Skip to main content

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

From: Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Tool URL Language Input Comments Ref.
Control-FREECa 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 Python BAM Using singular value decomposition to normalize copy number and avoiding batch bias by integrating multiple samples [54]
XHMMb C++ BAM Uses principal component analysis to normalize copy number and HMM to detect CNVs [55]
ExomeCNVc R BAM/pileup Using read depth and B-allele frequencies from exome sequencing data to detect CNVs and LOHs [49]
CONTRAc Python SAM/BAM Comparing base-level log-ratios calculated from read depth between case and control samples [77]
CONDEX Java Sorted BED files Using HMM to identify CNVs [78]
SeqGene Python, R SAM/pileup Calling variants, including CNVs, from exome sequencing data [79]
PropSeqc 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 Java BAM/pileup Using pairwise comparisons of the normalized read depth at each position to estimate CNV [50]
ExoCNVTestb Java, R BAM Identifying and genotyping common CNVs associated with complex disease [56]
ExomeDepthb R BAM Using beta-binomial model to fit read depth of WES data [30]
  1. aControl-FREEC accepts either matched case-control samples or single sample as input.
  2. bTools use multiple samples as input.
  3. cTools require matched case-control samples as input.