Skip to main content

Table 1 Summary of paired-end mapping (PEM), split read (SR), and de novo assembly (AS)-based tools for CNV detection using NGS data

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

Method URL Language Input Comments Ref.
BreakDancer Perl, C++ Alignment files Predicting insertions, deletions, inversions, inter- and intra-chromosomal translocations [17]
PEMer Perl, Python FASTA Using simulation-based error models to call SVs [18]
VariationHunter C DIVETa Detecting insertions, deletions and inversions [20]
commonLAW C++ Alignment files Aligning multiple samples simultaneously to gain accurate SVs using maximum parsimony model [21]
GASV Java BAM A geometric approach for classification and comparison of structural variants [65]
Spanner N/A N/A N/A Using PEM to detect tandem duplications [59]
AGE C++ FASTA A dynamic-programming algorithm using optimal alignments with gap excision to detect breakpoints [23]
Pindel C++ BAM /FASTQ Using a pattern growth approach to identify breakpoints of various SVs [22]
SLOPE C++ SAM/FASTQ/MAQb Locating SVs from targeted sequencing data [26]
SRiC N/A N/A BLAT output CalibratingSV calling using realistic error models [25]
Magnolya Python FASTA Calling CNV from co-assembled genomes and estimating copy number with Poisson mixture model [58]
Cortex assembler C FASTQ/FASTA Using alignment of de novo assembled genome to build de Bruijn graph to detect SVs [57]
TIGRA-SV C SV callsc + BAM Local assembly of SVs using the iterative graph routing assembly (TIGRA) algorithm N/A
  1. aThe specific input format for VariationHunter, including the reads with multiple alignments.
  2. bFile format from MAQ mapview.
  3. cThe file including the detected structure variations using other tools.