Starting with each mapper having been used and created the .bam files and the large .sync file:
Each .sync files (main chromosomes all together) and the .bam files after GATK relaignment
Novoalign
novo_episodic_main.sync
F115ConR1_TAGCTT_novo_merge_novo_final_realigned.bam
F115ConR2_GGCTAC_novo_merge_novo_final_realigned.bam
F115SelR1_GTTTCG_novo_merge_novo_final_realigned.bam
F115SelR2_GTGGCC_novo_merge_novo_final_realigned.bam
F38ConR1_ATCACG_novo_merge_novo_final_realigned.bam
F38ConR2_TTAGGC_novo_merge_novo_final_realigned.bam
F38SelR1_ACTTGA_novo_merge_novo_final_realigned.bam
F38SelR2_GATCAG_novo_merge_novo_final_realigned.bam
F77ConR1_ATGTCA_novo_merge_novo_final_realigned.bam
F77ConR2_ATTCCT_novo_merge_novo_final_realigned.bam
F77SelR1_TTAGGC_novo_merge_novo_final_realigned.bam
F77SelR2_GATCAG_novo_merge_novo_final_realigned.bam
MGD3_SO_CAGATC_novo_merge_novo_final_realigned.bam
Bwa -mem
episodic_data_main.gatk.sync
F115ConR1_TAGCTT_merged_aligned_pe.final_realigned.bam
F115ConR2_GGCTAC_merged_aligned_pe.final_realigned.bam
F115SelR1_GTTTCG_merged_aligned_pe.final_realigned.bam
F115SelR2_GTGGCC_merged_aligned_pe.final_realigned.bam
F38ConR1_ATCACG_merged_aligned_pe.final_realigned.bam
F38ConR2_TTAGGC_merged_aligned_pe.final_realigned.bam
F38SelR1_ACTTGA_merged_aligned_pe.final_realigned.bam
F38SelR2_GATCAG_merged_aligned_pe.final_realigned.bam
F77ConR1_ATGTCA_merged_aligned_pe.final_realigned.bam
F77ConR2_ATTCCT_merged_aligned_pe.final_realigned.bam
F77SelR1_TTAGGC_merged_aligned_pe.final_realigned.bam
F77SelR2_GATCAG_merged_aligned_pe.final_realigned.bam
MGD3_SO_CAGATC_merged_aligned_pe.final_realigned.bam
Bowtie
episodic_data_bowtie_main.gatk.sync
F115ConR1_TAGCTT_merged_bowtie_pe.final_realigned.bam
F115ConR2_GGCTAC_merged_bowtie_pe.final_realigned.bam
F115SelR1_GTTTCG_merged_bowtie_pe.final_realigned.bam
F115SelR2_GTGGCC_merged_bowtie_pe.final_realigned.bam
F38ConR1_ATCACG_merged_bowtie_pe.final_realigned.bam
F38ConR2_TTAGGC_merged_bowtie_pe.final_realigned.bam
F38SelR1_ACTTGA_merged_bowtie_pe.final_realigned.bam
F38SelR2_GATCAG_merged_bowtie_pe.final_realigned.bam
F77ConR1_ATGTCA_merged_bowtie_pe.final_realigned.bam
F77ConR2_ATTCCT_merged_bowtie_pe.final_realigned.bam
F77SelR1_TTAGGC_merged_bowtie_pe.final_realigned.bam
F77SelR2_GATCAG_merged_bowtie_pe.final_realigned.bam
MGD3_SO_CAGATC_merged_bowtie_pe.final_realigned.bam
Split the sync file by chromosome:
Script: novo_split_sync2chromosomes.sh
Will show the procedure for each mapper to generate files for outline of methods
Create pileup files for every .bam file
ex.
samtools mpileup -B -Q 0 -f ${ref_genome} ${input}/${base}_merge_novo_final_realigned.bam > ${output}/${base}.pileup
Novoalign
Bwa -mem
Bowtie
Run script to calcualte Tajima's Pi using the Variance-sliding.pl script from Popoolation1
ex.
perl ${popoolation}/Variance-sliding.pl --input ${input}/${base}.pileup --output ${output}/${base}.pi --measure pi --window-size 10000 --step-size 10000 --min-count 2 --min-coverage 4 --max-coverage 400 --min-qual 20 --pool-size 120 --fastq-type sanger --snp-output ${output}/${base}.snps --min-covered-fraction 0.5
Create plots of tajima Pi data
On local machine, this R function can run each .pi file to output a plot for each mapper
ex.
Pi_PlotFunction('FILE.pi', "Plot_Title_Details-(i.e mapper)")
Not done for bwa-mem (worth it??)
Running Fst
ex.
perl ${fst} --input ${novo_mpileup}/novo_episodic_main.sync --output ${novo_fst}/novo_episodic_main.fst --min-count 6 --min-coverage 10 --max-coverage 250 --min-covered-fraction 1 --window-size 500 --step-size 500 --pool-size 120
Novoalign
#!/bin/bash
#Variable for project name (title of mpileup file)
project_name=novo_episodic
#Variable for project:
project_dir=/home/paul/episodicData/novoalign
#Path to .sync files
novo_mpileup=${project_dir}/novo_mpileup
#Path and variable for script from PoPoolation to create .sync files
fst=/usr/local/popoolation/fst-sliding.pl
mkdir ${novo_mpileup}/novo_fst
novo_fst=${novo_mpileup}/novo_fst
perl ${fst} --input ${novo_mpileup}/novo_episodic_main.sync --output ${novo_fst}/novo_episodic_main.fst --min-count 6 --min-coverage 10 --max-coverage 250 --min-covered-fraction 1 --window-size 500 --step-size 500 --pool-size 120
Bwa -mem
#!/bin/bash
#Variable for project:
project_dir=/home/paul/episodicData
#Path to .sync files
input=${project_dir}/R_dir
#Path and variable for script from PoPoolation to create .sync files
fst=/usr/local/popoolation/fst-sliding.pl
#mkdir ${project_dir}/bwa_fst
#output=${project_dir}/bwa_fst
output=/home/paul
perl ${fst} --input ${input}/episodic_data_main.gatk.sync --output ${output}/episodic_data_main.fst --min-count 6 --min-coverage 10 --max-coverage 250 --min-covered-fraction 1 --window-size 500 --step-size 500 --pool-size 120
Bowtie
#!/bin/bash
#Variable for project:
project_dir=/home/paul/episodicData/bowtie
#Path to .sync files
input=${project_dir}/R_bowtie
#Path and variable for script from PoPoolation to create .sync files
fst=/usr/local/popoolation/fst-sliding.pl
#mkdir ${project_dir}/bowtie_fst
#output=${project_dir}/bowtie_fst
output=/home/paul
perl ${fst} --input ${input}/episodic_data_bowtie_main.gatk.sync --output ${output}/episodic_data_bowtie_main.fst --min-count 6 --min-coverage 10 --max-coverage 250 --min-covered-fraction 1 --window-size 500 --step-size 500 --pool-size 120
Combine here??
In R, split the file into each compasison
Works for all three just by changing the working directory and the file read in
# Script to read in one .fst file and split this into many .csv files based on comparisons
# .fst files generated from Popoolation2 fst-sliding.pl script
# Need to change the working directory, the input, and the number of comparisons present (i.e 6:ncol for .fst file)
### Packages Required (tidyverse, but more specifically tidyr)
require(data.table)
require(tidyverse)
######### NOVOALIGN ##########
### Set working directory to location of .fst file
setwd("~/Bioinformatics/episodic_practice/FST/novo_fst")
### Read in the .fst file into R (requires data.table)
Fst <- fread('novo_episodic_main.fst')
######### Bowtie2 ##########
### Set working directory to location of .fst file
#setwd("~/Bioinformatics/episodic_practice/FST/bowtie_fst")
### Read in the .fst file into R (requires data.table)
#Fst <- fread('episodic_data_bowtie_main.fst')
######### BWA-MEM ##########
### Set working directory to location of .fst file
#setwd("~/Bioinformatics/episodic_practice/FST/bwa_fst")
### Read in the .fst file into R (requires data.table)
#Fst <- fread('episodic_data_main.fst')
### Make into long format
XCC <- gather(Fst, Comparison, Fst_measure, 6:83, factor_key=TRUE)
### Remove intermediate:
rm(Fst_novo)
### Seperate the Fst (ex. 1:2=na) into a comparison column and a fst column
novo_Fst <- XCC %>%
separate(Fst_measure, "=", into = c('Comp', 'Fst'))
### Remove intermediate:
rm(XCC)
### Remove unnecessary column (column 6 has no value)
novo_Fst <- novo_Fst[,c(1,2,3,4,5,7,8)]
### Rename columns:
colnames(novo_Fst) <- c('chr', 'window', "num", 'frac', 'meanCov','Comp', 'Fst')
### Create list of all unique comparisons:
X_compLIST <- unique(novo_Fst$Comp)
### For loop that will create a .csv file for every comparison:
for (vxcx in X_compLIST){
CXV_Comp <- novo_Fst[which(novo_Fst$Comp==vxcx),]
write.csv(CXV_Comp, file=paste("Novo_fst_", vxcx, '.csv', sep = ""), row.names = FALSE)
}
R script that will split the .fst file into many .csv files with each comparison of interest (can choose the necessary ones from here)
Combining the data files from three mappers
## Packages:
require(data.table)
require(tidyverse)
## Main directory holding fst directories for mappers:
setwd("/Users/paulknoops/Bioinformatics/episodic_practice/FST")
## List directories in working directories:
workdir <- getwd()
mydirs <- list.dirs(path=workdir, recursive = FALSE)
## Make list of compasisons to combine (no use doing them all?)
patty <- c('_fst_1:3.csv', '_fst_2:4.csv', '_fst_5:7.csv', '_fst_6:8.csv', '_fst_9:11.csv', '_fst_10:12.csv')
#For each comparison:
for (patt in patty){
mtdf <- NULL
# For each directory holding the comparisons for each mapper (should be in working directory)
for (dir in mydirs){
#All files that end with the comparison of interest in the dir (i.e. the one comparison for each mapper)
mycomp <- list.files(path = dir, pattern=patt, full.names = T)
for (file in mycomp){
Xc <- fread(file)
Xc <- Xc[-which(Xc$Fst=='na'),]
Xc$Fst <- as.numeric(Xc$Fst)
}
mtdf <- rbind(mtdf, Xc)
}
# Get a count of the number of occurances of a position (window)
comp_final <- mtdf %>%
group_by(chr, window, Comp) %>%
mutate(count = n())
# Keep those with a count of 3 (three times mapped)
compcomb <- comp_final[which(comp_final$count==3),]
#Calcualte mean Fst:
comp_final_2 <- mtdf %>%
group_by(chr, window, Comp) %>%
summarise(meanFst = (mean(Fst)))
# Writes many files with the mean FST value of the three mappers for the comparisons of interest
write.csv(comp_final_2, file=paste("combined", patt, sep = ""), row.names = FALSE)
}
#Plot data?
require(data.table)
require(tidyverse)
setwd("/Users/paulknoops/Bioinformatics/episodic_practice/FST")
XC2 <- fread('combined_fst_1:3.csv')
CX2 <- fread('combined_fst_2:4.csv')
tttle <- 'F115'
ddat <- rbind(CX2, XC2)
rm(CX2)
rm(XC2)
#ddat
ddat2 <- ddat %>%
group_by(chr, window) %>%
summarise(meanFst = (mean(meanFst)))
rm(ddat)
xcs <- mean(ddat2$meanFst)
#median(ddat2$meanFst)
g <- nrow(ddat2[which(ddat2$chr=='2L'),])
h <- nrow(ddat2[which(ddat2$chr=='2R'),])
i <- nrow(ddat2[which(ddat2$chr=='3L'),])
j <- nrow(ddat2[which(ddat2$chr=='3R'),])
k <- nrow(ddat2[which(ddat2$chr=='4'),])
l <- nrow(ddat2[which(ddat2$chr=='X'),])
#To change the order for X to be first:
ddat2$number <- c((l+1):(l+g),
(l+g+1):(l+g+h),
(l+g+h+1):(l+g+h+i),
(l+g+h+i+1):(l+g+h+i+j),
(l+g+h+i+j+1):(l+g+h+i+j+k),
(1:l))
ggxcv <- ggplot(data = ddat2, aes(x=number, y=meanFst, color=chr))
ggxcv2 <- ggxcv +
geom_point(size=0.2, show.legend = F, alpha = 0.75) +
theme(panel.background = element_blank()) +
scale_y_continuous(limits=c(0, 1), breaks=seq(0, 1, 0.1)) +
geom_hline(yintercept = xcs) +
xlab("Chromosome") +
ggtitle(tttle) +
scale_x_discrete(limits=c(l/2, l+(g/2), (l+g+(h/2)), (l+g+h+(i/2)), (l+g+h+i+(j/2)), (l+g+h+i+j+(k/2))), labels = c("X","2L", "2R", '3L', '3R', "4")) +
scale_colour_manual(values=c("#56B4E9", "#E69F00", 'grey30', 'grey46', 'wheat3', 'lemonchiffon4'))
print(ggxcv2)
Long Script:* novo_regression_model_LONGSCRIPT.sh
This script will break the chromosomal .sync files into smaller managable pieces and run through multiple R scripts while removing intermediates:
R script to covert sync to Count data: Sync_to_counts.R
ex.
Rscript Sync_to_counts.R '${Sync Directory}'
Running the model for each position along the chromosome: Counts_to_model.R
In long script: this is set up to work in parallel, having each chromosome running at the same time (6 instances running over 11 sections)
NOTE: Script needs to be changed to run faster/ more efficiently ex.
Rscript Counts_to_model_2.R 'DIRECTORY'
Combine all the split chromosome pieces back into one chromosome: Combine_chromo.R
ex.
Rscript Combine_chromo.R 'DIRECTORY' 'OutputDIRECTORY'
#! /bin/bash
### Set all variables (need to make an output directory):
#Variable for project name (title of mpileup file)
project_name=novo_episodic
#Variable for project:
project_dir=/home/paul/episodicData/novoalign
#Path to .sync files
SyncFiles=${project_dir}/novo_mpileup
mkdir ${SyncFiles}/splitsync_dir
splitSync=${SyncFiles}/splitsync_dir
#Output dir:
poolSeq=${project_dir}/novo_PoolSeq
# Need to copy three R scripts and add to a new directory (i.e. novo_Rscripts)
Rscripts=${project_dir}/novo_Rscripts
# The seperated .sync files
sync[0]=${SyncFiles}/novo_episodic_3R.sync
sync[1]=${SyncFiles}/novo_episodic_2R.sync
sync[2]=${SyncFiles}/novo_episodic_3L.sync
sync[3]=${SyncFiles}/novo_episodic_2L.sync
sync[4]=${SyncFiles}/novo_episodic_X.sync
sync[5]=${SyncFiles}/novo_episodic_4.sync
##-----------------------------------------------##
### Split into treatment vs. control
for file in ${sync[@]}
do
name=${file}
base=`basename ${name} .sync`
cat ${SyncFiles}/${base}.sync | awk '{print $1,$2,$3,$6,$7,$10, $11, $14, $15, $16, $16}' > ${splitSync}/${base}_Sel.sync
cat ${SyncFiles}/${base}.sync | awk '{print $1,$2,$3,$4,$5,$8, $9, $12, $13, $16, $16}' > ${splitSync}/${base}_Con.sync
done
##------------------------------------------------##
### Split the sync files into many sized files (12):
files=(${splitSync}/*.sync)
for file in ${files[@]}
do
name=${file}
base=`basename ${name} .sync`
mkdir ${splitSync}/${base}_Split
split_sync=${splitSync}/${base}_Split
length=($(wc -l ${splitSync}/${base}.sync))
#Split length into 12 segements (12th == length) (can extend this if to large)
cut=$((${length}/12))
cut_2=$((${cut}*2))
cut_3=$((${cut}*3))
cut_4=$((${cut}*4))
cut_5=$((${cut}*5))
cut_6=$((${cut}*6))
cut_7=$((${cut}*7))
cut_8=$((${cut}*8))
cut_9=$((${cut}*9))
cut_10=$((${cut}*10))
cut_11=$((${cut}*11))
sed -n " 1, ${cut} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_1.sync
sed -n " $((${cut} + 1)), ${cut_2} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_2.sync
sed -n " $((${cut_2} + 1)), ${cut_3} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_3.sync
sed -n " $((${cut_3} + 1)), ${cut_4} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_4.sync
sed -n " $((${cut_4} + 1)), ${cut_5} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_5.sync
sed -n " $((${cut_5} + 1)), ${cut_6} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_6.sync
sed -n " $((${cut_6} + 1)), ${cut_7} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_7.sync
sed -n " $((${cut_7} + 1)), ${cut_8} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_8.sync
sed -n " $((${cut_8} + 1)), ${cut_9} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_9.sync
sed -n " $((${cut_9} + 1)), ${cut_10} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_10.sync
sed -n " $((${cut_10} + 1)), ${cut_11} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_11.sync
sed -n " $((${cut_11} + 1)), ${length} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_12.sync
syncs=(${split_sync}/*.sync)
for file in ${syncs[@]}
do
(Chromo=$(cat ${file} | awk '{print $1; exit}')
Rscript ${Rscripts}/PoolSeq_SelCoeff.R ${file} ${Chromo} ${split_sync}) &
done
wait
rm -f ${split_sync}/*.sync
done
wait
Rscript ${Rscripts}/combinePoolseqCSV.R ${splitSync}
##------------------------------------------------##
#RscriptTest:
args <- commandArgs(trailingOnly = TRUE)
require(methods)
require(data.table)
require(foreach)
require(stringi)
require(matrixStats)
### Source the scripts (Copied) for Pool-Seq (only one fails and is not needed)
source('/home/paul/episodicData/novoalign/novo_Rscripts/Taus_Scripts/testTaus/loadaf.R')
source('/home/paul/episodicData/novoalign/novo_Rscripts/Taus_Scripts/testTaus/estsh.R')
source('/home/paul/episodicData/novoalign/novo_Rscripts/Taus_Scripts/testTaus/idsel.R')
source('/home/paul/episodicData/novoalign/novo_Rscripts/Taus_Scripts/testTaus/simaf.R')
### Possibly need custom function to read in manipulated .sync files:
### With fiddling with the .sync file, a personal read.sync function is needed
source("/home/paul/episodicData/novoalign/novo_Rscripts/Taus_ReadSync.R")
### Read in the data file for args[1]
setwd(args[3])
mySync <- read.sync_Personal(file=args[1], gen=c(115, 115, 38, 38, 77, 77, 0, 0), repl=c(1,2,1,2,1,2,1,2), polarization = "rising")
# Turn alleles to data frame:
ff <- as.data.frame(mySync@alleles)
# Find length (number of positons)
len <- round(length(ff$posID))
# Keep only positions:
pst <- as.numeric(ff$pos)
pst2 <- sort(pst)
# Generations:
ccc <- c(0,38,77,115)
rm(pst)
rm(ff)
pst2 <- sample(pst2[1]:pst2[len], 1000)
### Create empty matrix to read into for estiamting S:
pbj <- matrix(NA,length(pst2), 3)
for (i in 1:length(pst2)) {
b_b <- pst2[i]
TrajTEST <- af.traj(mySync, args[2], repl=c(1,2), pos=b_b)
BfsfTEST <- estimateSH(TrajTEST, Ne=150, t=ccc, h=0.5, haploid = FALSE, simulate.p.value=TRUE)
pbj[i,] <- c(BfsfTEST$s, BfsfTEST$p.value, b_b)
rm(TrajTEST)
rm(BfsfTEST)
rm(b_b)
}
x2 <- args[1]
x3 <- gsub("\\..*","", x2)
write.csv(pbj, file=paste(args[3], "/", x3, ".csv", sep=""), row.names=FALSE)
rm(pbj)
rm(mySync)
rm(ccc)
rm(pst2)
- Rscripts should be the same:
- need to split into Chromosomes first:
#!/bin/bash
#Name of the .sync file for spliting:
project_name=episodic_data
#Variable for project:
project_dir=/home/paul/episodicData
#Path to .sync files
SyncFiles=${project_dir}/mpileup_dir
mkdir ${SyncFiles}/splitsync_dir
splitSync=${SyncFiles}/splitsync_dir
# Need to copy three R scripts and add to a new directory (i.e. novo_Rscripts)
Rscripts=${project_dir}/novoalign/novo_Rscripts
grep '3R' ${SyncFiles}/${project_name}_main.gatk.sync > ${SyncFiles}/${project_name}_3R.sync &
grep '2R' ${SyncFiles}/${project_name}_main.gatk.sync > ${SyncFiles}/${project_name}_2R.sync &
grep '3L' ${SyncFiles}/${project_name}_main.gatk.sync > ${SyncFiles}/${project_name}_3L.sync &
grep '2L' ${SyncFiles}/${project_name}_main.gatk.sync > ${SyncFiles}/${project_name}_2L.sync &
grep '^4' ${SyncFiles}/${project_name}_main.gatk.sync > ${SyncFiles}/${project_name}_4.sync &
grep 'X' ${SyncFiles}/${project_name}_main.gatk.sync > ${SyncFiles}/${project_name}_X.sync
wait
# The seperated .sync files
sync[0]=${SyncFiles}/${project_name}_3R.sync
sync[1]=${SyncFiles}/${project_name}_2R.sync
sync[2]=${SyncFiles}/${project_name}_3L.sync
sync[3]=${SyncFiles}/${project_name}_2L.sync
sync[4]=${SyncFiles}/${project_name}_X.sync
sync[5]=${SyncFiles}/${project_name}_4.sync
##-----------------------------------------------##
### Split into treatment vs. control
for file in ${sync[@]}
do
name=${file}
base=`basename ${name} .sync`
cat ${SyncFiles}/${base}.sync | awk '{print $1,$2,$3,$6,$7,$10, $11, $14, $15, $16, $16}' > ${splitSync}/${base}_Sel.sync
cat ${SyncFiles}/${base}.sync | awk '{print $1,$2,$3,$4,$5,$8, $9, $12, $13, $16, $16}' > ${splitSync}/${base}_Con.sync
done
##------------------------------------------------##
### Split the sync files into many sized files (15):
files=(${splitSync}/*.sync)
for file in ${files[@]}
do
name=${file}
base=`basename ${name} .sync`
mkdir ${splitSync}/${base}_Split
split_sync=${splitSync}/${base}_Split
length=($(wc -l ${splitSync}/${base}.sync))
#Split length into 15 segements (15th == length) (can extend this if to large)
cut=$((${length}/15))
cut_2=$((${cut}*2))
cut_3=$((${cut}*3))
cut_4=$((${cut}*4))
cut_5=$((${cut}*5))
cut_6=$((${cut}*6))
cut_7=$((${cut}*7))
cut_8=$((${cut}*8))
cut_9=$((${cut}*9))
cut_10=$((${cut}*10))
cut_11=$((${cut}*11))
cut_12=$((${cut}*12))
cut_13=$((${cut}*13))
cut_14=$((${cut}*14))
sed -n " 1, ${cut} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_1.sync
sed -n " $((${cut} + 1)), ${cut_2} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_2.sync
sed -n " $((${cut_2} + 1)), ${cut_3} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_3.sync
sed -n " $((${cut_3} + 1)), ${cut_4} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_4.sync
sed -n " $((${cut_4} + 1)), ${cut_5} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_5.sync
sed -n " $((${cut_5} + 1)), ${cut_6} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_6.sync
sed -n " $((${cut_6} + 1)), ${cut_7} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_7.sync
sed -n " $((${cut_7} + 1)), ${cut_8} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_8.sync
sed -n " $((${cut_8} + 1)), ${cut_9} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_9.sync
sed -n " $((${cut_9} + 1)), ${cut_10} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_10.sync
sed -n " $((${cut_10} + 1)), ${cut_11} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_11.sync
sed -n " $((${cut_11} + 1)), ${cut_12} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_12.sync
sed -n " $((${cut_12} + 1)), ${cut_13} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_13.sync
sed -n " $((${cut_13} + 1)), ${cut_14} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_14.sync
sed -n " $((${cut_14} + 1)), ${length} p" ${splitSync}/${base}.sync > ${split_sync}/${base}_15.sync
syncs=(${split_sync}/*.sync)
for file in ${syncs[@]}
do
(Chromo=$(cat ${file} | awk '{print $1; exit}')
Rscript ${Rscripts}/poolSeq_selectionCoeff.R ${file} ${Chromo} ${split_sync}) &
done
wait
rm -f ${split_sync}/*.sync
done
wait
# Did not work in the other: will do manually later
#Rscript ${Rscripts}/combinePoolseqCSV.R ${splitSync}
##------------------------------------------------##