modification_analysis_comparison.sh

#!/bin/bash
while getopts b:u:m:o:e:n: option
do
case "${option}"
in
b) base=${OPTARG};;       #modified base
u) unm=${OPTARG};;        #path to file with unmodified data .csv
m) mod=${OPTARG};;		  #path to file with modified data .csv
o) OUTPUT=${OPTARG};;     #output dir
e) model=${OPTARG};;      #boolean for building a svm model 
n) name=${OPTARG};;		  #string with the dataset names separated with a comma
esac
done

#filtering needed for curlcakes
for i in $unm $mod; do 
head -1 $i > ${i%.*}.filtered.csv
awk -F"," "\$1 ~ /[^/$base/][^/$base/][/$base/][^/$base/][^/$base/]/" $i >> ${i%.*}.filtered.csv
done

if [[ $model == true ]]; then

echo Building epinano model...

cut -d "," -f 5-14,20-24 ${unm%.*}.filtered.csv > test
tail -n +2 test > test2
sed 's/$/,UNM/' test2 > test3
a=$( wc -l test3 )
b=$( echo $a | awk '{print $1;}' )
t=$( echo "$b * 0.75" | bc -l | xargs printf "%.*f\n" 0 )   # 75 % for training
p=$( echo "$b * 0.25" | bc -l | xargs printf "%.*f\n" 0 )   # 25 % for predicting
echo q1,q2,q3,q4,q5,mis1,mis2,mis3,mis4,mis5,del1,del2,del3,del4,del5,sample > training
echo q1,q2,q3,q4,q5,mis1,mis2,mis3,mis4,mis5,del1,del2,del3,del4,del5,sample > predicting
head -$t test3 >> training 
tail -$p test3 >> predicting

cut -d "," -f 5-14,20-24 ${mod%.*}.filtered.csv > test
tail -n +2 test > test2
sed 's/$/,MOD/' test2 > test3
head -$t test3 >> training 
tail -$p test3 >> predicting
rm test test2 test3 

SVM.py -a -t training -p predicting -cl 1-15 -mc 16 -o $OUTPUT/svm_$base

elif [[ $model == false ]]; then

echo Skipping epinano model

else
echo No model option provided; fi




echo Plotting...

if [ -z $name ]; then
	echo Using "UNM,MOD" as dataset names
	Rscript scripts/kmer.R -u ${unm%.*}.filtered.csv -m ${mod%.*}.filtered.csv -o $OUTPUT
else
	Rscript scripts/kmer.R -u ${unm%.*}.filtered.csv -m ${mod%.*}.filtered.csv -o $OUTPUT -n $name
fi