update all

01-SNPfilters/filtering.sh

@@ -133,15 +133,16 @@ vcftools --vcf DP3g95maf05.FIL.recode.vcf --positions positions_HoFis_$SPCODE.tx
 # 1- calculate individual homozygosity with vcftools
 # 2- calculate individual heterozygosity in R
 # 3- inspect values and keep only individuals with non-extreme values
-#    extreme values are defined as those that fall outside of 9 times the interquartal range. (typical outliers are defined as 1.5IQR) this was set arbitrarily after visual inspection, and to allows the same criteria to be applied to both species
+#    extreme values are defined as those that fall outside of 6 times the interquartal range. (typical outliers are defined as 1.5IQR) 
+#    this was set arbitrarily after visual inspection, and to allow the same criteria to be applied to both species
 
 # calculate individual homozygosity
 vcftools --vcf DP3g95maf05.FIL.HFis.recode.vcf --het --out DP3g95maf05.FIL.HFis.indHo
 cp DP3g95maf05.FIL.HFis.indHo.het DP3g95maf05.FIL.HFis.indHo.csv
 # calculate individual heterozygosity, asses extreme outliers and find individuals to keep
-Rscript --vanilla ../filterstep_indHetO_9IQR.R DP3g95maf05.FIL.HFis.indHo.csv indv_HETo_9IQR.txt
+Rscript --vanilla ../filterstep_indHetO_9IQR.R DP3g95maf05.FIL.HFis.indHo.csv indv_HETo_6IQR.txt
 # remove individuals
-vcftools --vcf DP3g95maf05.FIL.HFis.recode.vcf --keep indv_HETo_9IQR.txt --recode --recode-INFO-all --out DP3g95maf05.FIL.HFis.indHet
+vcftools --vcf DP3g95maf05.FIL.HFis.recode.vcf --keep indv_HETo_6IQR.txt --recode --recode-INFO-all --out DP3g95maf05.FIL.HFis.indHet
 
 # step 13 : rename final filtered dataset
 cp DP3g95maf05.FIL.HFis.indHet.recode.vcf "$SPCODE"_all_filtered_origid.vcf

01-SNPfilters/filterstep_Ho6_Fis5.R

@@ -28,9 +28,9 @@ hf <- genind2hierfstat(genind)
 stats <- basic.stats(hf)
 
 # Filter SNPs for Ho and Fis
-loc_H.6<- stats$perloc[which(stats$perloc$Ho<0.6),]
-loc_H.6_Fis.5 <- loc_H.6[which(loc_H.6$Fis<0.5 & loc_H.6>-0.5),]
-locnames<-rownames(loc_H.6_Fis.5)
+loc_H.6 <- stats$perloc[which(stats$perloc$Ho<0.6),]
+loc_H.6_Fis.5 <- loc_H.6[which(loc_H.6$Fis<0.5 & loc_H.6$Fis>-0.5),]
+locnames <-rownames(loc_H.6_Fis.5)
 print(paste("# SNPs to keep =", length(locnames)))
 
 # extract loci to remove from the dataset

01-SNPfilters/filterstep_indHetO_9IQR.R

 #!/usr/bin/env Rscript
 args = commandArgs(trailingOnly=TRUE)
+library(gridExtra)
 
 start <- Sys.time()
 print(paste("run started at",start))
 
 # Read homozygosity file
 dat <- read.csv(args[1], sep = "\t")
+# dat <- read.csv("~/Documents/project_SEACONNECT/seaConnect--dataPrep/01-SNPfilters/02-Mullus/DP3g95maf05.FIL.HFis.indHo.csv", sep = "\t")
+# dat <- read.csv("~/Documents/project_SEACONNECT/seaConnect--dataPrep/01-SNPfilters/01-Diplodus/DP3g95maf05.FIL.HFis.indHo.csv", sep = "\t")
 # calculate heterozygosity
 dat$O.HET. <- dat$N_SITES - dat$O.HOM.
 
 # in interactive mode: visualise heterozygosity to determine 
 # - if individuals need to be removed
 # - adapted boxplot range
-#   plot(dat$O.HET.)
-#   boxplot(dat$O.HET.)
-#   boxplot(dat$O.HET., range = 9)
-
-# extract non-extreme individuals
-OutVals <- boxplot(dat$O.HET., range = 9)$out
+pdf(file="plot_indv_HETo.pdf")
+   plot(dat$O.HET., main = "observed heterozygosity per individual")
+   boxplot(dat$O.HET., main = "observed heterozygosity per individual")
+   boxplot(dat$O.HET., range = 6, main = "o het with interquartal range = 6")
+
+# extract extreme individuals
+#OutVals <- boxplot(dat$O.HET., range = 9)$out
+OutVals <- boxplot(dat$O.HET., range = 6)$out
+plot.new()
+grid.table(dat[dat$O.HET. %in% OutVals,])
+dev.off()
 
 OutVals.ind <- as.character(dat$INDV[which(dat$O.HET. %in% OutVals)])
 print(paste("# Individuals to remove =", length(OutVals.ind)))
@@ -27,7 +35,7 @@ indToKeep <- setdiff(as.character(dat$INDV),OutVals.ind)
 print(paste("# Individuals to keep =", length(indToKeep)))
 
 # output individuals list
-write.table(indToKeep, args[2], sep = "\t", quote = F, row.names = F, col.names = F)
+write.table(indToKeep, args[3], sep = "\t", quote = F, row.names = F, col.names = F)
 
 # duration of script run
 end <- Sys.time()

01-SNPfilters/rename_id.R

@@ -6,23 +6,10 @@ id <- read.table(paste0("id_",args,".txt"), stringsAsFactors=F)
 
 # correct names
 
-if(args[1] == "mul") {
-  
-# mullus
-cell <- gsub("_.*","", id$V1)
-cell <- paste0("C", cell)
-ind <- gsub(".*_","", id$V1)
-ind <- gsub("E.*", "", ind)
-newid <- paste0(cell,"i", ind) 
-
-} else {
-  
-# dip
 cell <- gsub("_.*","", id$V1)
 ind <- gsub(".*i","", id$V1)
 ind <- gsub("E.*", "", ind)
 newid <- paste0(cell, "i", ind) 
-}
 
 # check
 bothid <- cbind(id, newid)

02-Bayescan/Bayescan_evaluation.R

-setwd("~/Documents/project_SEACONNECT/seaConnect--dataPrep/02-Bayescan/")
-#----- BayeScan -----#
-## evaluate model convergence and extract results
-## script adapted from http://evomicsorg.wpengine.netdna-cdn.com/wp-content/uploads/2016/01/BayeScan_BayeScEnv_exercises.pdf
-
-# load libraries
-library(coda)
-library(vcfR)
-
-# set arguments
-spCode <- "mul" # species
-runNum <- 1 # run number
-inputName <- paste0(spCode,"_bayesc_2pop_run",runNum) # 2pop or 3pop analysis (only for mullus)
-#vcfPath <- "../01-SNPfilters/02-Mullus/mul_all_filtered.vcf"
-#vcfPath <- "../01-SNPfilters/01-Diplodus/dip_all_filtered.vcf"
-
-## model convergence ####
-chain1 <- read.table(paste0(inputName,"_ch1.sel"),header=TRUE)
-#chain<-chain[-c(1)] # remove first column (step number)
-# create a MCMC object with the correct thinning interval
-chain1 <- mcmc(chain1,thin=10)
-# Plot
-plot(chain1)
-# Summary statistics
-summary(chain1)
-# Test correlation between sampled parameters to verify that the sample is large enough
-autocorr.diag(chain1)
-# Effective size of the sample
-effectiveSize(chain1)
-
-# Statistic test for convergence
-#### Geweke's convergence diagnistic
-#The diagnostic reports the z- scores for each parameter. 
-#With ?? = 0.05, We reject H0 (equality of means => convergence) if z < -1.96 or z > +1.96.
-geweke <- geweke.diag(chain1, frac1=0.1, frac2=0.5)
-geweke
-which(abs(geweke$z)>1.96) # 0 parameter does not converge
-
-#### Heidelberg and Welch's test
-heidel.diag(chain1, eps=0.1, pvalue=0.05) # all parameters passed
-
-#### Test comparing two chains
-chain2 <- read.table(paste0(inputName,"_ch2.sel"),header=TRUE)
-#chain2<-chain2[-c(1)]
-chain2 <- mcmc(chain2,thin=10)
-plot(chain2)
-
-combined = mcmc.list(chain1,chain2) 
-plot(combined)
-gelman.diag(combined) # should be close to 1
-gelman.plot(combined,ask)
-
-## Bayescan results ####
-
-## Functions defined in the script 'plot_R.r'in the BayeScan folder
-results<-plot_bayescan(paste0(inputName,"_ch2_fst.txt"),FDR=0.05)
-
-# detect outliers
-outliers_bayescan <-results$outliers # position of outliers
-results$nb_outliers #283 outliers for mullus, 126 for diplodus
-
-# extract corresponding SNP names and positions
-vcf <- read.vcfR(vcfPath, verbose=F)
-loci <- as.data.frame(vcf@fix[,1:2])
-outlier_loci <- loci[results$outliers,]
-
-# output positions table
-write.table(outlier_loci, file = paste0("outl_pos_bayesc_",spCode,"_run",runNum,".txt"), sep = "\t", quote = F, row.names = F, col.names = F)
-print(paste0("outlier positions table exported to outl_pos_bayesc_", spCode,"_run", runNum,".txt"), quote = 0)
-
-

02-Bayescan/Bayescan_input.R

-#----- BayeScan -----#
-## produce Bayescan input file
-# setwd("~/Documents/project_SEACONNECT/seaConnect--dataPrep/02-Bayescan/")
-
-#!/usr/bin/env Rscript
-args = commandArgs(trailingOnly=TRUE)
-#args <- c("../01-SNPfilters/01-Diplodus/dip_all_filtered.vcf","dip")
-#args <- c("../01-SNPfilters/02-Mullus/mul_all_filtered.vcf","mul")
-
-start <- Sys.time()
-
-# load libraries
-library(vcfR)
-library(adegenet)
-library(radiator)
-
-# define arguments
-inputVcf <- args[1]
-speciesCode <- args[2]
-outputBayeScan <- paste0(speciesCode,"_bayesc_input_2pop")
-  
-# import vcf file to genind
-vcfFile <- read.vcfR(inputVcf, verbose = F)
-!is.null(vcfFile)
-genind <- vcfR2genind(vcfFile)
-!is.null(genind)
-
-# use DAPC groups as pop
-grp <- find.clusters(genind, max.n.clust = 40, n.pca = round(nrow(genind$tab)/3))
-2
-grp$size 
-pop(genind) <- grp$grp
-pop(genind) <- paste0("pop", pop(genind))
-
-# convert to bayescan format
-tidy_genind <- tidy_genomic_data(genind)
-write_bayescan(tidy_genind, pop.select = levels(pop(genind)), filename = outputBayeScan)
-
-end <- Sys.time()
-print(paste("run ended at", end))
-end - start

02-Bayescan/plot_R.r

-# 	 This file is used to plot figures for the software Bayescan in R.
-
-#    This program, BayeScan, aims at detecting genetics markers under selection,
-#	 based on allele frequency differences between population. 
-#    Copyright (C) 2010  Matthieu Foll
-#
-#    This program is free software: you can redistribute it and/or modify
-#    it under the terms of the GNU General Public License as published by
-#    the Free Software Foundation, either version 3 of the License, or
-#    (at your option) any later version.
-#
-#    This program is distributed in the hope that it will be useful,
-#    but WITHOUT ANY WARRANTY; without even the implied warranty of
-#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#    GNU General Public License for more details.
-
-#    You should have received a copy of the GNU General Public License
-#    along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
-# Arguments:
-# - file is the name of your file ex: "output_fst.txt"
-# - the q-value threshold corresponding to the target False Discovery Rate (FDR)
-# - size is the size of the points and text labels for outliers
-# - pos is the distance between the points and the labels 
-# - highlight is a optional list of marker indices to display in red.
-# - name_highlighted alows to write the indices of highlighted markers instead of using a point like the other markers
-# - add_text adds the indices of the outlier markers
-
-# Output:
-# This function returns different paremeters in a list
-# - outliers: the list of outliers
-# - nb_outliers: the number of outliers
-
-# Typical usage: 
-# - load this file into R (file/source R code)
-# - in R, go to the directory where "output_fst.txt" is (file/change current dir)
-# - at the R prompt, type 
-# > plot_bayescan("output_fst.txt",0,FDR=0.05)
-# if you save the output in a variable, you can recall the different results:
-# results<-plot_bayescan("output_fst.txt",0,FDR=0.05)
-# results$outliers
-# results$nb_outliers
-
-#
-# plotting posterior distribution is very easy in R with the output of BayeScan:
-# first load the output file *.sel produced by BayeScan
-# > mydata=read.table("bi.sel",colClasses="numeric")
-# choose the parameter you want to plot by setting for example:
-# parameter="Fst1"
-# then this line will make the plot for:
-# > plot(density(mydata[[parameter]]),xlab=parameter,main=paste(parameter,"posterior distribution"))
-# you can plot population specific Fst coefficient by setting
-# parameter="Fst1"
-# if you have non-codominant data you can plot posterior for Fis coefficients in each population:
-# parameter="Fis1"
-# if you test for selection, you can plot the posterior for alpha coefficient for selection:
-# parameter="alpha1"
-# you also have access to the likelihood with:
-# parameter="logL"
-# if you have the package "boa" installed, you can very easily obtain Highest Probability 
-# Density Interval (HPDI) for your parameter of interest (example for the 95% interval):
-# > boa.hpd(mydata[[parameter]],0.05)
-
-
-plot_bayescan<-function(res,FDR=0.05,size=1,pos=0.35,highlight=NULL,name_highlighted=F,add_text=T)
-{
-if (is.character(res))
-  res=read.table(res)
-
-colfstat=5
-colq=colfstat-2
-
-highlight_rows=which(is.element(as.numeric(row.names(res)),highlight))
-non_highlight_rows=setdiff(1:nrow(res),highlight_rows)
-
-outliers=as.integer(row.names(res[res[,colq]<=FDR,]))
-
-ok_outliers=TRUE
-if (sum(res[,colq]<=FDR)==0)
-	ok_outliers=FALSE;
-
-res[res[,colq]<=0.0001,colq]=0.0001
-
-# plot
-plot(log10(res[,colq]),res[,colfstat],xlim=rev(range(log10(res[,colq]))),xlab="log10(q value)",ylab=names(res[colfstat]),type="n")
-points(log10(res[non_highlight_rows,colq]),res[non_highlight_rows,colfstat],pch=19,cex=size)
-
-if (name_highlighted) {
- 	if (length(highlight_rows)>0) {
- 		text(log10(res[highlight_rows,colq]),res[highlight_rows,colfstat],row.names(res[highlight_rows,]),col="red",cex=size*1.2,font=2)
- 	}
-}
-else {
-	points(log10(res[highlight_rows,colq]),res[highlight_rows,colfstat],col="red",pch=19,cex=size)
-	# add names of loci over p and vertical line
-	if (ok_outliers & add_text) {
-		text(log10(res[res[,colq]<=FDR,][,colq])+pos*(round(runif(nrow(res[res[,colq]<=FDR,]),1,2))*2-3),res[res[,colq]<=FDR,][,colfstat],row.names(res[res[,colq]<=FDR,]),cex=size)
-	}
-}
-lines(c(log10(FDR),log10(FDR)),c(-1,1),lwd=2)
-
-return(list("outliers"=outliers,"nb_outliers"=length(outliers)))
-}
-

02-Bayescan/run_bayescan.sh

-#!/bin/bash
-
-# first run, two chains per run
-# PCAdapt detected between two and three populations for mullus so we run the model for both options
-#BayeScan2.1_macos64bits mul_bayesc_input_2pop.txt -o mul_bayesc_2pop_run1_ch1 -n 5000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-#BayeScan2.1_macos64bits mul_bayesc_input_2pop.txt -o mul_bayesc_2pop_run1_ch2 -n 5000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-#
-#BayeScan2.1_macos64bits mul_bayesc_input_3pop.txt -o mul_bayesc_3pop_run1_ch1 -n 5000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-#BayeScan2.1_macos64bits mul_bayesc_input_3pop.txt -o mul_bayesc_3pop_run1_ch2 -n 5000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-
-# for diplodus: 2 populations detected
-#BayeScan2.1_macos64bits dip_bayesc_input_2pop.txt -o dip_bayesc_2pop_run1_ch1 -n 5000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-#BayeScan2.1_macos64bits dip_bayesc_input_2pop.txt -o dip_bayesc_2pop_run1_ch2 -n 5000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-
-# second run, two chains per run
-# here we try with a higher prior odds
-#BayeScan2.1_macos64bits mul_bayesc_input_2pop.txt -o mul_bayesc_2pop_run2_ch1 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 10000
-BayeScan2.1_macos64bits mul_bayesc_input_2pop.txt -o mul_bayesc_2pop_run2_ch2 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 10000
-
-#BayeScan2.1_macos64bits mul_bayesc_input_3pop.txt -o mul_bayesc_3pop_run2_ch1 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 10000
-#BayeScan2.1_macos64bits mul_bayesc_input_3pop.txt -o mul_bayesc_3pop_run2_ch2 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 10000
-#
-#BayeScan2.1_macos64bits dip_bayesc_input_2pop.txt -o dip_bayesc_2pop_run2_ch1 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 10000
-#BayeScan2.1_macos64bits dip_bayesc_input_2pop.txt -o dip_bayesc_2pop_run2_ch2 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 10000
-
-
-# third run
-# try again with prior odds = 100, and a greater number of iterations
-
-BayeScan2.1_macos64bits mul_bayesc_input_2pop.txt -o mul_bayesc_2pop_run3_ch1 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-BayeScan2.1_macos64bits mul_bayesc_input_2pop.txt -o mul_bayesc_2pop_run3_ch2 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-
-#BayeScan2.1_macos64bits mul_bayesc_input_3pop.txt -o mul_bayesc_3pop_run3_ch1 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-#BayeScan2.1_macos64bits mul_bayesc_input_3pop.txt -o mul_bayesc_3pop_run3_ch2 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-#
-#BayeScan2.1_macos64bits dip_bayesc_input_2pop.txt -o dip_bayesc_2pop_run3_ch1 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
-#BayeScan2.1_macos64bits dip_bayesc_input_2pop.txt -o dip_bayesc_2pop_run3_ch2 -n 10000 -thin 10 -nbp 20 -pilot 5000 -burn 50000 -pr_odds 100
\ No newline at end of file

03-PCAdapt/PCAdapt.R → 02-PCAdapt/PCAdapt.R

@@ -9,11 +9,13 @@ args = commandArgs(trailingOnly=TRUE)
 
 # load libraries
 library(pcadapt)
+library(qvalue)
 library(vcfR)
 
 # define arguments
 path_to_file <- args[1]
 speciesCode <- args[2]
+numPC <- args[3]
 
 # import vcf file to pcadapt format
 dat <- read.pcadapt(path_to_file, type = "vcf")
@@ -21,17 +23,23 @@ dat <- read.pcadapt(path_to_file, type = "vcf")
 # choose number K of principal components
 x <- pcadapt(input = dat, K = 20)
 # set plot output file name
-pdf(file=paste0("RPlots_", args[2],".pdf"))
+pdf(file=paste0("RPlots_", args[2],"_Kselection.pdf"))
 # scree plot of principal components
 plot(x, option = "screeplot", K = 10)
 # score plot of principal components
 plot(x, option = "scores")
 plot(x, option = "scores", i = 3, j = 4) # check PC axis 3 and 4
+plot(x, option = "scores", i = 1, j = 3)
+plot(x, option = "scores", i = 2, j = 3)
+dev.off()
 
-# compute outliers detection with K = 4
-x <- pcadapt(dat, K = 4)
+# compute outliers detection with K = 1 (# of PC to retain) for M. surmuletus.
+# K = 1 for D. sargus
+x <- pcadapt(dat, K = 1)
+summary(x)
+pdf(file=paste0("RPlots_", args[2],"_manhattan_qq_hist.pdf"))
 # Manhattan plot
-#jpeg(filename = paste0(args[2],"_manhattan_pcadpt",".jpeg"))
+#jpeg(filename = paste0("Rplots_",args[2],"_manhattan_pcadpt",".jpeg"))
 plot(x, option = "manhattan")
 #dev.off()
 # QQ plot
@@ -42,13 +50,28 @@ hist(x$pvalues, xlab = "p-values", main = NULL, breaks = 50, col = "orange")
 #dev.off()
 # histogram of test statistic
 plot(x, option = "stat.distribution")
+dev.off()
 
 # choose cutoff for outlier detection
+
+# q-values & FDR
+qval <- qvalue(x$pvalues)$qvalues
+alphaQ <- 0.005
+outliersQ <- which(qval < alphaQ)
+length(outliersQ)
+
 # Bonferroni correction
 padj <- p.adjust(x$pvalues, method="bonferroni")
-alpha <- 0.05
-outliers <- which(padj < alpha)
-print(paste(length(outliers), "outliers detected"),quote=0)
+alphaB <- 0.05
+outliersB <- which(padj < alphaB)
+print(paste(length(outliersB), "outliers detected"),quote=0)
+
+# compare
+setdiff(outliersB, outliersQ)
+setdiff(outliersQ, outliersB)
+
+# keep the ones inferred from FDR & q-values
+outliers <- outliersQ
 
 # extract corresponding SNP names and positions
 vcf <- read.vcfR(path_to_file, verbose=F)
@@ -57,4 +80,4 @@ outlier_loci <- loci[outliers,]
 
 # output positions table
 write.table(outlier_loci, file = paste0("outl_pos_pcadpt_",speciesCode,".txt"), sep = "\t", quote = F, row.names = F, col.names = F)
-print(paste0("outlier positions table exported to outl_pos_pcadpt_",speciesCode), quote = 0)
+print(paste0(length(outliers), " outlier positions exported to outl_pos_pcadpt_",speciesCode), quote = 0)

04-finalPrep/outlier_positions.sh → 03-finalPrep/outlier_positions.sh

@@ -4,28 +4,20 @@
 
 inputFile=$1
 spCode=$2
-runNum=$3
 
 #### adaptive SNPs ####
 
-## step 1: create a final list of all outlier loci positions
-
-cat ../02-Bayescan/outl_pos_bayesc_"$spCode"_"$runNum".txt ../03-PCAdapt/outl_pos_pcadpt_"$spCode".txt > "$spCode"_outl_pos_combi.txt
-
-# how many are detected by both methods?
-nPosOutDup=`sort "$spCode"_outl_pos_combi.txt | uniq -d | wc -l` 
-echo ""$nPosOutDup" outlier loci detected with both methods"
+# step 1: rename list of outlier positions
+cp ../03-PCAdapt/outl_pos_pcadpt_"$spCode".txt "$spCode"_outl_pos.txt
 # extract total number of unique outlier positions (for file naming)
-nPosOut=`sort "$spCode"_outl_pos_combi.txt | uniq -u | wc -l` 
+nPosOut=`sort "$spCode"_outl_pos.txt | uniq -u | wc -l` 
 echo ""$nPosOut" unique outlier loci in total"
 # remove leading whitespaces (from here: https://www.cyberciti.biz/faq/bash-remove-whitespace-from-string/)
 shopt -s extglob # turn it on
 nPosOut="${nPosOut##*( )}"
 shopt -u extglob # turn it off
-
-# extract file with unique positions
-sort "$spCode"_outl_pos_combi.txt | uniq -u > "$spCode"_outl_pos_"$nPosOut".txt
-#rm "$spCode"_outl_pos_combi.txt
+# add number of outliers to file name
+mv "$spCode"_outl_pos.txt "$spCode"_outl_pos_"$nPosOut".txt
 
 ## step 2: subset filtered vcf file by outlier positions
 

README.md

@@ -39,44 +39,6 @@ bash filtering.sh ../00-rawData/02-Mullus/mullus.vcf 02-Mullus mul
 The final step consists of removing individuals with (extreme) outlier heterozygosity values. Here we define outliers as falling outside 6 * interquartile range. 
 This theshold value is set in accordance to our data and it is advised to look at the outputted figures to validate this choice for your data.
 
-### SNP filtering results for Mullus surmuletus					
-				
-| filtering step | filter for                                        | individuals retained | SNPs retained | run time (sec) | output |
-| -------------- | ------------------------------------------------- | :------------------: | :-----------: | :------------: | ------ |
-| step 0         | ddocent output data                               | 526                  |               |                | mullus.vcf
-| step 1         | call below 50%, mac < 3, min quality score = 30   | 526                  | 549881        | 71.00          | mullus.g5mac3.recode.vcf
-| step 2         | min mean depth genotype call = 3                  | 482                  | 549881        | 74.00          | mullus.g5mac3dp3.recode.vcf
-| step 3         | individuals > 50% missing data                    | 482                  | 549881        | 70.00          | mullus.g5mac3dplm.recode.vcf
-| step 4         | sites > 5% missing data, maf 0.05, min meanDP = 5 | 482                  | 3657          | 14.00          | DP3g95maf05.recode.vcf
-| step 5         | filter for allele balance                         | 482                  | 3421          |                | DP3g95maf05.fil1.vcf
-| step 6         | filter out sites with reads from both strands     | 482                  | **SKIP**      |	             | 
-| step 7         | ration mapping qualities ref vs alternate alleles | 482                  | 3284          |                | DP3g95maf05.fil3.vcf
-| step 8         | paired status                                     | 482                  | 3284          |                | DP3g95maf05.fil4.vcf
-| step 9         | remove sites quality score < 1/4 depth            | 482                  | 3263          |                | DP3g95maf05.fil5.vcf
-| step 10        | depth x quality score cutoff	                     | 482                  | 2857          |	             | 
-| step 11        | He > 0.6 & Fis > 0.5 & Fix < -0.5                 | 482                  | 2794          | 25 min         | DP3g95maf05.FIL.HFis.recode.vcf
-| step 12        | remove extreme outliers individual O HET          | 482                  | 2794          | 23.00          | DP3g95maf05.FIL.HFis.indHet.recode.vcf
-| step 13        | rename                                            |                      |               |                | mul_all_filtered_origid.vcf
-
-### SNP filtering results for Diplodus sargus								
-				
-| filtering step | filter for                                        | individuals retained | SNPs retained | run time (sec) | output |
-| -------------- | ------------------------------------------------- | :------------------: | :-----------: | :------------: | ------ |
-| step 0         | ddocent output data                               | 297                  | 13362         |                | sar_ddocent_.vcf
-| step 1         | call below 50%, mac < 3, min quality score = 30   | 297                  | 13362         | 14.00          | g5mac3.recode.vcf
-| step 2         | min mean depth genotype call = 3                  | 297                  | 13362         | 14.00          | g5mac3dp3.recode.vcf
-| step 3         | individuals > 50% missing data                    | 297                  | 13362         | 16.00          | g5mac3dplm.recode.vcf
-| step 4         | sites > 5% missing data, maf 0.05, min meanDP = 5 | 297                  | 10389         | 11.00          | DP3g95maf05.recode.vcf
-| step 5         | filter for allele balance                         | 297                  | 10202         |                | DP3g95maf05.fil1.vcf
-| step 6         | filter out sites with reads from both strands     |                      | **SKIP**      |                | SKIP
-| step 7         | ration mapping qualities ref vs alternate alleles | 297                  | 9689          |                | DP3g95maf05.fil3.vcf
-| step 8         | paired status                                     | 297                  | 9689          |                | DP3g95maf05.fil4.vcf
-| step 9         | remove sites quality score < 1/4 depth            | 297                  | 9688          |                | DP3g95maf05.fil5.vcf
-| step 10        | depth x quality score cutoff	                     | 297                  | 8325          | 11.00          | 
-| step 11        | He > 0.6 & Fis > 0.5 & Fix < -0.5                 | 297                  | 8206          | 27 min         | DP3g95maf05.FIL.HFis.recode.vcf
-| step 12        | remove extreme outliers individual O HET          | 297                  | 8206          |                | DP3g95maf05.FIL.HFis.indHet.recode.vcf
-| step 13        | rename                                            |                      |               |                | dip_all_filtered_origid.vcf 
-
 ### rename individuals for conventional naming system and create population map
 this is adapted for our case, review R script or skip according to naming needs