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Table 1 A brief summary of existing tools for simulating DNA sequencing data

From: SimuSCoP: reliably simulate Illumina sequencing data based on position and context dependent profiles

Simulator Layoutb Output Language Genomic variation Tumor sample GC bias Profiles Sequencing strategyc Ref
SNV CNV Indel Impurity Aneuploidy Intra-tumor heterogeneity Position dependent Context dependent
ART SE, PE FQ, SAM C++, Perl         X   G [7]
Grinder SE, PE FQ, FA Perl   X       X   G [8]
pIRS PE FQ C++, Perl X X X     X X   G [9]
GemSIM SE, PE FQ, SAM Python X        X X G [10]
Wessima SE, PE FQ, SAM Python        X X X E [11]
NeSSM SE, PE FQ C, Perl        X X   G [12]
BEAR SE, PE FQ Perl, Python         X   G [13]
FASTQSim SE FQ Python         X   G [14]
SInC PE FQ C X X X      X   G [15]
SCNVSima SE, PE FQ Java X X X X X X   X   G [16]
NEAT SE, PE FQ Python X X X     X X   G, E [17]
IntSIM SE, PE FQ C++, Perl, R X X X X X X X X   G [18]
Pysim-sva SE, PE FQ Python X X X X X X X X   G [19]
InSilicoSeq PE FQ Python        X X   G [20]
SimuSCoP SE, PE FQ C++ X X X X X X X X X G, E  
  1. X: a given functional capability is supported by a simulator. a: these tools depend on third party NGS read simulator to generate reads. b: SE denotes single end and PE represents paired-end. c: G denotes whole-genome sequencing, and E indicates target or exome sequencing