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Table 2 Read depth (RD)-based tools for CNV detection using whole genome 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.
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 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 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
  1. aTools require matched case-control sample as input.
  2. bTools use multiple samples as input.