	0.50719492	1.16406626	0.1998446	-0.39106282	0.80257770	-1.21474302	-1.23994657	-1.09970478	0.45690810	-0.004092271	0.36003195	0.5480006844	0.007993032	-0.191693813	8.673617e-17
ss3_20	0.03871472	2.01719310	-0.6907287	-3.12339634	-2.82958681	1.5596246	0.49255668	-0.24716886	-0.12242973	0.44366221	-0.36019013	0.33441036	0.146179426	0.29177550	-0.1081014256	0.009544983	-0.148261334	-4.996004e-16
ss5_18	-3.78856679	1.04950912	0.7456069	-1.15205229	-0.03062846	-2.4033483	0.94016172	0.07906030	-0.02724268	-0.42268378	0.73023433	-0.18596246	-1.195223461	-0.14947506	-0.3796834404	-0.173121361	-0.108817312	-4.163336e-17