Table 3.

Summary of bioinformatics tools for CNV detection using exome sequencing data

Tool	URL	Language	Input	Comments	Ref.
Control-FREECa	http://bioinfo-out.curie.fr/projects/freec/	C++	SAM/BAM/pileup/Eland, BED, SOAP, arachne, psi (BLAT) and Bowtie formats	Correcting copy number using matched case-control samples or GC contents	[53]
CoNIFERb	http://conifer.sf.net/	Python	BAM	Using singular value decomposition to normalize copy number and avoiding batch bias by integrating multiple samples	[54]
XHMMb	http://atgu.mgh.harvard.edu/xhmm/	C++	BAM	Uses principal component analysis to normalize copy number and HMM to detect CNVs	[55]
ExomeCNVc	http://cran.r-project.org/src/contrib/Archive/ExomeCNV/	R	BAM/pileup	Using read depth and B-allele frequencies from exome sequencing data to detect CNVs and LOHs	[49]
CONTRAc	http://contra-cnv.sourceforge.net/	Python	SAM/BAM	Comparing base-level log-ratios calculated from read depth between case and control samples	[77]
CONDEX	http://code.google.com/p/condr/	Java	Sorted BED files	Using HMM to identify CNVs	[78]
SeqGene	http://seqgene.sourceforge.net	Python, R	SAM/pileup	Calling variants, including CNVs, from exome sequencing data	[79]
PropSeqc	http://bioinformatics.nki.nl/ocs/	R, C	N/A	Using the read depth of the case sample as a linear function of that of control sample to detect CNVs	[52]
VarScan2c	http://genome.wustl.edu/software/varscan	Java	BAM/pileup	Using pairwise comparisons of the normalized read depth at each position to estimate CNV	[50]
ExoCNVTestb	http://www1.imperial.ac.uk/medicine/people/l.coin/	Java, R	BAM	Identifying and genotyping common CNVs associated with complex disease	[56]
ExomeDepthb	http://cran.r-project.org/web/packages/ExomeDepth/index.html	R	BAM	Using beta-binomial model to fit read depth of WES data	[30]

^aControl-FREEC accepts either matched case-control samples or single sample as input.

^bTools use multiple samples as input.

^cTools require matched case-control samples as input.