#pipeline for running the rearrangement analysis
#1. define phenotypes based on number of lines sharing a segment
minshared=5
phenotypesfile='../Phenotypes/trans.phenotypes.'$minshared'.txt'
mosaicfile='~/DPhil/Recombination/Results2/Mosaics/MaskedTransposons/mosaic10.highlow.infcoords.clusters.txt'
mappingdir='../MappingTranslocations/HaplotypeMode/'
pdfdir='../HaplotypeModePlots/'

Rscript ./translocation.phenotypes.R ../LikelyTranslocations/likely.translocations.txt $mosaicfile $minshared $phenotypesfile

#2. run reconstruction on these phenotypes - in haplotype mode
IFS=$'\t';
read -r -a arr < $phenotypesfile;
unset arr[0];
N=${#arr[@]};
k=$(($N/10));
l=1;
echo ${arr[1]}
for ((i=1;i<=10;i++))
do
    echo 'i='$i
    cphen=''
    for ((j=$l;j<=$(($l+$k-1));j++))
    do
	cphen=$cphen${arr[$j]}
        cphen=$cphen','
    done
    echo $cphen
    nice ~/magic-genomes/genome_scan -d ~/magic-genomes/ -a ~/magic-genomes/ -f $phenotypesfile -p $cphen -h -w $mappingdir 
    l=$(($l+$k));
done
echo 'i=11'
cphen=''
for ((j=$l;j<=$N;j++))
do
    cphen=$cphen${arr[$j]}
    cphen=$cphen','
    nice ~/magic-genomes/genome_scan -f $phenotypesfile -p $cphen -h -w $mappingdir 
done
echo $cphen 

#3. plot qtls
Rscript ./plot.translocation.qtl.R $mappingdir $pdfdir $mosaicfile $phenotypesfile

#4.investigate if they are translocations
python trans.from.qtls-new.py

#5.classify them - for the non-translocations find out if they are related to inversions or duplications - by examining strandedness and read coverage

#6.for the translocations make mapping plots - that show the distribution of reads and the structure of the rearrangement