Step-1: Run varscan2 on normal-tumor-mpileup and make copy-number call.
java -jar $varscan copynumber normal-tumor-mpileup $outputFile –mpileup 1 –p-value 0.005 –min-coverage 30 –min-map-qual 20 –min-base-qual 20 –data-ratio Calculate_From_Data

Step-2: Use CBS binary segmentation in R to find segmented region. Use mergeSegment.pl (provided by varscan) to merge them.
library(DNAcopy)
P1 = read.table (“filename.copynumber”, header=TRUE)
CNA.object <- CNA( genomdat = P1[,7], chrom = P1[,1], maploc = P1 [,2], data.type = \’logratio\’)
CNA.object.smoothed <- smooth.CNA(CNA.object)
CNA.object.smoothed.seg <- segment(CNA.object.smoothed, verbose=0, min.width=2)
seg.pvalue <- segments.p(CNA.object.smoothed.seg, ngrid=100, tol=1e-6, alpha=0.05, search.range=100, nperm=1000)
write.table (seg.pvalue, file=”cbs_P1_pvalue”, row.names=F, col.names=F, quote=F, sep=”\t”)

# This will not print the p-value.
#segmented = CNA.object.smoothed.seg$output
#write.table (segmented[,2:6], file=”cbs_P1″, row.names=F, col.names=F, quote=F, sep=”\t”)

• Step-2.1: Input the output file to mergeSegments.pl [downloaded from varscan2]. Note: mergeSegment.pl needs an additional column-2, where “chrom” needs to inserted.

cat cbs_P1_pvalue | awk \’BEGIN {OFS=”\t”} { print $1,”chrom”,$2,$3,$4,$5,$6,$7,$8,$9,$10 }\’ > formattedInputFile

perl mergeSegment.pl formattedInputFile –ref-arm-sizes $refArmSize –output-basename filteredOutput

Step-3: Now, let\’s try to find recurrent CNV among population, for which i use varscan and GISTIC.

• Step-3.1: Make marker position

cat filename.copynumber | grep -v -e chrom -e chrM -e chrX -e chrY | awk \’BEGIN {OFS=”\t”} { print “ID\'”,$1,$2 }\’ | sed \’s/chr//g\’ > markerPosition

• Step-3.2: Make segment file

$name=filteredOutput
cat $name | awk \’BEGIN {OFS=”\t”} { print “ID”,$2,$3,$4,$5,$6 }\’ | grep -v -e chrM -e chrX -e chrY | sed \’s/chr//g\’ > segmentedFile

Step-4: Run GISTIC

markersfile=markerPosition

segfile=segmentedFile

gp_gistic2_from_seg -b $basedir -seg $segfile -mk $markersfile -refgene $refgenefile -genegistic 1 -smallmem 1 -broad 1 -brlen 0.5 -conf 0.90 -armpeel 1 -savegene 1 -gcm extreme