Tool | URL | Language | Input | Comments | Ref. |
---|---|---|---|---|---|
SegSeqa | Matlab | Aligned read positions | Detecting CNV breakpoints using massively parallel sequence data | [33] | |
CNV-seqa | Perl, R | Aligned read positions | Identifying CNVs using the difference of observed copy number ratios | [31] | |
RDXplorerb | Python, Shell | BAM | Detecting CNVs through event-wise testing algorithm on normalized read depth of coverage | [28] | |
BIC-seqa | Perl, R | BAM | Using the Bayesian information criterion to detect CNVs based on uniquely mapped reads | [41] | |
CNAsega | R | BAM | Using flowcell-to-flowcell variability in cancer and control samples to reduce false positives | [44] | |
cn.MOPSb | R | BAM/read count matrices | Modelling of read depths across samples at each genomic position using mixture Poisson model | [46] | |
JointSLMb | http://nar.oxfordjournals.org/content/suppl/2011/02/16/gkr068.DC1/JointSLM_R_Package.zip | R | SAM/BAM | Population-based approach to detect common CNVs using read depth data | [45] |
ReadDepth | R | BED files | Using breakpoints to increase the resolution of CNV detection from low-coverage reads | [38] | |
rSW-seqa | http://compbio.med.harvard.edu/Supplements/BMCBioinfo10-2.html | C | Aligned read positions | Identifying CNVs by comparing matched tumor and control sample | [34] |
CNVnator | C++ | BAM | Using mean-shift approach and performing multiple-bandwidth partitioning and GC correction | [40] | |
CNVnorma | R | Aligned read positions | Identifying contamination level with normal cells | [32] | |
CMDSb | C, R | Aligned read positions | Discovering CNVs from multiple samples | [47] | |
mrCaNaVar | C | SAM | A tool to detect large segmental duplications and insertions | [35] | |
CNVeM | N/A | N/A | N/A | Predicting CNV breakpoints in base-pair resolution | [42] |
cnvHMM | C | Consensus sequence from SAMtools | Using HMM to detect CNV | N/A |