Table 3 Summary of bioinformatics tools for CNV detection using exome sequencing data

Control-FREEC^a

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]

CoNIFER^b

http://conifer.sf.net/

Python

BAM

Using singular value decomposition to normalize copy number and avoiding batch bias by integrating multiple samples

[54]

XHMM^b

http://atgu.mgh.harvard.edu/xhmm/

C++

BAM

Uses principal component analysis to normalize copy number and HMM to detect CNVs

[55]

ExomeCNV^c

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]

CONTRA^c

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]

PropSeq^c

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]

VarScan2^c

http://genome.wustl.edu/software/varscan

Java

BAM/pileup

Using pairwise comparisons of the normalized read depth at each position to estimate CNV

[50]

ExoCNVTest^b

http://www1.imperial.ac.uk/medicine/people/l.coin/

Java, R

BAM

Identifying and genotyping common CNVs associated with complex disease

[56]

ExomeDepth^b

http://cran.r-project.org/web/packages/ExomeDepth/index.html

R

BAM

Using beta-binomial model to fit read depth of WES data

[30]